Across industries, companies are investing unprecedented sums in reskilling programs to prepare employees to use emerging technologies.1 The programs are typically built around forecasts of which skills, such as data literacy, digital fluency, systems thinking, and adaptability, will matter most. Each year, when the forecasts are updated, training courses — and their related costs — proliferate.

Yet in the three years we spent studying 10 European manufacturers navigating exactly this kind of technological change, we found that the most relevant new skills workers developed were almost never the ones that had been forecast. They emerged organically, as workers and managers figured out together how to make new tools fit the existing work — or realized that they could not. In most cases, these skills were recognizable as important capabilities only to the few managers who understood where and how to look for them.

At a well-known Italian furniture manufacturer in our study, the head of the varnishing department wanted to identify and support new-skill development. Walking the floor was part of his routine, but what made him unusual was how he responded to what he saw. When workers raised concerns about equipment used on the job or devised their own ways of handling an awkward step, he carried those observations to a newly appointed head of production and negotiated changes to the workflow. He treated the production floor as the site of ongoing capability development, and his job as the connective tissue between the people doing the figuring out and the people with the authority to act on it. What he was watching grow was concrete.

At this company, the rollout of new production equipment, including computer numerical control (CNC) machinery, was steadily turning manual artisans into machine operators and digital production monitors. That meant that craftspeople’s judgment about quality and finish now had to be expressed through digital settings and on-screen interfaces. The varnishing head’s own emerging skill was a form of shop-floor diagnosis and reengineering: spotting where a new machine hindered or disrupted the work and devising a fix for it. It was a bricoleur capability that sat between hands-on craft and process engineering — one that no job description had ever named.2 The workflow changes that he negotiated were the visible trace of that skill taking shape, within both him and the workers.

Our research found that the companies managing technological transitions most successfully were not the ones with the best skills forecasts. They were the ones whose managers had developed a particular habit of attention — one that let them see what was already emerging in the work and to harness it before an employee walked out the door with an emerging skill. We call this practice SPOT. Later, we’ll explain what it is, why it matters now, and how to start doing it within your own organization.

The Forecasting Trap

Walk into a large company today and chances are you will find someone building a future-skills matrix. The rationale is simple: If we can name the skills we will need, we can train for them in advance. But this logic does not survive contact with an actual shop floor.

From 2023 to 2025, as part of the Horizon Europe Up-Skill project, our team conducted ethnographic field work in 10 companies across Europe, from a large automotive manufacturer to small artisanal workshops. Each was adopting advanced manufacturing technologies, such as collaborative robots, mixed-reality training systems, and 3D printers.3 We watched these companies discover that the skills they needed became visible only after the new technology they had introduced collided with work on the ground.

At one company in Sweden, to help workers learn lock-assembly procedures, managers introduced a mixed-reality system — a headset-based class of tools that overlay digital guidance directly onto the physical workspace, blending elements of virtual and augmented reality.4 The visual aspects of the system could show workers what to do but could not convey the reasoning behind the steps. Workers and managers eventually developed workarounds together, and the company found that the tacit understanding of the process the system was supposed to capture was the very thing it could not. At another Swedish firm, a plan to automate a grinding operation fell apart because the automated line could not replicate the judgment of experienced human workers. Only when the automation failed did the depth of the human expertise become visible.

But the more revealing part of each story is what the workers built next. At the lock-assembly company, the gaps in the system became the catalyst for developing genuinely new skills: Workers learned to program, re-sequence, and troubleshoot the system themselves, and they worked out how to teach the unwritten “why” that the headset left out (for instance, why a particular part of the lock should or should not be greased) so that the reasoning could pass from one person to the next. At the firm whose grinding line resisted automation, a new digital system for tracking production had a parallel effect: As operators worked with it, they began to read how their own task fed the wider flow of the line — a kind of systemic awareness that the job had never previously demanded.

It is worth separating two things in these cases. What the machine could not do exposed a skill the workers already had; what the workers built around its limitations was the skill that was genuinely new: the programming, the teaching of the “why,” and the new perspective on the whole production line.

A third firm, a small Italian manufacturer of high-end accordions, watched a competitor adopt robots for a sensitive manual step. It decided not to follow suit because it suspected that the competitor was automating away something the robot could not replicate.

We observed a pattern: The skills that matter most during a technological transition are the ones that surface when the new tool meets the old workflow: when something breaks, when a worker improvises a fix, when a manager notices that the thing the machine cannot do is the thing the customer is actually paying for. You cannot forecast what has not yet emerged. So the question for leaders is not “Which skills will we need next?” It is “Which skills are already trying to grow inside our company, and are we paying enough attention to notice?”

The SPOT Framework: Seeing and Growing Emerging Skills

We developed SPOT — a mnemonic for see, partner, orchestrate, transform — as a framework for capturing the habits we observed among the managers who were best at identifying, stabilizing, and retaining emerging skills. Let’s explore each of the four elements.

See the invisible. Most managers walking a production line look for problems, but an emerging skill does not look like a problem. The head of the varnishing department we mentioned earlier was not scanning for failures. He was scanning for moments when someone was solving a problem the system had not anticipated.

The skills you are trying to see are ones the worker cannot yet fully articulate. If you ask, “What new skill are you developing?” you will get a shrug. The better questions are about the task: What is this machine doing today that it was not doing last week? What are you doing differently since the new line came in? The skill is hiding inside the answers.

Take the high-end furniture manufacturer. The useful question its managers learned to ask was not “Can you run the machine?” but “How did you decide on that setting?” The answer revealed a skill that had migrated rather than disappeared. An artisan reads the grain, the density, and the absorbency of a particular piece of wood — judgments that were once expressed through the hand and eye alone — and now translates them into the digital settings that drive a CNC machine, and the on-screen checks that monitor its work. That blend of material sense and interface fluency is itself the new skill, and it lives in the doing, not in any manual that could have been written in advance.

Managers who have been in the same department for years may have difficulty seeing what is emerging without making a deliberate effort to reframe their scanning approach. (See “Four Questions for Your Next Floor Walk.”) Rotating managers into unfamiliar settings, or deliberately hiring from outside the function, may provide a fresh set of eyes that are better able to see an emerging skill. The newly appointed head of production at the furniture company was effective in part because he was able to notice what had become overly familiar to others.

Partner with workers. Seeing signs of emerging skills is only the beginning. An emerging skill lives in the head and hands of the person developing it. The manager’s job is not to diagnose it from the outside. It is to sit alongside the worker and interpret it with them.

At a small manufacturer of brass musical instruments in the United Kingdom, management and workers were actively discussing whether collaborative robots (those designed to work safely alongside humans) could handle delicate components. Rather than making a decision in isolation and rolling out the technology, the company was treating the question as something to be worked out with the people whose work the cobots would affect. What the joint evaluation surfaced, though, was a skill the company had never named. To judge whether a cobot could be trusted with delicate, one-off components, workers had to articulate exactly what they themselves were doing: reading the small irregularities of a handmade piece and adjusting their handling by feel, one piece at a time. That judgment had always been treated as simply “how the work is done.” Putting the skill into words turned it into an explicit capability — one the company could then choose to protect, teach, and build on rather than lose through inattention.

Schedule a conversation whose only purpose is to understand how the work has changed since a new tool arrived. Ask the worker to describe what is different and then ask what they would teach someone who was about to take over the job. The answer to that second question is almost always the emerging skill. Pay attention to where the worker hesitates or gestures instead of describing. Those are the places where capability is forming. At the lock-assembly firm, the answer to “What would you teach your successor?” was not the sequence of steps that the system already displayed but the reasons behind them: the unwritten logic that tells an experienced worker when the standard procedure should not be followed. That is the skill the conversation is trying to surface.

Orchestrate learning in real workflows. Once a capability has been surfaced, the tendency of most organizations is to pull the emerging skill out of its context and turn it into a training course. That rarely works. Skills that emerge in the flow of work tend to die when they are lifted out of it, because they are closely tied to the specific problem they were solving.

At a smoking-pipe maker, an employee with engineering and 3D printing expertise took on a substantial project: developing an in-house solution for producing mouthpieces. He designed a prototype machine and partnered with another company to manufacture it, resulting in a custom lathe integrated with bespoke software. He did not develop this capability in a training room. He developed it while solving a concrete production problem. The project was the curriculum, and it led to a highly specific new capability. It was not simply “3D printing” or “machining” but the ability to combine engineering judgment, hands-on additive-manufacturing experience, and software integration well enough to specify, commission, and program a custom production machine from end to end. That composite skill existed nowhere in the firm before the employee assembled it on the job, and it is now part of what the company can do.

When a worker shows signs of developing a new skill, resist the temptation to let them practice in a sandbox. The skill will develop faster if it is applied to a real production problem with actual stakes. Your job is not to remove the risk but to make the environment around it supportive enough that the worker can learn from what happens. At a specialty print firm, workers migrating a manual engraving process onto a new machine were themselves contributing their tacit knowledge to the digital systems. When tacit knowledge must be translated into something a digital system can use, the person best placed to do the translating is the person whose knowledge is being translated. And the translation is itself the emerging skill, not a preliminary to it. Learning to turn a feel for the work into instructions a machine can follow happens only on the live system, against real material and real consequences, not in a classroom where it is rehearsed in the abstract. The ability to transfer knowledge to digital systems becomes a new skill that the company has at its disposal when new digital technologies come along.

Transform insights into lasting capability. New skills identified and developed in the three steps above can live in the head of the worker and the memory of the manager for a while but will eventually vanish if not transferred to a more robust medium.

Let’s return to the accordion maker. The firm’s refusal to follow its competitor into robotic production looked, from the outside, like a conservative choice. Viewed through SPOT, it was a transformative decision. By drawing a circle around a capability it had recognized but could not yet fully specify, the firm converted a tacit and fragile skill into a strategic commitment the organization could articulate and defend. The subtle feel and sound of handcrafted components was no longer something the firm happened to have.5 It was something the firm was now explicitly protecting as a matter of policy. That stance, not any specific training program, is what locked the capability into the organization’s future — what is sometimes referred to as a company’s DNA.

A parallel example came from the furniture manufacturer’s varnishing department. The newly appointed head of production was actively encouraging employee involvement in technology adoption across the company. What began as one department head’s way of working was being supported and, in the process, normalized by a shift in managerial culture at the top. The transform move was the institutional decision to frame bottom-up innovation as how the company worked rather than as an exception or aberration. What had started as the varnishing head’s hybrid skill — reading where a machine failed to support the work and adapting to that — started as one individual’s practice before it migrated to the rest of the firm. Treating support for bottom-up innovation as established practice turned it into an embedded competence that could deliver competitive advantage into the future.

SPOT is not a new training methodology. It is a reorientation of managerial attention, away from the forecasting of skills and toward noticing the ones emerging quietly right in front of you, if you care to look, as workers solve problems and get on with their day. This managerial work is slower and less visible than delivering training and certainly less dramatic than strategic restructuring. But it is also, based on our study of manufacturers adapting to technological change, what actually works. Start with one floor walk this week, incorporating the SPOT framework, and see what your dashboard never told you.

By the ordinary measures of any new technology, the current wave of generative AI has moved fast. By some estimates, about 2.4 billion people worldwide use generative AI platforms each month, and coding agents have changed how software is written.

Efforts to commercialize the technology have scaled just as fast. AI coding platform Cursor reportedly passed a $2 billion revenue run rate by early 2026, and, as of April 2026, Perplexity was reported to have more than 100 million monthly users across its products by challenging one of the internet’s most entrenched markets: search. And this growth is not confined to AI-native companies. Salesforce’s Agentforce has reached $1.2 billion in annual recurring revenue, Harvey has spread across large law firms, and Shopify has made AI use a baseline expectation across its operations. By many conventional markers, these developments increasingly resemble the early stages of a platform ecosystem.

Yet AI’s larger promise is not to become another successful technology platform. It is to become a true general-purpose technology — like electricity or the internal combustion engine — that reshapes organizations, industries, and, ultimately, the broader economy. Judged against that standard, progress remains shallow.1 The process of complementary innovation, organizational integration, and economywide transformation expected of a general-purpose technology remains in its infancy.

The obvious culprits — immature models, ordinary adoption friction — are real, but they’re not the constraint. Like earlier general-purpose technologies, AI will not become economically transformative simply because it is broadly applicable. It will realize that potential when a surrounding technological, industrial, and institutional architecture enables decentralized organizations to confidently build upon it — in other words, when the technology becomes platformed.

Here, I will explain what platforming entails (the technological, industrial, and institutional architectures a technology needs), why AI remains only partly platformed, and how organizations can innovate and invest effectively while that process is still unfolding.

Platforming a General-Purpose Technology

Scholars have long argued that general-purpose technologies are able to transform economies because they can be applied across many industries while stimulating successive waves of complementary innovation — as was the case with electricity, the steam engine, and the internet.2 By the same token, the potential of such technologies is unusually hard to realize. Broad transformation requires large numbers of independent organizations to make interdependent investments; redesign products, processes, and business models; develop new capabilities; and coordinate despite deep uncertainty about how the technology and its ecosystem will evolve. Therefore, the central challenge is creating the technological, industrial, and institutional conditions under which decentralized organizations can confidently build upon the technology — a process of platforming. Someone has to build that foundation: It is what makes decentralized downstream integration, complementary innovation, and co-invention possible at all.

Electrification illustrates this. Electricity was technologically proven and commercially viable by 1882, yet widespread electrification did not follow for nearly four decades. Technological architecture stabilized when the Niagara Falls hydroelectric power project (1895-1896) confirmed polyphase alternating current at commercial scale. Industrial architecture matured as a division of labor settled among utilities, equipment makers, and financiers, under the regulated utility model that took hold between 1898 and 1907. Institutional architecture followed, with the first comprehensive state public-utility commissions forming in 1907.

As these technological, industrial, and institutional architectures progressively aligned, organizations gained sufficient confidence to invest and experiment, and electrification accelerated. Platforming emerged through the combined efforts of inventors, manufacturers, utilities, financiers, standards bodies, and regulators. Other general-purpose technologies — notably, personal computing — illustrate alternative pathways: Platform leaders, such as Microsoft and Intel, more directly orchestrated these architectures to support ecosystem growth.3

A technology becomes platformed when a surrounding technological, industrial, and institutional architecture creates conditions stable enough for decentralized organizations to confidently build upon it. Platforming reduces uncertainty by stabilizing expectations about how the technology can be used and how it will evolve. It establishes clear lanes for complementary innovation — where to innovate, where to rely on others, and what can be treated as stable — and the governance, rules, and incentives that enable organizations to capture value from their investments while coordinating with others. Decentralized investment and experimentation can then scale from isolated successes into broad transformation through the alignment of the three architectures.

The Platforming of AI: Where Are We Now?

The platforming of AI remains incomplete, but recognizable technological, industrial, and institutional architectures are emerging. Understanding what has stabilized — and what has not — clarifies the opportunities and the frustrations of building on AI before it has been fully platformed. Let’s take a look at the current state of AI.

AI’s technological architecture is still emerging. Today’s dominant AI architecture rests on a relatively specific trajectory, especially among leading frontier developers: pretrained, predominantly language-based foundation models; specialized hardware; cloud-based training and inference; and API-mediated delivery.4 AI is taking shape as a layered stack — chips, cloud infrastructure, foundation models, and the applications built on them. (See “Key Elements of the AI Stack.”) The stack’s lower three layers are converging on a centralized foundation in which model development and most computation reside with a few cloud-hosted frontier models, with most organizations consuming intelligence remotely through APIs rather than owning it. This departs from the digital services economics that were once taken for granted: Rather than distributing software that runs locally at little additional cost, AI delivers intelligence through continual, cloud-hosted inference, performing heavy computation each time intelligence is used.5 Although the prevailing architecture continues to evolve, the likely alternatives — open-weight ecosystems and parallel stacks developed by Chinese companies — are variations on it rather than fundamentally different trajectories. It’s likely that to the extent it continues, much of the uncertainty around the lower layers will subside.

The application and deployment layer, where most organizations hope to build complementary products and services, remains fluid. As emerging orchestration, agent, and middleware layers compete to define how AI should be integrated into products, workflows, and enterprise systems, some companies build around chatbot interfaces and others directly on foundation model APIs. Meanwhile, frontier models keep absorbing capabilities that many people expected to reside elsewhere: Enterprise search, retrieval from knowledge bases, and persistent memory are increasingly being handled by the model rather than by separate software. It remains unclear which abstractions, interfaces, and patterns will become stable enough to support broad complementary innovation.

AI’s industrial architecture is still cohering. Technological architecture determines how intelligence is built — the trajectory and approach, and the division of the larger problem into components — whereas industrial architecture determines who builds what: how the ecosystem divides problem-solving and commercial activity across specialized organizations, and how value creation and capture are distributed among them.6 Whether that division of labor can be occupied at all is another matter — one that is dependent on the capabilities organizations build, the skills the labor market supplies, how organizations align with one another and with the wider economy, and the returns that sustain them. Where these misalign, complementary innovation falls far short.

A recognizable division of labor has begun to emerge around the lower layers of the stack: Nvidia in AI accelerators; Amazon Web Services, Microsoft, and Google Cloud in compute; OpenAI, Anthropic, Google DeepMind, and xAI in frontier models; and Meta (Llama), Mistral, DeepSeek, and Alibaba (Qwen) in the open-weight ecosystem. Much of the ecosystem’s measurable investment is concentrated in these foundational layers — chips, compute, power, data centers, cloud, and frontier models.

Some businesses have clearly emerged as complementors on top of the foundation models. But AI has produced nothing like the governed marketplaces and stable interfaces earlier developers could build on — the app stores of iOS and Android, or the backward-compatible APIs of Windows — with the clear categories and rules that once gave thousands of them the certainty to commit. OpenAI’s GPT Store has stayed thin; meanwhile, a different candidate layer is forming one level up, around AI app builders such as Replit and Lovable, on which nondevelopers can generate and ship software. Whether a durable application economy consolidates there, among the model providers, or within incumbent suites is the contest still unresolved.

For now, AI reaches users through a heterogeneous mixture of forms: the frontier providers’ own applications, applications built on frontier models, agents, vertical applications, AI embedded within incumbent software, and proprietary enterprise deployments. Even what counts as the application and deployment layer is unsettled: thin chatbots, AI features embedded in existing tools, orchestration and middleware, wrapper apps, or third-party agents.

Vertical integration further blurs these boundaries as frontier developers move upward into user-facing products while incumbents embed foundation models throughout their suites. Rather than competing within established categories, companies are competing to define them — advancing not merely different products but competing hypotheses about how foundation model capabilities should be organized, accessed, and converted into value.

Meanwhile, returns on complementary investment remain uncertain, most fundamentally with regard to the industry’s eventual division of labor, dominant application architecture, and sources of durable advantage. AI also runs into a familiar conundrum: The same foundation models that reduce the cost of innovation also reduce the cost of imitation, making differentiation harder.7

Investments in the lower layers also face uncertainty, but for different reasons. Frontier-model developers are pouring vast sums into future scale, ecosystem leadership, and pricing power — expectations that depend on an industrial structure that has not yet emerged. Switching costs remain modest, customers frequently use multiple models, and the mechanisms that historically produced durable platform leadership have yet to develop. Meanwhile, training and inference costs weigh on profitability, and capable open-weight models keep pressuring proprietary ones. Ultimately, these investments are a bet that AI will become platformed enough to generate value sufficient to justify today’s spending.

AI’s institutional architecture is emerging slowly. Institutional architecture governs how decentralized organizations coordinate, invest, and build on a common foundation. Successful platform ecosystems require more than technology and market forces; they require institutions and governance that let organizations invest independently while remaining collectively coordinated. AI’s institutional architecture is emerging through standards bodies, consortia, technology providers, and governments developing protocols, but it remains far less developed than the technological and industrial architectures.

Institutional coordination can arise through several mechanisms. Governments can establish legal frameworks and public standards; industry alliances, standards bodies, and multiparty initiatives can coordinate interoperability and shared conventions. In AI, we might particularly expect the emergence of platform leadership, in which a central company organizes the ecosystem by establishing stable interfaces, governing participation, signaling architectural direction, committing to what it will not absorb, and creating credible incentives for complementary innovation. Such leadership is itself an investment: It takes a company with enough platform power to set and enforce the terms and enough incentives to bear the cost. Microsoft and Intel exemplify that in personal computing, Apple in the iPhone, and Google in Android.8 Today we can see that while many governments have taken a light-touch approach, industry consortia, evaluation frameworks, and private protocols are emerging.

A shared protocol, such as Anthropic’s Model Context Protocol, is a useful standard, but platform leadership runs far deeper. It is the active coordination and orchestration of an ecosystem that extends well beyond the platform owner’s own boundaries — governing participation, aligning incentives, signaling architectural direction, committing to what it will not absorb, and giving large numbers of independent companies the confidence to build. In that deeper work, today’s frontier companies are investing comparatively little. The ecosystem lacks not leadership in technology but leadership in coordination.9 Someone has always supplied that coordination — such as a lead company in personal computing, and public authorities and engineering bodies in electricity. In AI, organizations are integrating vertically instead, which is not the same thing.

In the absence of mature platform leadership, many leading companies are instead solving coordination problems through vertical integration, combining frontier models, cloud infrastructure, developer tools, enterprise software, consumer applications, and distribution within integrated ecosystems. But direct control by one company is not ecosystem governance: It forgoes the diversity and decentralized effort of large numbers of independent complementors — the engine of broad transformation — and so may postpone the mature platform ecosystem that it appears to be substituting for.

Building and Innovating Before AI Is Fully Platformed

Most companies will not compete by building frontier models. They will compete by building on them — integrating them into products and operations, and creating the complementary goods and services around them. That places them, awkwardly, where the architecture is least settled: Although the lower layers are converging, the application and integration layer, where most of this building happens, is the part still in flux.

It can seem natural to wait for the costs, risks, and uncertainty of building on AI to fall as the technology becomes platformed. But the real challenge is to keep them in mind and act anyway — for a company to invest in the ways that best strengthen its position while managing the problems of building on a general-purpose technology that has not yet been fully platformed. That means making investments that will pay off however the architecture settles.

Learn faster than you commit. When the architecture is unsettled, what a company learns is worth more than what it locks in — and the cost of learning is unusually low right now. Much of the experimentation can run on open-weight models — such as Llama, Mistral, or DeepSeek — on a company’s own hardware, where the marginal cost of a query is near zero and proprietary data never leaves the building. Frontier models can be reserved for the work that genuinely needs them. The most valuable thing a company can record — where its experts overrode the model and why — is also at its richest now, while the models are still making enough mistakes to generate corrections. Those corrections also map the jagged frontier of what AI does reliably on a company’s own tasks. But that margin is closing: Once a model reliably beats a company’s experts on a task, the corrections, and the signals they provide, disappear.

Build assets that will survive architectural change. No one yet knows which model, stack, or way of organizing intelligence will win, so businesses should be wary of making bets specific to any one of them. The danger is subtler than it looks. Most companies ask only the technical question “If the model were swapped tomorrow, would the system still run?” and stop there. But even a perfectly swappable model can be locked in commercially, through the way a vendor bills and what its contract permits. Salesforce’s Agentforce meters agent work in “agentic work units” it alone defines, and SAP’s 2026 policy restricts what outside AI is allowed to do with the data inside its software.10 A company should keep its options open at the applications level, too. Holding back on bets wired to an architecture that is still unsettled is a strategy in itself — not a failure to act.

Invest in complements, not intelligence. Nearly every company will buy rather than build its AI, as will its competitors — who will often purchase the very same models. Intelligence that everyone can rent cannot be anyone’s advantage: The same models lift the floor for a business and its rivals alike, and the work they do well converges toward a common mean.11 Advantage has to come from what the shared model cannot reach, such as proprietary data, domain expertise, trusted customer relationships, distribution, brands, and specialized workflows. This is not a new idea: Rents accrue to the holders of co-specialized complements, not to the freely available input itself.12

Where to invest turns on the seam — the interface between layers where value can be captured. A defensible seam rests on something that the layers above cannot easily reproduce: a regulator’s standing trust, an audit trail, an embedded billing relationship, a proprietary data corpus. This is why many hospitals’ clinical AI runs through Epic: A smarter interface still has to clear the trust bar, which Epic has already done. An illusory seam is a thin wrapper around someone else’s model — useful this quarter but enveloped the next, when the provider folds the same capability into its own product at no extra charge. Before investing in a seam, an organization should ask who will try to take it — the model provider reaching up or the platform incumbent reaching down — and whether what anchors it can be reproduced.

The logic holds even in the case that seems to overturn it: a model capable enough to do the integration, the judgment, and the work itself, leaving little apparent need for a wider ecosystem. Even then, the assets that endure are the ones a model cannot internalize, such as the regulator’s trust, the audit trail, the embedded contract, and the proprietary data. Betting on intelligence pays off only for whoever wins the frontier race; betting on complements pays off regardless of who wins.

The question, then, is not how to own the AI. It is how to own the assets that become more valuable as AI becomes abundant.

Build organizational capability. The most valuable use of AI in this period is the least obvious one: not to do today’s work faster but to become an organization that understands its own workings — where its knowledge sits, how its decisions get made, where its bottlenecks are, and how work moves across its teams. These are the assets the earlier moves depend on. Vendors already have a name for this work — the “forward-deployed engineer” they send to sit inside a customer — and adopters will need the role in-house. The capability a company builds now, such as the memory, the routines, and the judgment about where AI helps and where it does not, is itself among the complements a shared model cannot reach, and it is what keeps producing new ones as the architecture shifts. Learning feeds capability; capability yields the durable complements.

The deep gains from a general-purpose technology always arrive late, and they require two things: The surrounding architecture must settle, and organizations must rebuild themselves around the technology. A company cannot hurry the former. It can begin the latter now, and history suggests that those that do are the ones that pull ahead. Manufacturers that learned to redesign factories around electric power saw gains long before electrification was universal; companies that learned to reorganize around information technology saw benefits long before personal computing matured. The companies that come out ahead in AI may look less like today’s adopters and more like knowledge factories — organizations whose advantage is not throughput but the rate at which they turn their own operations into validated understanding.

Building before AI is platformed is necessarily about investing in what the technology cannot supply — learning, complements, and organizational capability that compound while the architecture is still in motion. It is not about adopting the most tools or predicting the final form first.

After receiving some challenging feedback about my management style, I’ve done a lot of introspection, taken responsibility, worked with a coach, and I believe I’ve made real changes in how I work with others. But I don’t sense that others’ perceptions have changed accordingly. What else do I need to do?

It can be frustrating to do the hard work of growth but then still feel defined by who you used to be. You’ve changed how you engage and react, but your team still seems to see you as the same leader. So, what does it take to be recognized as having changed?

In a situation like this, a few things could be at play. What may feel like a major change to you may be perceived as a minor adjustment by others or go unnoticed. Some people may be reserving judgment, especially if they’ve had difficult interactions with you in the past and want to see sustained evidence of change before they buy it as genuine.

Although we may feel transformed by growth, and that we are beginning a new story, people may interpret our new behaviors through old narratives. For instance, someone who was inconsiderate of others by being consistently late may have tackled this issue and is now regularly on time. However, any slip — just one instance of tardiness — will stand out, reinforcing the original perception.

The persistence of your old image is not a failure of effort but rather a lack of adequately signaling your efforts. It might be difficult to share how you are trying to effect positive change if you feel shame about old behaviors. But one of the most effective ways to signal change is to talk about it openly.

When you are working on a specific behavioral goal, don’t just keep it to yourself. Share it with your team! Let them know what you are trying to improve, and then create a rhythm of accountability. Regularly ask, “How am I doing? What could I do better?” Ideally, ask not in the abstract but with specifics. For example, say, “I’m trying to run meetings more inclusively. At our team check-in yesterday, do you think that everyone who wanted to contribute was able to?”

This technique, which I learned from my mentor Marshall Goldsmith, does two things. It reinforces that you are a leader committed to continuous growth and trains others to notice your changes. When people are explicitly invited into the process, they become more attuned to your progress rather than defaulting to old assumptions.

Equally important is how you reinforce that progress in everyday conversations — without boasting or seeming to fish for affirmation. Simple comments like “I’m trying something new with the weekly meeting agenda so that everyone has a chance to weigh in on what’s working and what’s not” could subtly shift perception. These small signals accumulate over time and help others update their mental models of you.

People can only respond to what they can see. Make your internal evolution visible externally. Signaling change is not about self-promotion. It’s about making your actions visible so that others can clearly see the growth that has already taken place. And those changes in perception will amp up your future-forward leadership.

Leaders may prioritize executing strategy and driving organizational performance, but they are also responsible for developing the next generation of leaders. Traditionally, leadership development has focused heavily on performance — an understandable emphasis, given that leaders themselves are evaluated and rewarded based on execution and results.

However, as organizational strategies increasingly rely on cross-functional teams and external partnerships, employees’ success increasingly depends not only on technical capabilities and performance but on the ability to navigate relationships, influence stakeholders, access information across boundaries, and build credibility beyond their immediate roles. Yet many organizations still develop high-potential talent primarily through experiences that advance performance — that is, the ability to execute tasks. They overlook the importance of providing development experiences that help individuals develop a critical asset for leadership effectiveness and advancement: social capital.

In organizational settings, social capital reflects the trust, goodwill, and credibility that shape how individuals are perceived, influence how contributions are interpreted, and enable the support and advocacy required for advancement. It determines whether performance stays where it is produced or travels across teams, functions, and decision makers, translating into broader visibility, advocacy, and opportunity.

Yet, despite social capital’s influence, few professionals are explicitly taught how to build it, convert it into opportunity, or use it to sustain their career mobility. Rarely explicitly discussed and seldom incorporated into development, it often operates as an invisible system governing how individuals advance within organizations.

Social Capital in Practice

Social capital determines whether performance stays where it is produced or travels to where opportunity exists. To better understand professionals’ perspectives on how it shapes career mobility, I interviewed 21 people representing a range of functions and career stages, from just a few years of experience to more than four decades in the workforce.

While we might expect networking activity to be at the heart of developing social capital, participants in my research rarely discussed it solely in those terms. Instead, they saw it operating in the practical realities of organizational life: who is trusted, who is sought out for advice, whose judgment carries weight, who receives advocacy, and who is afforded grace when mistakes occur. As one research participant put it, “Social capital is the amount of blind faith that someone will give you based on how they feel about you and their prior experience with you.” While formal organizational structures define reporting relationships and responsibilities, participants consistently emphasized that many opportunities, decisions, and career movements are enabled by less visible relational dynamics.

Several interviewees described social capital as influencing how performance itself is interpreted. Strong performance may establish credibility, but credibility alone does not guarantee visibility, advocacy, or advancement. Rather, social capital shapes whether accomplishments remain confined to an immediate team or become recognized across functions, business units, and decision-making circles.

This dynamic becomes particularly important when individuals pursue movement across organizational boundaries. These shifts across teams, functions, or business units are what the study refers to as zigzag moves, in contrast with progress up a traditional, defined hierarchy. Unlike advancing within a familiar environment, making zigzag moves often requires individuals to establish credibility with stakeholders who have little direct knowledge of their previous work. In these situations, social capital — reputation, relationships, and advocacy — provide signals that reduce uncertainty and increase trust. In this way, social capital serves as a mechanism through which performance becomes portable.

As high-potential individuals ascend the ranks of leadership, their social capital becomes increasingly important to how they are evaluated. Their work is unlikely to be directly observed by those considering candidates for advancement, and decisions about opportunities (especially emerging opportunities), promotions, and leadership potential are increasingly informed by reputation, trust, relationships, and the perspectives of others. In these situations, social capital often determines whose capabilities are recognized, whose ideas gain traction, and whose name surfaces when opportunities emerge.

Interviewees suggested that social capital is commonly accumulated through everyday work interactions: moments where credibility is reinforced, trust is established, and relationships are strengthened. It is accumulated in meetings through thoughtful contributions, collaborative problem-solving, and consistent follow-through — small but meaningful social capital deposits that compound over time.

Social capital is also seen as fragile, reflecting the old adage that reputation can take a lifetime to build but be lost in a minute. Trust, credibility, and goodwill can be diminished quickly through poor judgment, broken commitments, lack of reciprocity, or misaligned relationships.

To that point, social events such as company “happy hours” that might be seen as prime opportunities to build one’s network were viewed with caution by participants. One noted, “Social events are always weird. There’s always weird power dynamics. There’s already cliquishness. That social capital has already been established. It’s easy to lose social capital at a social event.”

For Black professionals, these dynamics carry additional significance. Across interviews, participants observed that social capital comes with different risks, scrutiny, and rewards for Black employees. While all professionals are expected to build trust and credibility, participants noted that the same behaviors are not always interpreted or rewarded equally. Black professionals were seen operating with a smaller margin for error, where trust often has to be earned repeatedly and is more easily challenged.

How Social Capital Is Built

Based on the experiences people shared in the interviews, I identified four underlying mechanisms through which social capital is built and converted into mobility: performance, reputation, influence, and network.

Performance reflects an individual’s ability to execute, deliver results, and establish credibility through consistent output. This is the foundation of reputation: one’s subject-matter expertise, ability to solve problems and handle complexity, and reliability. As noted earlier, performance is not sufficient to enable career mobility, but it is necessary for credibility.

Reputation captures how an individual is perceived across the organization based on their behavior, judgment, and consistency over time. It is shaped not only by outcomes but by how individuals engage others across levels, upward, laterally, and downward.

Influence reflects the ability to shape decisions, align stakeholders, and mobilize action without relying solely on formal authority. As roles expand, success increasingly depends on the ability to cooperate with others rather than through direct execution.

Network represents the relational infrastructure through which information, opportunity, and advocacy flow. More than proximity or access, it reflects the strength, relevance, and credibility of relationships, particularly those that extend beyond immediate teams or functions. It also serves as a reputational signal that is reflected in who associates with an individual and whether those relationships amplify credibility and opportunity.

Together, these four components form the architecture through which social capital is built. While each contributes independently to professional effectiveness, participants saw their value as emerging in concert. Performance establishes credibility, credibility develops into reputation, and reputation enables influence and networks. Together, influence and network amplify reputation beyond an individual’s immediate environment, creating the conditions for career mobility.

As individuals advance into management and leadership roles, performance becomes assumed rather than continuously evaluated. Mobility increasingly relies on the interactions between reputation, influence, and network. Each advancement strengthens reputation, expands network reach, and increases influence, which in turn creates additional opportunities for mobility.

Why Building Social Capital Can Be Difficult

If social capital governs mobility, then understanding why some otherwise capable professionals struggle to build it is important in identifying how to help high-potential individuals prepare for leadership roles. The following patterns emerged in my research.

1. The productivity trap. High-performing individuals whose social capital is built almost exclusively through performance may be operationally indispensable but strategically invisible. These professionals consistently deliver results, solve problems, and become highly valued within their immediate teams. Yet their reputation, influence, and network remain largely confined to the environment in which their work is performed, especially if they work on projects that don’t create touch points outside the immediate team.

As a result, they become indispensable to current operations but increasingly invisible to those selecting potential leaders for growth opportunities. Their contributions are recognized locally but rarely translated into visibility, advocacy, or mobility elsewhere in the organization. Over time, performance ceases to generate mobility and instead becomes the mechanism through which they remain anchored to their existing role.

2. Reputation without advocacy. Some individuals develop strong credibility and positive reputations but lack the relationships or influence necessary to convert that reputation into opportunity. These professionals are respected, viewed favorably by colleagues, and often considered capable of larger responsibilities. However, when advancement opportunities emerge, they lack relationships with people who could actively advocate for them.

In these situations, reputation becomes passive capital. Trust exists, but it isn’t mobilized. As a result, advancement frequently lags behind capability.

3. Network without credibility. The third pattern reflects the opposite imbalance: Some individuals successfully develop broad networks and visibility but lack the credibility necessary to take advantage of the opportunities those relationships create. Access outpaces demonstrated capability, and the individual’s visibility exceeds their readiness to perform at the required level.

While networks can create opportunity, long-term mobility still depends on trust and performance. Without credibility, relationships alone rarely generate sustained advancement and may even create reputational risk, when expectations exceed demonstrated capability. One participant observed that an employee’s network could be seen as a distraction if their “ducks aren’t in a row.”

These patterns suggest that career mobility is rarely constrained by a complete absence of social capital. Understanding these obstacles can help point to where development efforts should be focused and what may be preventing otherwise capable professionals from moving forward.

How to Develop for Social Capital, Not Just Performance

Excellent performance is typically what makes potential leaders stand out, but often, development efforts remain focused on building expert skills, leaving the broader mechanisms of mobility largely unaddressed. Social capital should be developed as intentionally as any other professional capability. Managers and sponsors are uniquely positioned to do this, but it requires them to offer different kinds of support than they may be accustomed to providing.

What Managers Can Do: Develop More Than Performance

Managers often focus development conversations on execution, productivity, and technical capability. While these factors remain essential, performance alone rarely prepares individuals for the complexity of future leadership roles. Managers should therefore view development through a broader lens, helping employees build the components of social capital while creating opportunities to accumulate trust, credibility, and relationships that support mobility.

They can begin by creating opportunities for employees to establish credibility beyond their immediate responsibilities. Cross-functional projects, enterprise initiatives, stakeholder-facing work, and opportunities to present ideas to broader audiences all help expand visibility and reputation. Managers can also help employees strengthen their influence by involving them in decision-making discussions, encouraging them to lead through collaboration, and providing them with opportunities to navigate competing stakeholder interests.

Equally important, managers should recognize when high performers are becoming trapped within the boundaries of their current role. Development should focus on helping performance travel beyond the environment in which it originated.

Perhaps most important, however, is helping employees recognize that these experiences create opportunities to accumulate social capital through repeated deposits. Completing a stretch assignment may create initial visibility, but sustained mobility often depends on what happens afterward. Maintaining relationships with stakeholders, staying connected with peers across functions, and continuing to engage with leaders from temporary assignments help reinforce trust, credibility, and familiarity over time. These deposits keep an individual’s name present in conversations long after a project has concluded and increase the likelihood that opportunities will emerge from relationships built through prior experiences rather than current performance alone.

Where Sponsors Can Help: Extend and Educate

Sponsors occupy a unique position because they can extend their own social capital on behalf of another individual. Like managers, sponsors can help individuals build social capital by creating opportunities for visibility, exposure, and relationship development. However, sponsors contribute something additional: Through advocacy, introductions, endorsements, and visibility, they lend their own social capital to create opportunities that may not have been accessible through performance alone, in a dynamic described by scholar Ronald S. Burt in his writing on social capital. However, with any loan, there is an expectation of repayment: The sponsor expects to benefit in some way that will enhance their reputation and influence. This reciprocity is key to a successful relationship between a sponsor and the individual they’re sponsoring.

But effective sponsorship extends beyond opening doors. Sponsors routinely make decisions based on trust, reciprocity, credibility, judgment, relationships, and reputation because they understand how mobility operates within organizations. However, these dynamics are often left implicit. Individuals may benefit from sponsorship without fully understanding why opportunities emerged, what reputational considerations were involved, or how advocacy was earned.

Effective sponsors make these mechanisms visible. They help individuals understand how social capital is accumulated through consistent deposits and built through the four components I outlined, and how it ultimately translates into opportunity. In doing so, sponsors move beyond providing access and begin teaching others how to build, accumulate, and benefit from social capital throughout their careers.

Performance remains essential for career success, but it is rarely sufficient on its own. As careers progress, advancement increasingly depends on whether performance is recognized, trusted, advocated for, and carried beyond the environment in which it is produced. Social capital provides the mechanism through which that occurs.

For professionals, the implication is clear: Social capital must be built, accumulated, and intentionally developed alongside performance. For managers and sponsors, the responsibility extends further. Developing talent requires more than improving execution; it requires helping individuals cultivate the trust, credibility, influence, and relationships that enable mobility. Ultimately, social capital determines whether performance stays where it originated or travels to where opportunity exists.

Algorithmic tools promise to democratize access to knowledge and thus spark creativity and innovation, but research we conducted revealed a hidden risk: Those tools may be silently narrowing organizations’ creative potential by suppressing the value of expertise. The fault lies not with the experts but with the hidden architecture of the tools they use. The invisible design choices embedded in algorithmic tools fundamentally shape creative output.

Standard algorithms behind search, discovery, recommendations, or large language models (LLMs) are designed around exploitation logic: They prioritize popular, relevant results, which reinforces what users already know instead of challenging them to explore. When such tools are designed for efficiency rather than exploration, they channel users toward conventional information, creating what we call ideation bubbles: clusters of similar ideas that represent a dangerous homogeneity of thought.

But our findings also revealed a solution: When we modified exploitation-based algorithms to surface diverse, uncommon information, experts who used them generated solutions that were significantly more creative, and they were able to break free from the convergent thinking patterns that can trap entire organizations.

Most digital tools we use today are designed to prioritize efficient access to popular answers. They also draw on a user’s existing knowledge frameworks (such as their search or chat histories) when presenting information and rarely challenge them to explore new territory. While this approach excels at delivering quick, useful results, our research found that this kind of bias is detrimental to creativity and innovation.

At the individual level, creators gravitate toward familiar solutions. The problem compounds at the organizational level. When multiple people use the same exploitation-based tools to brainstorm solutions to the same challenge, they are independently channeled toward the same information, and they independently generate similar ideas. As is the case with news bubbles, ideation bubbles are imperceptible to those inside them: Individuals believe that they are generating diverse ideas because they are working independently, but the shared algorithmic infrastructure steers everyone toward the same solution space. This convergence risk is especially dangerous for strategic challenges requiring breakthrough thinking.

Surfacing a Wider Spectrum of Ideas

To test our thinking about exploration versus exploitation, we designed an algorithmic modification we called XYZ that uses natural language processing. Built on top of Google Search, it surfaced results from semantically distinct clusters of ideas rather than the most popular or relevant matches — prioritizing exploration over exploitation. We then conducted two complementary studies: a controlled laboratory experiment with 104 participants, who were asked to generate creative ideas for reducing resource overconsumption; and a global field experiment, in which 245 participants, ranging from sustainability novices to seasoned sustainability experts, participated in an ideation challenge to reduce food waste in households. In both cases, we compared their creative output when using either Google Search or XYZ. The ideas were evaluated by independent expert judges blind to the experimental conditions.

In the laboratory study, ideas developed with XYZ were rated 14% more creative than those developed with standard Google Search, evidence that exploration can lift creativity even without deep domain knowledge. The more striking finding came in the field study: When using standard Google Search, domain experts showed no statistically significant advantage over novices at generating creative solutions. When using exploration-based algorithms, experts significantly outperformed novices, and ideas were rated 11% more creative on average. The algorithmic design, operating imperceptibly beneath the surface, shaped whether experts could make their strongest contributions.

What explains the gap between experts and novices? The key lies in what we call recombinant innovation: synthesizing diverse information elements into novel combinations. Exploitation-based algorithms surface familiar information that echoes existing mental models, but exploration-based algorithms expose users to insights from fields that may be unfamiliar to them, such as, in our study, behavioral economics or supply chain optimization. Experts have the knowledge foundation to harness this diversity effectively. A novice encountering the same diverse information lacks that scaffolding.

Experts can recognize which unfamiliar ideas are relevant, which are dead ends, and how to integrate them into a workable solution. For instance, one expert in our study combined insights about community food-sharing platforms with smart-home technology and behavior change techniques to propose a neighborhood-based “food rescue network” with automated inventory matching. That recombination required a deep understanding of food waste patterns.

Helping Domain Experts Break Out of Ideation Bubbles

While the effects of exploration-based algorithms are profound for individuals, the organizational implications may be even more significant. Using natural language processing to semantically cluster all ideas from our field study, we found two distinct effects and a critical interaction between them. First, exploration-based algorithms increased idea diversity for all participants. Novices using Google Search (exploitation) produced ideas that fell into just one semantic cluster; novices using XYZ (exploration) produced ideas spanning two clusters.

Second, expertise alone had a similarly modest effect: Experts using Google Search generated ideas across two clusters compared with novices’ one. But the interaction between expertise and exploration-based algorithms was dramatic: Experts who used XYZ generated ideas across five distinct clusters, compared with one or two from every other group. These experts did not just contribute more ideas within existing solution spaces; they generated entirely new ones, breaking dominant ideation bubbles and creating unconventional clusters of thinking.

These findings have immediate implications for how organizations structure their innovation processes.

First, they should treat algorithm type as a design input, not a default. Most tools that organizations use are optimized for efficiency, not exploration, which can suppress the value of what experts can contribute. When tackling strategic challenges that require breakthrough ideas, companies should consider approaches that surface diverse and uncommon information instead of the most popular or obviously relevant results.

Second, organizations should match the algorithm to the task. Exploitation-based algorithms remain valuable for accuracy and efficiency — identifying best practices or answering well-defined questions. Exploration-based approaches are best reserved for early-stage ideation, when divergent thinking is most valuable. This applies directly to how organizations use LLMs: Prompting for exploration rather than exploitation — for instance, asking for approaches that draw from unrelated industries or explicitly challenging dominant assumptions — can dramatically affect creative output. When reviewing AI-generated material, experts should particularly attend to unexpected or unfamiliar elements and avoid gravitating toward expected, recognizable patterns. Novel results are where the raw material for recombinant innovation often lies.

Third, organizations should audit their idea portfolios for ideation bubbles. If a team’s ideas cluster around a narrow set of solutions, the problem may be the tools. Semantic clustering can help identify bubble formation; deploying domain experts with exploration-based tools can be the best way to break them.

Fourth, they should invest in expertise. Our findings show that domain knowledge remains essential for innovation, particularly when paired with the right tools. The organizations best positioned to benefit from AI in innovation are those that develop expertise and configure their algorithmic tools to unlock it.

Looking Forward: The Expert Advantage

Proponents of AI argue that it reduces the cost of accessing expertise, but our research suggests that AI also does something more interesting: It transforms how expertise creates value. When algorithms democratize access to information, the premium shifts to those who can synthesize, recombine, and innovate with that information. Our research suggests that, far from being diminished by AI, expertise is transformed by it.

True breakthroughs depend on people having the ability to make unexpected connections — a skill at which domain experts excel, provided that they have the right tools. The question for leaders is not whether experts are needed but whether their organization’s tools are designed to let experts do what only they can do.

Many companies around the world have made significant progress in adding a higher proportion of board directors from traditionally underrepresented groups, such as women and people of color. But despite the increased diversity, many boards lag on inclusivity — that is, ensuring that diverse voices are actually heard and that all board members are able to contribute to decision-making.

In our interviews with more than 25 board and committee chairs and 20 nonchair board members, a contradiction emerged: Chairs overwhelmingly believed that they led inclusive boards, but many board members disagreed.

This pattern mirrors findings from a recent Egon Zehnder global survey of board directors.1 While most chairs reported that they create inclusive spaces, only half of board members said their boards were actually inclusive. Men were significantly more likely than women to agree that they “can bring their full selves to the boardroom.”

This disconnect between what chairs and directors perceive matters. A board can be diverse — which means that people with a variety of different perspectives, backgrounds, and demographic profiles are seated around the table — without being inclusive. Without explicit inclusive leadership from the chair, a board that is diverse can actually see its effectiveness reduced rather than enhanced. Research on groups and teams shows that when a wider range of perspectives enters a discussion without being accompanied by enabling conditions, communication breaks down, factions form, and decision-making slows.2

Boards today face complex strategic challenges, including geopolitical uncertainty, climate risk, disruptive technologies, stakeholder activism, and rapid shifts in consumer value systems. They need the cognitive diversity brought by different lived experiences, disciplines, and backgrounds. But to benefit from that diversity, chairs must create environments where all directors feel able to speak up — even when their perspectives challenge prevailing views.

Defining the Inclusive Board

In our interviews, we asked the question “What does an inclusive board mean and look like to you?” The answers we received can be distilled into the following definition: An inclusive board is one where every board member feels respected, valued, and empowered to contribute, regardless of background, identity, or expertise. An inclusive board fosters a culture in which diverse perspectives are actively solicited and thoughtfully considered, ensuring that all voices are heard. Importantly, an inclusive board does not mean that everyone’s input has equal weight in decision-making; rather, it ensures that decisions are enriched by the breadth of input and made transparently, with clarity around how and why certain viewpoints shape certain outcomes.

However, we discovered that many chairs unintentionally overlook certain behaviors that meaningfully shape their boardrooms. The unintentional part is really important: Every chairperson we talked to expressed their intention to create inclusive boardrooms. And the nonchair directors with whom we spoke emphasized that even in the least inclusive boardrooms in which they sat, there was never the feeling that the chair wanted to exclude certain people or voices. The chairs just weren’t aware of the implications of their actions or failure to act.

Based on our interviews, we identified five overlooked behaviors that any chair, regardless of personality, leadership style, or cultural context, can implement to foster a more inclusive board. These aren’t the obvious behaviors — such as “ensuring that everyone speaks once” or “asking good questions.” They are less visible, more structural, and more consequential behaviors that the board members we interviewed consistently identified as shaping inclusion. And these behaviors span before, during, and after meetings. When chairs use the behaviors intentionally, they shift boards from simply looking diverse to harnessing their diversity in the discussions they have and decisions they make.

Previous research has suggested that the inclusivity of boards is dependent on the chair’s traits.3 But we have witnessed inclusive (and non-inclusive) boards run by chairs who are introverted, extroverted, analytical, relational, hierarchical, consensus-seeking, and everything in between. Inclusion is not about who the chair is. It’s about what the chair does.

Five Ways Board Chairs Can Act More Inclusively

The board members we interviewed repeatedly highlighted a consistent set of behaviors that shaped their feelings of inclusion, but the board chairs overlooked, underestimated, or misunderstood those behaviors. Here are the key ways chairs can ensure more inclusive board meetings:

1. Use pre-meeting calls to understand perspectives, not to control them. What happens before the meeting itself matters. Pre-meeting calls with individual directors ahead of board meetings are de rigueur for many chairs. But the purpose and format of those calls vary widely — and matter greatly. Some chairs and board members cited pre-calls as detracting from inclusivity, while others cited them as contributing to inclusivity. Pre-calls during which the chair tries to align with certain members ahead of time, expresses their views on specific positions that they want the board to take, or discourages dissent on certain issues all detract from inclusivity. As one seasoned board member told us, “I am terrified to learn that board members voice their views in pre-calls versus in the boardroom and that they do not dare speak up [in the meeting].”

Pre-calls can better promote inclusivity when they are used to review the upcoming agenda to ensure that, based on the directors’ perspectives, nothing is missing. They may also be helpful in mapping the landscape of opinions and, in particular, to surface “quiet voices” and then encourage the more reticent to speak up in the actual meeting. Finally, they are also helpful in building relational trust, especially with newer members who might need to socialize certain ideas before putting them in front of their peers.

For such pre-calls to enhance inclusivity, board members need to be confident that the calls occur with all members, not a select few. If chairs contact only some directors and not others, they’ve created a second board within the board.

2. Frame agenda items for discussion, not presentation. How chairs introduce agenda items shapes who speaks, what gets surfaced, and whose voices carry weight. Board members described two typical approaches — one oriented to presentations, and the other to discussion.

In the former scenario, the chair walks through a topic, outlines key considerations, and only then asks for input. This structure tends to bias the group toward the presenter’s framing, limit dissenting voices, reinforce hierarchical dynamics, and reward directors already aligned with the dominant perspective.

A chair who is oriented toward discussion will introduce the topic, state why it matters, and open the floor to the others before offering a personal perspective. This invites more diverse viewpoints, encourages questioning and constructive disagreement, allows lower-status members (those with a non-CEO background) to speak without contradicting the chair directly, and signals that the decisions are not predetermined. While our interviewees noted that discussion-style meetings can feel messier, they consistently reported that they lead to better decisions and greater inclusion.

One former chair and current board member who sits on the boards of multiple publicly listed companies told us that when he chaired, ahead of the meeting, he circulated a one-page summary on each issue describing why it mattered to the company, and the CEO’s and/or executive team’s perspective on the topic. He then indicated whether it was a topic that was merely up for discussion (and, if so, why it was on the agenda) or whether the board would be making a formal motion on it. He also indicated the time that would be allotted for the agenda item so that directors would know how much discussion would be possible.

Chairs should open the conversational space before narrowing it, and delay their own viewpoints until the board’s perspectives have surfaced. They can also separate discussion from decision-making.

3. Demonstrate hearing, not just listening. All of the chairs we spoke with said that they not only invite everyone to speak up but also signal active listening through nodding, making eye contact, and so on. In fact, active listening was the most common inclusive behavior mentioned by chairs. Nonchair directors told us that these cues are insufficient. Instead, they look for deeper indicators that their contributions were heard, valued, and integrated.

One important way of doing this is to call on directors based on their expertise. Several directors shared stories about meetings in which their domain expertise (such as sustainability, cybersecurity, or AI) was directly relevant to the agenda — and yet the chair did not invite their perspective. At the same time, chairs should avoid confining directors to their perceived expertise. Some chairs believe that they are being efficient by calling on directors only when a topic aligns with their past functional role or domain of expertise, but board members said that this practice reinforces silos, discourages cross-disciplinary thinking, restrains growth and contribution, and sends subtle signals about who “belongs” where.

Likewise, chairs should signal that everyone has their full attention. The most exclusivity-building behavior that directors cited was chairs appearing to favor certain board members by giving them more time to speak, asking them more follow-up questions, making more eye contact with them or smiling at them, and making more frequent references to them (“As Jackson said …”).

4. Ensure that seating arrangements reflect equal distribution of power. Physical space, especially in a boardroom, communicates status and relationships. When it came to creating inclusive board environments, the chairs often had one thought about what led to inclusive seating while the directors had another. More than three-fourths of the chairs we talked to said that they explicitly think about where they sit relative to the other directors. Specifically, they said that they are careful not to sit at the head of the table — a position that denotes power and authority. But when we talked with board members, they mentioned that it was where others sat (relative to the chair) that made the difference.

Most board members preferred assigned seating over free seating. In the latter case, they told us, the higher-status members gravitate toward the chair, while newer members or those from underrepresented groups end up in more peripheral seats, which results in limited side conversations and static influence channels. Directors advocated for assigned seating that is regularly rotated. This disrupts power clusters by preventing the same individuals from forming an inner circle, distributes proximity to the chair, and enables varied informal interactions. New seating leads to new side conversations during breaks. One board member sat on a board where, at every break, the seats were rotated. She said that this provided a new dynamic that allowed for different conversations to emerge within the different seating constellations. However, if there are members of the board with hearing impairments, the chair should ensure that they are regularly seated in a central location so that they don’t miss out on the conversation.

5. Ensure that all members have access to the same information and people. Inclusivity extends beyond meetings. The board members we spoke with also highlighted the importance of equal access to internal stakeholders and information.

Some chairs grant members unfettered access to company executives and employees, whereas others require directors to route all interactions through them. Directors did not express a preference for one model over the other. What mattered most was consistency. Boards became less inclusive when some directors had informal access and others did not; certain members were quietly gatekept.

Directors also emphasized the importance of equitable access to information — particularly committee-level documents. Most chairs said they shared these documents broadly, beyond just the committees for which they were intended. But many nonchair directors reported that only half the boards on which they were serving actually consistently received committee documents. Without question, committee chairs need deeper knowledge and awareness of certain issues on their desks. And when chairs share the documents exclusively within committees, it creates information asymmetry, signals whose perspectives matter, reduces some members’ confidence to speak during discussions, and introduces a hierarchy of insiders versus outsiders. We also heard that inclusivity is enhanced when committee chairs are required to give a comprehensive update during the broader board meeting on what they discussed in the committee. The rule of 6:1 is a good one: Each 60-minute span spent on an item in committee should have a corresponding 10-minute update in the broader board meeting.

Two Additional Practices That Boost Inclusion

Beyond the five core behaviors mentioned above, chairs and directors described two additional practices — neither of which occur around meetings — that meaningfully strengthen inclusive boardrooms: explicitly measuring board inclusiveness and providing board members with opportunities to receive training to fill knowledge gaps.

There are a couple of ways that we have seen chairs effectively measure board inclusiveness. The first is to do it during annual conversations with directors, and the second is to have an outside party measure it through a survey or interviews.

Chairs typically hold yearly one-on-one conversations with directors. Few explicitly ask about inclusion, but those who do generate richer insights and deeper trust. Effective questions can include, but are not limited to, the following:

• Over the past year, when did you feel most included or heard in board discussions? Why?
• Were there times when you felt that your voice was overlooked or undervalued? What was it that made you feel like your voice wasn’t heard or valued?
• Have you ever hesitated to speak up on the board? When was that, and what kept you from speaking up?
• To what extent do you feel that your background and experience are recognized on the board? What is it about the board that leads you to feel this way?
• Do you possess experiences, skills, or perspectives that you feel the board has not tapped into?
• How connected do you feel with your fellow board members outside of formal board interactions? What might make you feel more connected?
• Are there any formal or informal board practices that you think unintentionally exclude some members?
• What support or changes from me, as the chair, would help you feel more engaged or valued on our board?

Such questions normalize conversations about inclusion without making them political or personal. They can also surface board members who might feel less included. And they signal that the chair explicitly cares about the topic.

Another option is to have an outsider, such as a board assessment consultant or academic expert, conduct surveys and interviews. For one board, we surveyed the directors about specific inclusion behaviors, including balanced participation and evidence of listening. At the start of each subsequent meeting, the chair reviewed the aggregated results with the full board and invited discussion of any patterns that required attention. Over time, this consistent feedback loop helped members adjust their own behaviors, reduced dominance effects, encouraged quieter voices to contribute, and fostered a shared sense of accountability for how the group listened. As these listening norms improved, the board became more forward-looking and able to engage in strategic initiatives that strengthened the organization’s prospects.

Helping members close expertise gaps is essential as boards increasingly face complex topics — AI, sustainability, cyber risk, geopolitics — on which only a few directors might feel confident contributing. Some boards even provide their directors with an education budget. One director described staying silent during AI discussions because she felt unqualified and uninformed. Recognizing this, her chair sponsored optional AI training for all directors. Afterward, more directors felt empowered to contribute to these discussions. By investing in learning, the chair democratized influence. This expertise gap can also apply to knowledge about the company. Providing newer board members with onboarding briefings about the organization, its history, and its current strategy can bring them up to speed and allow them to feel more confident to speak during meetings.

Traditional assumptions about strategy, governance, technological readiness, and stakeholder expectations are shifting rapidly. The boards most likely to navigate these shifts successfully will be those that fully capitalize on the diversity of thought sitting around their tables. But that cannot happen without intentional inclusive leadership. We repeatedly heard that inclusion is not an outcome of good intentions; universally, the chairs we spoke to and heard about had good intentions. Inclusion is the output of consistent, conscious behaviors. The encouraging news from our research is that the intention is there and all behaviors that we cited above are accessible, learnable, and controllable. And incorporating those behaviors is unmistakably the chair’s job.

A recent battle for AI talent illustrates how difficult it is for companies to retain key employees. In 2025, OpenAI’s stock-based compensation averaged roughly $1.5 million per employee — unprecedented for a pre-IPO company — yet OpenAI still experienced high-profile defections. Rivals such as Meta were reportedly extending offers in the hundreds of millions of dollars for top AI talent, prompting OpenAI to issue multimillion-dollar one-time retention bonuses and to relax equity vesting requirements twice in a single year. The result was an escalating bidding war with no clear ceiling and no guarantee of success.

That dynamic has not ceased. Rather, the competition for talent has intensified and expanded, with AI companies recruiting not only elite researchers but also senior executives and business leaders from across the technology sector.

The AI talent wars are an extreme case, but they illustrate a broader problem across industries: The most valuable employees are also the most mobile. In sectors such as artificial intelligence and consulting, intense competition reflects rapid technological change that makes certain capabilities suddenly scarce. In health care and skilled trades, retention pressures stem from long-standing workforce shortages. Construction companies are facing a growing labor gap driven in part by retirements, even as demand is rising with the expansion of large-scale infrastructure and data center projects. Across all of these contexts, when an employee leaves, their employer loses embedded knowledge, client trust, and innovation capacity along with them.

Yet most companies respond to this challenge in a tactical, episodic fashion: a counteroffer here, a culture initiative there, or a noncompete added to a contract. These one-off measures address symptoms rather than causes, and, as OpenAI’s experience shows, pay-based competition alone can trap companies in a race they cannot win.

Traditional approaches to understanding the problem focus on factors that make employees move, but what is missing is a systematic framework for understanding why they stay.1 Our recent article in the Journal of Management investigates the factors that limit employee mobility away from an employer — what we call employee mobility barriers.2 Drawing on research from multiple disciplines, we have synthesized these barriers into a practical architecture that managers can use to retain talent more deliberately — and compete for new hires more effectively — in today’s talent wars.

Understanding Employee Mobility Barriers

Whenever an employee considers leaving, they navigate a set of frictions that make moving harder and staying more attractive. These barriers range from the concrete — noncompete agreements, unvested stock, pension accrual — to the more intangible, such as the satisfaction of meaningful work, the promise of career advancement, and social ties that make teamwork productive and fulfilling. Together, such forces shape whether a move feels possible and worthwhile or not worth the disruption.

Barriers differ along two dimensions: level and control. Some barriers operate at the individual level, such as an employee’s career stage, personal preferences, or priorities. Others stem from organizational systems and practices, including social ties, employment contracts, or career development systems. Still others are rooted in broader societal factors, such as visa restrictions or labor market conditions.

Control is the degree to which employers can influence the mobility barrier and employee experiences. Employers can directly shape factors such as compensation, job design, and employment contracts, but they have less influence over others. For instance, employee attributes such as age or personality can be “shaped” only at the point of selection, while social ties and networks emerge over time and are difficult for a company to directly engineer. Other barriers, such as location preferences, regional labor-market conditions, or visa and licensing requirements, are largely outside an employer’s control.

Perceptions also play an important role in how mobility barriers function. How employees interpret such barriers they face and the opportunities available to them elsewhere can influence their decision to stay or leave. For example, an employee who believes that they have better career development opportunities with their current employer than with a competitor may choose to stay, regardless of whether that belief is accurate.

Employee Mobility Barriers in Practice

In practice, employee mobility barriers and companies’ effectiveness at retaining talent can vary across industries, companies, roles, and individuals. What keeps an AI engineer from moving may differ from what retains an industrial salesperson or a veteran nurse. Stock options are a powerful retention mechanism in the tech industry but largely irrelevant in the public sector or education. An entry-level employee building their initial professional network and reputation may experience very different barriers than a more senior employee with strong ties to the organization. Managers need to understand which barriers matter most in their specific contexts and for their employees so that their retention interventions are effective.

This context dependence also informs a crucial strategic principle: The best mobility barriers are those that are difficult for competitors to replicate. Compensation alone is a weak barrier in this regard because it can trap companies into bidding wars that better-resourced rivals can win, as OpenAI’s experience demonstrates. In contrast, Anthropic, founded by former OpenAI engineers, has emphasized employee autonomy and AI safety and has achieved higher retention rates than its rivals as a result. Similarly, Periodic Labs lured more than 20 engineers away from Meta, OpenAI, and Google DeepMind, despite offering lower pay, by promising a distinctive work environment focused on scientific discovery — closer in spirit to traditional research labs than the typical Silicon Valley firm is. Culture, mission, and working conditions can be more durable barriers precisely because they are embedded in how people work and interact — and thus far harder for competitors to imitate or re-create.

Understanding employee mobility barriers is also valuable for attracting new hires. An effective job offer should consider the barriers a candidate faces in leaving their current role. Sometimes that means offering what a candidate would be giving up. That could mean convincing a candidate that future colleagues and work conditions will be as good as what they are leaving behind, for example. Other times, it means offering something that the current employer cannot match, such as a path for career growth or a unique project. Thinking about hiring through the lens of mobility barriers turns recruiting from a pitch into a more intentional, targeted conversation.

An Architecture for Managing Employee Mobility Barriers

The varying levels and degrees of managerial control of employee mobility barriers make developing a coherent, companywide talent management strategy challenging.

To help leaders navigate this complexity, we developed an architecture of strategic modes that characterizes how companies manage employee mobility barriers, based on the two dimensions introduced above: the level of the barrier (individual, organizational, or societal) and the degree of control the employer has over it (high or low). (See “Architecture of Strategic Modes for Managing Employee Mobility Barriers.”) While firms may rely on or encounter a portfolio of different mobility barriers, the architecture of strategic modes focuses on how those barriers are managed.

Together, these dimensions define four strategic modes: For any given mobility barrier, its level and the degree of employer control determine how companies should manage it — either by directly shaping the barrier (proactive) or by adjusting other organizational practices (reactive); and either leaving it to individual managers to design and implement (delegated) or rolling out organizationwide policies (centralized).

When mobility barriers operate at the individual level and are outside the employer’s control —such as personal ties to colleagues or family circumstances — the appropriate response will be reactive and delegated (upper-left quadrant). The right move here is to empower managers to recognize these situations and respond with accommodations where appropriate, such as making schedule adjustments or redefining a role to better fit an employee’s circumstances or needs.

When mobility barriers operate at the individual level but are within the company’s control, the employer can take a proactive and delegated approach (upper-right quadrant). In these cases, managers actively design roles and work experiences — through job design, meaningful project assignments, or customized development opportunities, for example. In this quadrant, managers have the discretion to shape these elements based on the needs of their teams.

In contrast, when mobility barriers operate at the organizational or societal level and are outside the company’s control, the employer must respond in a reactive and centralized manner (lower-left quadrant). In these situations, businesses cannot directly control the focal barrier. Instead, they need to act indirectly by reconfiguring how work is organized or by introducing complementary practices that allow them to operate effectively under the constraints. Accommodating existing mobility constraints may involve reallocating work across teams, restructuring collaboration patterns, or redesigning how tasks are distributed.

Finally, when mobility barriers operate at the organizational level and are within the company’s control, the employer can take a proactive and centralized approach (lower-right quadrant). This mode captures the types of organizationwide programs and policies that companies typically associate with retention efforts. However, although these system-level interventions are important, they represent only one part of a broader set of strategies that organizations must use to manage mobility barriers effectively. No company manages in only one quadrant. Effective management layers responses across all four modes, combining tailored individual-specific interventions with companywide systems and applying different approaches to different types of mobility barriers.

Managing Employee Mobility: A Toolkit for Managers

Understanding the architecture of employee mobility barriers can inform how employers manage them. To develop a systematic approach, managers should take these three steps:

1. Inventory employee mobility barriers. Effectively managing employee mobility is not about implementing a single policy, program, or perk. Rather, it requires a systematic approach that combines multiple mobility barriers and management strategies. As a starting point, managers should develop an inventory of the barriers that matter for current employees and for the talent that the company wants to attract. This inventory must be customized to different functional areas, the needs of managers responsible for specific teams, and, in some cases, strategically important employees. It should also be reviewed and updated over time as business conditions, labor markets, and employee priorities change.

2. Evaluate for importance and impact. Once an inventory has been done, managers should assess it along two lines. First, they should determine which mobility barriers matter most in their specific context; not all barriers carry the same weight in shaping employee retention or attraction. For example, stock-based incentives may be highly effective for senior technical talent, whereas embedded social networks may matter more for midlevel managers.

Second, managers should identify gaps in the current mix of barriers and consider how different barriers reinforce or substitute for one another. By understanding where the most significant barriers lie, companies can focus their resources more effectively, avoiding both over- and underinvestment while identifying opportunities to strengthen complementary barriers. Like the inventory, this assessment should be treated as an ongoing exercise that evolves with business conditions, labor markets, and employee priorities — not as a one-time audit.

3. Match management strategies to mobility barriers. With a clear understanding of which barriers matter most, managers can determine how best to respond to them. The appropriate approach depends on which aspects of mobility the company can directly shape and which it cannot.

When companies can control mobility barriers, they can often deploy them strategically through organizationwide systems and policies. For example, companies often design structured career pathways, job-rotation programs, or stock-based incentives to retain employees. Salesforce’s AI-powered internal mobility platform, Career Connect, illustrates this approach: By providing personalized skills recommendations, highlighting internal career pathways, and suggesting upskilling opportunities, it helps employees find growth opportunities within the organization so they will not seek them elsewhere. Such a proactive, companywide initiative can help organizations retain employees by supporting internal career advancement.

Organizations can also proactively deploy mobility barriers in response to changing conditions. When the Federal Aviation Administration faced critical air traffic controller shortages in 2025, driven by retirements and its inability to hire and train replacements quickly enough, it responded with systemwide retention bonuses aimed at keeping experienced controllers from leaving. Although prompted by external pressures, that response reflects the deliberate use of centrally managed barriers organizations can proactively deploy.

In other cases, mobility barriers arise from conditions an organization cannot directly influence, particularly at the individual level. Here, effective management depends on managerial discretion and flexibility. A delegated or decentralized approach works in such cases because the managers closest to employees have the best understanding of their needs and can design effective retention strategies if empowered to do so. Such delegated actions may involve designing customized job roles, offering flexible schedules, or providing targeted professional development opportunities. For example, when Goldman Sachs’s international vice chairman, Richard Gnodde, decided to relocate from the U.K. to Milan to take advantage of Italy’s tax policy, his role was adapted so he could continue working for the company from a different location. This type of intervention illustrates how organizations can respond to mobility barriers outside their control by adapting the employment relationship at the individual level.

Taken together, these examples highlight a broader pattern: Centralized approaches are most effective when companies can shape organizationwide mobility barriers directly, while delegated approaches are better suited to barriers rooted in individual circumstances. In practice, barriers often operate at both the individual and organizational levels simultaneously, so an integrated approach to managing them is called for. For example, businesses may combine formal, organizationwide initiatives with locally driven efforts that managers and employees help shape. At financial services provider Synchrony, for instance, employees have launched many of the firm’s volunteer and community programs, illustrating how decentralized initiatives can strengthen social ties and organizational identification. Such efforts reinforce employees’ connection to the employer while they operate alongside more structured organizational practices, enhancing mobility barriers that cannot be fully designed from the top down.

Effective management in these cases requires active coordination between centralized programs and local managerial discretion. That may require significant training and communication, along with a shared understanding among managers at different levels of hierarchy and across the organization. Realistically, organizations may need to be highly selective about the set of barriers for which such intensive coordination is used. By combining central control with delegated discretion, organizations can programmatically deploy mobility barriers while remaining flexible to individual employee needs.

The intensifying AI talent wars highlight how challenging it has become for companies to retain and attract mobile employees. As competition for highly skilled workers grows, technological change is making valuable skills more portable, while remote and hybrid work are weakening some of the geographic and social barriers that once kept employees in place. At the same time, advances in AI are reshaping which forms of knowledge will remain company-specific: AI agents are beginning to take on tasks previously performed by humans, altering sources of retention advantage in ways that are still unfolding.

Against this backdrop, the underlying logic of employee mobility barriers becomes even more important. Rather than relying on any single retention tool, organizations must understand the full range of barriers shaping employee decisions to stay and match their strategies accordingly. What varies across organizations is not whether mobility barriers matter but which ones matter most and how effectively they are managed. This holds true regardless of how the specific barriers evolve. The managers best positioned to navigate these shifts will treat talent strategy as a dynamic system that requires them to continuously identify relevant barriers, evaluate their impact, and adjust responses over time. Organizations that adopt this approach will be better equipped not only to retain critical talent but also to adapt their retention strategies as conditions evolve.

Every organization, and every hiring manager, wants to know how to hire the best people. And every company wants to feel like it has an edge over its competitors. But let me ask you a personal question: Can you think back to a moment when someone in a decision-making position took a chance on you? Perhaps you were young and inexperienced, and maybe you didn’t have a flawless resume or a spotless track record, but they saw something worth investing in. You almost certainly rose to the occasion. You probably gave more to that person or organization than your experience or credentials alone would have suggested.

Why are so few leaders making that kind of bet today? And what are organizations losing as a result?

I have spent more than two decades studying how organizations recruit, promote, reward, and retain employees. In doing so, I have discovered that a company’s willingness to grant opportunity shapes employment interest and recruitment, job engagement, effort, and retention — far more than compensation or job design do. Opportunity can unlock human motivation and potential, which, I would argue, are among the most underpriced assets on your balance sheet.

Most leaders, however, reserve their biggest bets and opportunities for people they already perceive as “the best.” They want the candidate who has the most impressive credentials and checks every box on the job description. This instinct feels prudent, as if leaders are making a safe bet. In practice, it is one of the most expensive talent-management mistakes any organization can make.

Consider two problems with the instinct to hire or promote only the best. First, contrary to popular wisdom, nobody truly knows who the best talent is. My research shows that people rarely agree on what qualities or achievements matter for success in a given role. Two hiring managers in the same company, evaluating the same candidate, can reach wildly different conclusions about their fit, potential, and leadership qualities. Every company likes to believe it has a rigorous, objective talent-selection process. But in practice, decisions frequently rely more on subjective judgments and gut feelings. Many managers also default to conventional, easily defensible choices. After all, no one ever got fired for hiring the guy with a prestigious university credential.

The second problem is a market dynamic. The fixation on the very best pushes organizations into what I call an only the best mindset, which is the belief that a scarce, identifiable elite is out there and that success depends on finding them, hiring them, and paying them extraordinary salaries.

There have been dramatic examples of this recently, such as when Meta CEO Mark Zuckerberg led an aggressive drive to recruit top AI talent by offering compensation packages of up to $300 million over four years. But the "only the best" mindset produces a predictable set of outcomes: It often takes companies longer to fill positions, compensation bands become eye-watering, and talent pipelines narrow to the same small set of schools and employers.

This mindset also creates talent risks you may be underestimating. Star performers brought in from other organizations often perform worse once they move, as Boris Groysberg, Ashish Nanda, and Nitin Nohria famously highlighted. You might be paying a premium for a signal that fails to travel.

Another Way: Opportunity Mindset

There is an alternative to this chimerical pursuit of the best. I call it the opportunity mindset, and I want to be precise about what that means. It does not mean lowering the bar and settling for second-rate talent. It’s recognizing that identifying candidates who look great on paper is often entirely different from identifying those who would be good fits for the role. Recruiters and hiring managers who recognize this fact have a huge competitive advantage over those who do not.

Leaders with an opportunity mindset hire candidates who are good enough to succeed and then build the structural conditions — through excellent onboarding, mentorship, development, and evaluation processes — that help good people become great. These leaders evaluate potential by what a person actually does on the job, not by schools attended, past employers, or previous job titles.

In practice, here’s what the shift to an opportunity mindset looks like. Say your engineering team has an open entry-level role. The traditional process would typically narrow the pipeline down to candidates with a four-year computer science degree from a highly ranked university. A company with an opportunity mindset instead asks what key skills the job actually requires and then opens the candidate pool to include nontraditional applicants, such as community college graduates, career switchers, parents returning to work, or apprentices. Some applicants might not have a college degree, but if they can demonstrate the necessary skills, they are considered for the role.

Alternatively, imagine that a more senior person is needed. Rather than conducting an expensive external search, your organization posts the promotion opportunity internally and invites applications from anyone whose skills match the requirements. The role might not go to the employee whose face is most familiar to senior leadership but to the employee whose current work trajectory best predicts success.

Each of these choices is small. But, over time, they compound, deepening the talent pool and creating a more versatile and diverse workforce.

This tension between the "only the best" mindset and the opportunity mindset is at the heart of my book The Meritocracy Paradox. In it, I show how talent management systems that claim to reward top talent and hard work — the so-called meritocratic systems — can unintentionally reinforce bias and inefficiency. When hiring managers believe that their decisions are driven by purely objective metrics or that they operate in meritocratic organizations, they may fall prey to subconscious biases, such as perceiving the best as people who are demographically or culturally similar to themselves. In contrast, when organizations embrace an opportunity mindset, a key feature of truly meritocratic organizations, they shift from chasing after talent perfection to investing in potential.

Some organizations have already moved in this direction. For example, years ago, under now former CEO Ginni Rometty, IBM concluded that the traditional four-year-degree requirement was screening out capable workers and driving up the cost of talent without materially improving performance. IBM rebuilt its hiring practices to emphasize skills, launched apprenticeship programs to recruit from high schools and community colleges, and increased professional development opportunities for existing employees.

Bank of America implemented a skills-first approach and reported in 2022 that internal promotions accounted for nearly 45% of filled roles. This practice ensures that institutional knowledge stays put. Google, meanwhile, treats its own professional certificates as equivalent to four-year degrees for many entry-level roles. Both organizations have done the math and concluded that the only the best pipeline is too narrow, slow, and expensive to defend. Researchers have found similar gains at companies like Costco, Trader Joe’s, Mercadona (in Spain), and QuikTrip, where investment in employee skills development positively correlates with lower turnover, higher productivity, and stronger financial performance.

What About the Hiring Recession?

I anticipate an objection at this point, and it is a reasonable one. Early examples of skills-first hiring emerged from a different labor market era, in which workers were scarce and employers were desperate to widen the hiring funnel. The market has since changed. The white-collar job market has softened for job hunters, wage growth has slowed, and employees are holding on to their jobs instead of searching. College graduates, even those with desirable degrees from prestigious schools, are reporting lengthy and challenging job searches. In other words, we’re in the midst of a hiring recession, and the leverage has shifted to employers.

If you’re a hiring executive, you might think that you can now afford to be selective in a way you couldn’t have been just a few years ago. So why, you might reasonably ask, should you adopt an opportunity mindset now, when the case for holding out for the perfect resume has never been stronger?

I would respond that the case for an opportunity mindset has never been about scarcity. It has always been about return on investment. First, a hiring recession gives hiring managers more applicants, but not better judgment about which ones will succeed. If anything, a flood of AI-polished resumes makes credentialed signals noisier: Robert Half, for instance, reported that two-thirds of HR leaders now say that AI-generated applications are slowing their hiring, and a majority of hiring managers find AI-enhanced resumes harder to verify and trust. Grade inflation has added to the confusion, frustrating those employers that want to use GPA as a distinguishing factor for potential hires. When the paper trail can no longer be trusted, structured skills assessments and paid work trials become more valuable, not less.

Second, a soft hiring market is exactly when bets on unconventional candidates are cheapest. You are not bidding against 10 competitors for the same star employee; you are choosing from a deeper pool at lower cost. The capable career-switcher who would have been snapped up elsewhere in 2022 is now in your funnel.

Third, and most importantly, the current hiring recession will eventually come to an end. Demographic changes, such as an aging workforce and lower immigration, will shrink the labor force regardless of the current economic cycle. The organizations that can take a long-term approach, anticipate their future talent needs, and begin building that capacity from within, while they can, will benefit enormously when the market turns. Those that do not will have to find that capacity under pressure, at premium cost.

Let me close with a practical challenge for you and your organization. When making your next hiring decision, try one small experiment. Identify a small set of necessary (and specifically job-relevant) skills that predict success on the job. Evaluate every finalist using the same consistent assessment, giving everyone, from recent graduates to Generation Xers, the same shot. Then, once someone has been hired, commit to a specific development plan (if necessary) for their first year. Carefully track what happens to their performance, their retention, their growth, and the performance of the team around them. If the experiment fails, you have likely lost little. If it works, you will have found something your competitors are still paying a premium to chase: a way to turn good candidates into great employees.

This is the essence of the opportunity mindset: building a more resilient, more inclusive, and future-oriented organization. Remember the bet that someone once made on you. Recall how the returns compounded. The leaders who learn to make similar bets on others, systematically and thoughtfully, especially now, will be the ones who build the enduring organizations of the future.

We live on a blue planet where water appears to be plentiful. But there are strong warning signs indicating that our reliance on the fresh water we perceive to be abundant will need to change. Although water covers 70% of the Earth’s surface, only 0.5% of it is effectively usable. Further, global water cycles are increasingly becoming unbalanced in ways that threaten humans, plants, and other life forms that depend on fresh water.

Rapidly growing demands for water due to population growth and industrial uses, as well as issues such as misuse, pollution, and the interconnected effects of climate change, are contributing to a situation in which companies and communities can no longer take the availability of water for granted. As competition for water intensifies, the consequences could be severe for health and well-being, economic development, societal stability, and security — that is, unless we significantly rethink how all of us, including companies, manage it.

Growing Water Risks and Challenges

Many companies treat water as a utility controlled by operations or plant managers. They calculate their demand for it based on their business models, process designs, and product characteristics but rarely manage it as a key factor shaping economic competitiveness and strategic decision-making. In many cases, water is seen as a hygiene factor: essential, but not critical enough to register as a risk. Accordingly, procurement teams seek to ensure the security of the water supply, and costs are kept low, while public affairs teams handle any regulatory questions. But this widely established approach is now coming under significant pressure amid consensus that water demand and related challenges are intensifying globally.

In a 2024 report, the Global Commission on the Economics of Water warned of a growing water disaster: “We can no longer count on freshwater availability for our collective future.” By the end of 2025, 35.8% of the U.S. was experiencing a drought, with conditions touching nearly every region. Impacts included catastrophic wildfires in Southern California, historic low water levels for the Mississippi River, and record‑low streamflow in the Northeast. Worldwide, roughly 30% of global land area experienced drought conditions in 2025, driven by near‑record warming.

Drought is one of many current problems, such as poor water quality due to pollution from farm runoff, pharmaceutical residues, and the proliferation of nanoparticles in water. All of those issues create new operational and regulatory demands and challenges. For example, France’s ban on PFAS, which came into force in June 2026, requires that drinking water be tested for those synthetic “forever chemicals,” which are estimated to affect the water supplies of some 12.5 million people across Europe. The law also penalizes companies for releasing such chemicals into the environment.

All of those situations raise the stakes for how companies manage their use of water, because the exposure to water risks is widespread. Consider fast-growing companies and sectors that significantly depend on water for their operational processes. These notably include AI data centers, which require substantial amounts of water for cooling purposes; growing public opposition to these operations is driven in part by their threat to the sustainability of local water supplies. The emerging green hydrogen sector, which uses water rather than fossil fuels as the primary raw material for hydrogen production, faces pushback, given the process’s high demand for water. The technology industry’s increasing demand for critical minerals, such as those used in rechargeable batteries, also puts pressure on water, which is used in mining operations worldwide. Traditional sectors, such as the food and beverage industry, account for 20% of water withdrawals. Textile production continues to consume — and contaminate — large amounts of water: It is responsible for 20% of industrial water pollution worldwide.

However, it’s not just large industries that face water risk. It also plays a nontrivial role in many small businesses, including car wash facilities, breweries, and restaurants; shortages can also affect the construction, health care, sports, and tourism sectors. What this means is that executives can no longer ignore water issues.

The Complexities of Managing Water

Executives tuned in to these issues have begun to appreciate the complex relationships of water flows and cycles and are taking greater interest in and ownership of the resources across their production and supply chain processes. Managing water and making its use more efficient requires the creation of multiple new KPIs everywhere the company uses water. Managers must monitor multiple indicators for water stewardship, such as total water withdrawal, the water recycle/reuse rate, wastewater treatment improvements, and watershed restoration impact, to name a few. They must account for both the inflows and outflows.

However, many companies have a worrisome blind spot: They typically view water as an organizational concern, potentially ignoring extensive dependencies in their supply chains. For example, companies dependent on agricultural inputs can’t ignore the fact that this sector is responsible for almost 70% of global withdrawals of fresh water. As one manager in the agri-food sector we interviewed put it, “If you are not monitoring water in your supply chain, what are you doing? In my sector, droughts and water contamination both happen in my supply chain.”

Another factor adding to the complexity is that water supplies are essentially local, held in basins and aquifers, and so water scarcity creates localized (rather than general) risks. For companies with large and extended supply chains, this means that one-size-fits-all approaches and policies are unlikely to be sufficient or effective. For instance, a company with three sites in the U.S. Northeast could be affected by sewage contamination while its two sites in the Southwest face severe drought conditions. Uniform corporate policies and standards provide insufficient guidance here. Instead, companies need to empower all site managers to complete a thorough water risk analysis, identify local stakeholders they can work with, and develop action plans to tackle such localized risks.

Strategic Water Management Responses

So how should companies respond to the changing realities of this critical resource? As with so many similar issues, understanding the risks and dependencies is key. There are freely available resources that can provide businesses with detailed guidance and support, including the International Water Stewardship Standard, the Science Based Targets Network’s Step Up for Nature initiative, the U.N. Global Compact’s CEO Water Mandate, the World Resources Institute’s Aqueduct tools, and the WWF Water Risk Filter.

Companies must shift from treating water as a utility to elevating it to a strategic priority. Doing this well may require that they establish cross-functional teams to obtain all relevant managerial perspectives and tap into diverse expertise. Crucially, they must engage with key stakeholders, including suppliers, regulatory authorities, nonprofits, and academia, in order to get the full and evolving picture.

Executives should familiarize themselves with the key issues and the specialist language used to discuss matters such as water abstraction and withdrawal, water basins, water stress levels, and so on. At a minimum, they need to assess the direct and indirect water footprints involved in running their businesses before gauging the extent to which this dependence is threatened by emerging water trends.

Operationally, reducing water consumption through greater efficiencies, recycling, and business model transformation is essential but requires investment and technological know-how. Dedicated supplier engagement and/or diversification will be critical if risks cannot be mitigated. Where possible, replenishing or balancing water at source levels must be considered as an option for increased legitimacy and resilience. This requires companies to actively engage or invest in efforts to help restore aquifers or other water reservoirs to effectively balance out the amount of freshwater withdrawn.

In short, there are no simple answers, but ignorance is likely to create growing business risks. Managers must therefore approach water as a valuable resource to be preserved, not drained.

Most leadership advice is written for beginnings — how to launch initiatives, build momentum, scale ideas, and manage new projects. Far less attention is given to the work of ending something well.

Yet endings are everywhere in organizational life. A team is disbanded. A project is closed. A product is retired. A partnership concludes. A strategy is abandoned. A publication, department, or institution reaches its final chapter.

These moments are often treated as administrative events: Announce the decision, set the dates, manage the handover, move on. But endings are cultural tests as well as operational transitions. People remember how something ended because endings reveal what leaders truly value. Did they tell the truth? Did they honor the work? Did they protect people’s dignity? Did they preserve what mattered? Or did they rush to the next chapter so quickly that the previous one felt erased?

Ending well does not mean making everyone feel good about the decision. Some endings are painful or contested. But leaders can still shape how people experience them. The following practices can help.

Say Clearly What Is Ending

First, use truthful language. In difficult moments, leaders typically soften changes with words and phrases like “transition,” “realignment,” “sunsetting,” or “moving in a new direction.” Sometimes these words are accurate, but more often they are ways to avoid saying the harder sentence: “This is ending.”

People cannot process an ending if leaders refuse to name it. Say plainly what will stop, when it will stop, and what will change as a result. For example: “This team will close at the end of the quarter.” “This product will no longer be supported after June.” “This partnership will not continue next year.”

Plain language cannot remove emotion, but it reduces confusion. It also signals respect. Adults can usually handle difficult news. What damages trust is the feeling that leaders are trying to manage employees’ reactions rather than telling them the truth.

Explain Without Overexplaining

People need to understand why an ending is happening, but they do not need a defensive essay. Overexplaining can make leaders sound as if they are trying to win an argument that has already been decided.

A better structure: Lay out the decision, reason, constraint, and consequence. “We are closing this program. Participation has fallen for three years, the cost base is no longer sustainable, and continuing it would require cutting investments from higher-impact work. That means the final cohort will finish in September, and we will support affected staff through the transition.”

This kind of explanation does not ask people to like the decision. It gives them enough context to understand the logic and sufficient specifics to understand what happens next.

Honor What Was, Before Discussing What’s Next

Leaders often move too quickly from closure to future plans. They want to reassure people that there is a path forward — an understandable instinct. But premature future-talk can feel like erasure.

Before asking people to move on, name what the work made possible. What did the team build? Who benefited? What standards did it set? What relationships did it create? What should people be proud of?

The answers do not have to be sentimental, but they should be specific. “This team reduced response times by 40%.” “This project changed how we serve customers.” “This publication gave managers a place to think seriously about practice.” Specific recognition tells people that the work mattered, even if it will not continue.

Explain What Is and Isn’t Known

During an ending, ambiguity becomes emotional labor. People fill gaps with speculation: What happens to my role? Who owns the remaining work? What should I tell clients? What happens to the archive? Who makes final decisions?

Leaders should answer practical questions as early as possible, even if some answers are incomplete. A useful checklist includes: timeline, responsibilities, decision rights, stakeholder communication, support available, work to be paused, work to be completed, and what will happen after closure.

Where certainty is not yet possible, say so clearly: “We do not yet know X. We expect to know by Y date. Until then, Z remains the operating assumption.” Stating what is and is not known is better than silence because it offers people a sense of certainty.

Let People Have Mixed Feelings

Endings rarely produce one emotion, even within one individual. People may feel proud, angry, relieved, disappointed, loyal, anxious, and exhausted all at the same time. Leaders should resist the urge to impose a single emotional narrative.

Do not demand positivity. Do not tell people to be excited about the next chapter before they have absorbed the end of this one. Do not interpret sadness as resistance or questions as disloyalty.

A better message is: “People will feel differently about this, and those reactions may change over time. We can acknowledge that honestly while still doing the work needed to close this down well.” This type of statement preserves a necessary sense of responsibility for people’s work while giving them permission to be human and feel emotion.

Preserve What Should Last

When something ends, its impact can vanish quickly. Files are archived, routines stop, institutional memory fades as people leave. Leaders should decide deliberately what needs to be preserved. This might include a final report, a lessons-learned document, a customer handover, a public archive, a closing note, a celebration of contributors, or a record of practices that should continue elsewhere.

The point is not to preserve everything. It is to protect what has lasting value. Ask three questions: What did we learn? What should others inherit? What should not have to be rediscovered later? These questions transform closure into stewardship.

End With Dignity, Not Just Efficiency

A dignified ending gives people enough truth to understand, enough structure to act, and enough recognition to feel that their work counted for something. An efficient ending is not necessarily dignified.

The final meeting, message, or milestone matters. It should not be treated as a formality. Leaders should use the opportunity to specifically thank people, to mark the transition clearly, and to communicate what will be carried forward. People do not need a grand ritual, but they do need a meaningful one. The goal is not to make the ending painless — which would be impossible — but to make it conclusive.

Endings ask whether leaders can be honest without being cold, compassionate without being vague, and forward-looking without erasing the past. When leaders end something well, they give their people clarity and closure: evidence that the work mattered, that the truth can be spoken, and that dignity does not have to disappear at the end.

Innovation leaders often know how to manage success well. A fairly good idea is recognized, a strong contributor is rewarded, and the lessons from the win are carried into the next team project. In many cases, that is exactly the right organizational response.
 
But, sooner or later, most organizations have a different kind of success altogether — a next-level idea and success tied to one person: the engineer behind a product that changes the trajectory of a business unit; the scientist whose patent is embedded across product lines; the designer whose solution unlocks a long-stalled project. After a breakthrough like this, CEOs and managers often respond in the same way they would to any success, but with the volume turned up: more reward, more recognition, more autonomy, and a push toward the next big idea.
 
It’s the obvious response to exceptional success. Yet, the next big idea rarely comes. Our research suggests that this is because extreme success is not simply a bigger version of ordinary success. It can fundamentally change how innovators see themselves and how they respond to the people around them.

Drawing on both archival and experimental data, our research shows that innovators who deliver one extraordinary hit are strikingly unlikely to repeat it, and the way organizations manage them after the win is part of the reason why.1

The Stress of Exceptional Success

Why is extreme success particularly counterproductive for innovators? Our hypothesis was that the strengths that help someone produce a breakthrough are not always the same as the strengths necessary to sustain innovation over time. Why?

First, exceptional innovators may be highly skilled at solving hard problems, but they are not necessarily leaders who instinctively build teams or recognize and draw on the strengths of others. After an extreme success, the exceptional innovator may come to attribute the achievement mainly to their own talent and hard work and assume that they can do it again on their own. As a result, they may undervalue what a team can contribute and overestimate the costs of coordination. Consequently, even though their earlier success often attracts collaborators, they may see less need to work with others on the next idea.

Second, extreme success can inflate how innovators see their own status in the organization. This can shift their attention away from developing the next big idea and toward preserving the standing that came with the last one. Such a shift can also make them less open to feedback that could improve the next idea but might threaten their newly acquired standing.

To test our hypothesis, we looked first at four years of data from a global automotive company’s pool of 1,145 ideas submitted by 236 serial inventors. This company runs an internal ideation platform where employees can submit ideas. When an idea is selected and implemented, the inventor receives a monetary reward whose amount is set after the fact. There’s no cap on the reward: Most ideas earn modest amounts, but a few have generated significant payouts. It’s not a bonus or commission structure; the variable cash prizes are tied directly to the value of each idea.

The monetary rewards for implemented ideas in this full data set ranged from zero to 11,313 euros (about $13,112), with a mean of 290.83 euros and a standard deviation of 760.28 euros. We defined “extreme success” as an exceptionally large reward relative to the system’s normal reward pattern — specifically, one that exceeded 1,051 euros (one standard deviation above the mean).

In comparing innovators who had experienced extreme success with those who had not, we found that prior extreme success significantly reduced the likelihood of subsequent idea implementation by approximately 42%, on average.

This analysis supported our hypothesis about the rate of future success. Extreme success triggers two psychological shifts that explain its negative effect on future innovations. First, the success inflates inventors’ self-perceived social status within the organization. Second, it reduces their willingness to engage in team-based idea development: Team collaboration decreased by 16%, on average, after an extreme success, our research found. Notably, this pattern is not limited to purely individual wins; it can also emerge when the extreme-success idea is developed in a team.

Validating the Findings With a Randomized Experiment

To understand more about the cause of this dynamic, we set up an online experiment with 300 professionals in the U.K.

Participants were instructed to imagine themselves as employees at a confectionery company that was organizing an innovation contest to develop a new kid-friendly packaging design. They read a detailed brief about the contest requirements, which included submitting a 500-word proposal that included details related to visuals and interactive elements of the new packaging design. To make the task feel more realistic and engaging to the test subjects, participants were asked to submit a short written overview outlining their proposed design.

Following the submission of their ideas, each participant received a randomly assigned evaluation from a supposed expert panel. Contestants rated as extremely successful were told that their proposal surpassed all expectations and scored 95 out of 100, while those in the ordinary success condition were told that their proposal met all expectations, with a score of 70, and those in the failure condition were told that their proposal fell short of the expectations and scored only 40.

To measure self-perceived social status, participants were then presented with an image of a ladder with rungs representing employees’ standing within the company based on their role in driving innovation. Participants were informed that people at the top of the ladder were the most influential in driving innovation — that they received the most recognition for their innovative ideas, earned the highest respect for their contributions, and were seen as leaders in innovation. In contrast, at the bottom were people with the least influence on innovation, who were least recognized for their ideas, received little respect for their contributions as innovators, and were not seen as leaders in innovation. Participants were then asked to consider how their colleagues would rate them and to select the number (on a 0-10 scale) that best reflected their position on the ladder.

After their valuation and self-evaluation, participants were invited to enter another contest, to develop a new chocolate flavor, and were presented with the following question: “As you prepare for the contest, you have a chance to join a team of your colleagues who have invited you to join their team. How would you prefer to participate in this contest?”

As we found in our automotive company’s ideation platform data, participants who had been rated as extraordinary successes saw themselves as better innovators and were less interested in joining a team.

Preventing One-Hit Wonders: Four Best Practices

Extremely successful innovators and contributors are disproportionately important for organizations. They signal exceptional talent, shape outcomes, and raise the bar for everyone around them. Yet, the very success that marks them as stars can undermine what they do next. We are not suggesting that extreme success makes future success impossible; in rare cases, innovators do deliver repeated breakthroughs. But our evidence suggests that they do so despite the headwinds that extreme success creates, not because of any momentum it provides.

Many organizations are good at celebrating exceptional innovators after a major win, but when that success is not managed carefully, celebration can become counterproductive. More status, latitude, and deference may encourage the very behaviors that make future success harder to repeat: less openness to input, less collaboration, and more reliance on individual judgment.

The person first celebrated as a “rock star” can gradually come to operate like a “cowboy,” work like a “lone wolf,” and be experienced by colleagues as a “brilliant jerk.” The labels vary, but they point to the same managerial problem: A past success has been allowed to distort the behaviors needed for future success. To reduce this risk, managers should rethink how they handle innovators after extreme success and try to contain status inflation and make collaboration the most attractive path to the next win. Consider these four practices for managers.

• Make the innovator’s support network visible, and reward it. Extreme success rarely happens in a vacuum. Even if an idea is formally attributed to one person, it was often shaped by a broader network, both visible and invisible. Borrowing from scientific publishing, ask innovators to complete a short contribution statement that names direct collaborators and “invisible” supporters, such as colleagues who offered feedback, resources, or encouragement. This could also be paired with a peer-bonus feature, similar to the peer-recognition systems used at Google: After a big success, the winner receives a small amount of additional bonus money that can be allocated only to people who contributed to the success but didn’t appear on the formal submission.

  Together, these practices dampen “it was all me” attributions, make collaboration tangible, and signal that the organization rewards the ecosystem around an idea, not just the individual who submitted it.

• Frame extreme success as a milestone, and follow it with honest feedback. To prevent innovators from feeling like they have “made it,” managers should avoid excessive hype and rock-star language. Instead, frame extreme wins as major milestones that mark progress, while making it clear that the real goal is to figure out what comes next. Crucially, this framing should be paired with constructive, even disconfirming feedback — an approach that has proved effective in sustaining high performance in both trading and innovation.2

  Honest developmental input after a big win counters the status-related thinking that can set in when everyone around a successful innovator shifts to pure praise.

• Restructure rewards to sustain motivation and encourage teamwork. Rather than maximizing immediate cash payouts for breakthrough ideas, cap short-term rewards and shift the bulk of bonuses to deferred structures, such as equity grants, monthly stock units, or phased payments over time. Meanwhile, also broaden what gets rewarded: Recognize not only the idea generator but also the contributors needed to evaluate, develop, and implement ideas.

  Research shows that team-based reward structures stimulate collaborative behavior, and deferred incentives maintain motivation while fostering long-term alignment with the organization’s innovation goals.3 This follows the same logic that CEOs already apply to executive compensation through vesting schedules and long-term incentive plans.

• Invest in supportive leadership after a big win. The period after extreme success is when innovators are most vulnerable to status inflation and collaborative withdrawal. This is when managerial attention matters most. Research shows that supportive leadership — motivation, encouragement, and active engagement from a supervisor — can strengthen collaboration and counteract the isolating effects of a high-profile win.4

  Rather than leaving star innovators to coast on reputation, managers should actively promote teamwork, connect innovators with new collaborators, and reinforce that the next idea is a shared endeavor.

All in all, extreme success carries hidden innovation costs because it changes how high performers see themselves and work with others. The patterns are predictable and fixable, but few organizations manage them deliberately.

The biggest unlock for your innovation pipeline may therefore be the simplest: Help your best people win repeatedly, without losing the behaviors that got them there.

The most valuable thing a professional produces is not a faster analysis or a better summary. It is insight: a genuinely new way of seeing a problem, a connection that changes how they understand a situation, or a pattern that nobody has named. Think of a strategy consultant who identifies the real competitive challenge behind a client’s margin erosion, or a team member who notices a silence in the data that everyone else has learned to take for granted.

What makes this kind of discovery hard is that expertise, the very thing that makes professionals effective, also makes certain kinds of insight difficult to reach. The frame that lets them see a problem clearly also shapes what they look for — and what they stop looking for. But insights that change thinking are often found at the edges of that frame, such as in the friction between competing interpretations.

Conversational AI is already moving in this direction. A well-constructed prompt can surface competing interpretations, expose gaps, and challenge assumptions. But agentic AI — systems that are configured and directed rather than conversed with — can take users further still. Unlike a prompted conversation that is bounded by what a human supplies and thinks to ask, an agentic system holds more data and sustains analytical orientations across entire data sets without losing the thread. The discovery moves are the same; the depth is not.

Using agentic AI this way demands a professional skill different from both prompting and automation: knowing how to design systems for insight and how to make sense of what they reveal. We call it directing intelligence.

Two Ways of Working With AI

Most professionals encounter AI as a conversation: They type a question, evaluate the response, refine, then ask again. Their job is to supply the context and hold the analytical thread, and the exchange exists only as long as the window is open. The skill this demands is articulation, and specifically knowing what to ask, how to phrase it, and when to push back.

Agentic AI requires a different kind of interaction. Where prompting is reactive (a human asks, the model responds), an agent is proactive: The user configures it, and it operates. That configuration rests on three choices:

• Context (what the agent can access). Where a prompt depends on what is pasted in, an agent’s context is persistent: It’s connected to databases, documents, and records that the prompter chooses and that endure across interactions.
• Capabilities (what the agent can do). A prompted AI generates text, but an agent acts: running analyses, querying databases, comparing data sets, executing multistep analytical routines, and invoking specialized skills and tools without waiting for human input at each stage.
• Orientation (what the agent pays attention to). This moves past an instruction on how to produce a specific output and instead serves as an analytical directive by setting a purpose and trajectory that shape how the agent encounters whatever the data reveals.

The same context and capabilities, given different orientations, will surface different patterns. A single professional can direct multiple agents against the same data set and get genuinely different discoveries, not by asking different questions but by designing different systems. In practice, the professional designs not just individual agents but the system that connects them, often including an orchestration layer that compares and synthesizes across diverse AI agent outputs.

A single, well-configured agent can produce genuine insight. If an AI agent oriented toward customer behavior is given access to transaction and service records, it might discover that churn is concentrated among clients who are in their second year. The larger opportunity emerges when that agent becomes part of a system. Give the same data to specialist agents oriented toward sales conduct, onboarding experience, product usage, and service history, and each surfaces a different explanation for that second-year pattern. Add an orchestration agent configured to compare their outputs, identify where the explanations converge and diverge, and surface the contradictions that matter most, and the system produces something none of its parts could generate alone. The professional’s job is to design this system and evaluate what it reveals.

Four Approaches to Discovery With Agentic AI

Discovery rarely arrives through a single, well-aimed question. It tends to emerge from friction: from putting things in contact that are normally kept apart. Each of the four approaches that follow creates a specific kind of friction: between competing interpretations, between data and the conversations that surround it, between causes and the levels where they hide, and between categories and the reality they were meant to describe. The insight emerges from the friction itself. (See “Four Ways to Direct Intelligence.”)

Each move is also available through prompting. What agentic architecture adds, through what agents access, do, and pay attention to, is depth.

1. Use multiple lenses. The most reliable way to see past a single interpretation is to hold several at once — not sequentially, but simultaneously — in deliberate tension. When multiple well-reasoned frameworks are applied to the same situation and they contradict one another, the contradiction is itself informative. It points toward something none of the frameworks would surface alone.

Consider a strategy consultant engaged with a midsize manufacturer of engineered metal parts serving the aerospace, automotive, and energy markets. Margins have eroded for three consecutive years, and the CEO has blamed it on competitive pricing pressure across all three segments.

The consultant designs an agentic system. Four specialist agents are configured against the full engagement data set, which includes financials, competitive intelligence, customer contracts, interview transcripts, and internal strategy documents. Each agent is oriented toward a different strategic framework, with an orchestration agent configured to synthesize the specialist agents’ analyses.

A Michael Porter agent finds that aerospace is structurally attractive whereas automotive is structurally punishing, suggesting that the segments should not be treated equivalently.

A Jay Barney/VRIO (value, rarity, imitability, and organization) agent identifies a proprietary metallurgical process and aerospace relationships as genuinely rare and difficult to imitate but notes that capital expenditure is split roughly equally across all three segments. The resource advantage is real but the company is not organized to exploit it, because investment is spread evenly across all three segments rather than concentrated behind the aerospace business where the advantage actually lies.

A Richard Rumelt agent argues that the stated strategy is not a strategy at all: “Grow through diversification across end markets” is called out as a goal dressed up as a direction.

A Roger Martin agent maps the “where to play and how to win” decisions and finds them incoherent. The capabilities required to compete in aerospace directly contradict those required in automotive, and the company is attempting both.

No single framework produces a diagnosis. The friction between them surfaces a question nobody has been asking: whether the company should narrow its scope to the one segment where its genuine competitive advantage meets structurally attractive conditions.

Conducting this analysis by prompting a large language model to analyze a situation through Porter, then VRIO, then Rumelt, then Martin will generate real tensions worth exploring. The constraint with such prompting is that the consultant holds the thread. Each framework shift requires a new prompt, and the synthesis depends on the professional’s ability to carry all four interpretations forward simultaneously.

Agentic architecture changes what is possible. Each specialist agent works with the same evidence base at the same depth, so the contradictions are directly comparable. The orchestration agent reviews all four analyses and identifies where the frameworks agree, confirming what is robust (and where they clash) and pointing to what is unresolved. The professional’s role shifts from maintaining the analysis to evaluating what the system surfaces.

2. Surface silences. Some of what an organization knows about itself never makes it into a meeting, a report, or a strategic plan. Surfacing silences means systematically comparing what exists in the data with what appears in the discourse that surrounds it, and treating the gap as analytically meaningful. The insight lives in what is absent.

Consider a strategy consultant engaged with a specialized practice group within a larger professional services firm. The practice does premium, expertise-intensive work in a technical niche that demands deep domain knowledge and long-standing client relationships. The practice leader has never lost a competitive bid, personally oversees every significant engagement, and turns away more work than the practice accepts. The growth narrative is compelling: strong demand and few competitors at the same quality tier.

The consultant configures an agent with the full engagement data set, including transcripts from interviews with the practice leader and company executives, the practice’s strategic plans, market analyses, competitive intelligence, and financial records. The AI agent can systematically inventory themes across both the interview data and the formal strategic documents. Its orientation: Pay attention to what appears in one body of material but not the other.

The AI agent surfaces a silence: organizational dependency on a single individual. The practice leader, a senior expert with two decades of experience, appears as an implicit presence across virtually every topic in the interview transcripts. Growth capacity, client relationships, quality standards, pricing, talent development: Roughly a third of all substantive discussions reference his judgment, relationships, or standards, yet he is never explicitly named. Strategic plans discuss market opportunity, competitive positioning, and investment requirements, but not one of them names what happens when every advantage the practice holds is embodied in one person.

The agent also flags other potential gaps. An apparent absence of succession planning turns out to be addressed in firm-level documents outside the agent’s data set; silence around competitive risk reflects a data gap much more than an organizational one. The consultant investigates and sets both findings aside to focus on the one that holds up: the silence around talent dependency.

Everyone in the company knows that the practice leader is exceptional. But knowing it and naming it as the central strategic constraint are different things. The strategic conversation is organized around market opportunity, but the operational reality is organized around one person. This changes the question from “Can this practice grow?” to “Can it grow without first solving the problem nobody has put on the table?”

Prompting an AI to compare interview themes against strategic plan priorities will flag disconnects, and a skilled professional can push toward what seems absent. However, after weeks or years of seeing things in a particular way, the professional’s perspective becomes constrained in that way, which also shapes how they prompt AI.

The AI agent does not share that immersion in a particular context. It compares the full interview corpus against the full set of strategic documents, without having absorbed the team’s framing. And because the comparison is exhaustive rather than selective, the pattern’s pervasiveness is verifiable rather than impressionistic. The AI can more easily notice what familiarity has made invisible.

3. Bridge levels. When something goes wrong in an organization, the explanation is usually found at the same level where the symptom appeared. For instance, a portfolio problem gets a portfolio explanation, or an operational failure gets an operational diagnosis. But causes don’t always live where symptoms surface. Bridging levels means tracing causal chains across scales of analysis, connecting micro decisions to macro outcomes, and linking macro patterns to the specific behaviors that produce them. The insight comes from traversing the levels that are normally examined in isolation.

Consider a strategy consulting team that is working with a diversified industrial corporation that comprises three divisions and is seeing a declining return on invested capital (ROIC). The corporate narrative is a familiar one: The company is facing market headwinds and competitive pressure. The quarterly review focuses on portfolio-level metrics. Everyone nods.

The lead consultant configures an AI agent with data from three levels of the business (corporate, divisional, and operational) simultaneously, including corporate portfolio metrics and capital allocation records, divisional P&Ls and competitive positions, and operational data within each division. The AI can trace statistical relationships across all three levels. Its orientation: Look for places where a cause and its symptoms sit at different levels.

The agent traces the ROIC decline downward. It is concentrated in Division A, but not because Division A is underperforming within its market. A capital allocation formula implemented three years ago weights recent revenue growth when distributing investment. Division A is the fastest-growing division, but it is also growing in a commoditizing market with declining margins. Division B, slower-growing but carrying the strongest competitive position and the highest margins in the portfolio, is being systematically starved of capital. Its competitive edge is eroding quarter by quarter.

When the ROIC is traced upward, it becomes clear that the capital allocation formula was designed to invest behind growth. At the corporate level, this sounds entirely rational. But at the divisional level, it overinvests in deteriorating competitive dynamics and underinvests in defensible advantage. The ROIC decline the CEO has been attributing to the market is actually being produced by a corporate policy operating exactly as designed.

So here, the symptom lived at the portfolio level but the cause lived in a corporate policy. And the intervention — redesigning how capital is allocated — sat at a level that neither divisional management nor the CEO’s market narrative was examining.

Prompting an AI to explain how corporate capital allocation might be affecting divisional performance, or whether investment patterns align with competitive positioning, will surface plausible hypotheses worth investigating. The constraint is that the prompter has to suspect the connection before they can ask the question.

The agent doesn’t need that prior suspicion. With access to all three levels at once, it can follow connections the consultant didn’t know to look for, because it was configured to work across the levels rather than to test a hypothesis already in hand. The diagnosis was there in the data all along; it was simply invisible from any single level. And the finding can feed the next step: A stress-testing agent can check whether the formula’s measure of revenue growth actually tracks competitive strength in each division or whether that category is itself part of the problem.

4. Stress-test categories. Every organization runs on categories: the classification systems that sort customers, failures, costs, and behaviors into named buckets that route decisions to the right people. The problem is that categories are designed to reflect how an organization thinks, not necessarily how the world behaves. Stress-testing categories means comparing your classification system against the operational reality it was built to describe, and treating the divergence as the finding.

Consider a data analyst at a ready-mix concrete company investigating a persistently high rejected-load rate, where trucks arrive at job sites and are turned away. The company classifies rejections into five categories: wrong mix design, late delivery, quality failure, customer change, and over-order. Each category connects cleanly to a specific department. Wrong mix and over-order go to sales, late delivery goes to dispatch, quality failure goes to the plant, and customer change is marked uncontrollable.

The analyst configures an agent with the formal rejection classifications alongside the full operational record, including order tickets, dispatch logs, GPS tracking data, batch plant records, driver comments, weather data, and customer communications. Its explicit job is to run a pattern analysis across the entire rejection data set and attend to where the formal categories and the actual causal structure diverge.

The agent finds that the categories are both imprecise and actively misleading. Many late-delivery rejections cluster on specific days and weather conditions. Traced backward, they are downstream effects of a batch plant aggregate hopper that slows under adverse weather conditions, delaying loads that then arrive outside the pour window. Categorizing the rejection as late sends the investigation to dispatch, but the cause lives in the plant.

A cluster of customer-change rejections follows a different pattern. They’re concentrated in jobs where a general contractor placed the order but a subcontractor controlled the pour schedule. The label marks those rejections as uncontrollable when they are, in fact, a predictable coordination gap that the company could address. And then there is a pattern for which the formal system has no category at all. Driver comment fields across hundreds of loads record the same informal notation: Site not ready. The truck arrived, but the job site could not receive the pour. Drivers waited, returned, or were rerouted, and dispatchers coded the rejection into whatever category seemed fitting. Here, a recurring root cause existed only in the margins of the data because the classification system was never built to see it.

Pulling a sample of rejection records and asking an AI tool whether the categories are capturing real causes will surface plausible candidate causes and flag obvious mismatches. The constraint is sample size and selection: What the analyst pulls inevitably reflects what already seems worth investigating.

The agent, however, tests every category against the full operational record, across thousands of loads, with no prior assumption about which categories are accurate. It zeros in on weather patterns because it examined everything rather than only a representative slice. It surfaces the “site not ready” pattern because it has no deference to an existing taxonomy. Once those mismatches are surfaced, the broader agentic system can pursue them. A bridging agent could trace the “site not ready” pattern upward from driver comments to dispatch costs to fleet utilization to portfolio-level financial impact, quantifying a problem the formal system had rendered invisible.

How to Direct Intelligence Skillfully

Gaining the most useful results from directing AI agents to surface new insights requires practice, like any new skill. The following are some guidelines to keep in mind.

Configure for discovery, not answers. Start with how you set the system up. The instinct, especially for professionals trained to specify deliverables clearly, is to tell an agent what to find. Resist that instinct. An agent configured to confirm a competitive advantage will confirm it and, in doing so, won’t discover that the advantage is real but being systematically diluted by incoherent investment. A system of agents configured to hold four competing strategic frameworks in tension will produce something harder to digest and considerably more valuable: a contradiction that points at a question nobody was asking. This is problem-setting rather than problem-solving. Humans define what agents should pay attention to and not what they should conclude.

Treat the unexpected as signal, not error. What you do when agents produce something you didn’t expect matters as much as how you configured them. The natural response is to treat the unexpected as error. In efficiency mode, divergence from the anticipated output is waste, and the job is to correct that divergence. In discovery mode, that instinct runs exactly backward. An unexpected output is the most valuable signal the system can generate, because it tells you something about your own assumptions or about the actual structure of the problem you thought you understood. The “site not ready” pattern had existed in the driver comment field for years. The talent dependency permeated a third of the interviews conducted with employees, yet it appeared in none of the professional services firm’s strategic plans. In both cases, the discipline for anyone directing intelligence is to resist the urge to explain that kind of surprise away before investigating what it points to.

Evaluate proposals, not conclusions. The discipline to investigate rather than reject the unexpected carries directly into how you interpret what agents produce. Patterns surfaced by an agentic system are proposals, not findings. They open inquiry rather than close it. When the bridging agent traced ROIC decline to the capital allocation formula, the right response was to test that pattern against alternative explanations and assess whether the effect was large enough to matter. When the Porter and VRIO agents contradicted each other on the strategic situation, the productive move was not to decide which agent was right but to recognize that the contradiction was pointing at a question the company had never confronted. And when the “site not ready” pattern came through, it required verification against GPS time stamps and driver logs before it became actionable. Machine-generated patterns become knowledge through the professional’s judgment, not in spite of it.

Track what you advance, and what you reject. Over time, tracking which proposals you pursue and which you set aside builds something useful: a record of your own analytical instincts, which moves generate value in which contexts, and where your judgment tends to foreclose inquiry before it should. Your rejections reveal as much as your discoveries.

The agentic systems described in this article already involve orchestration: specialist agents whose outputs are synthesized, compared, and set in tension through an orchestration layer that the professional designs. As these systems mature, the orchestration will deepen. A silence-surfacing agent could autonomously trigger a lens-multiplying agent to investigate what it finds; a bridging agent could hand a pattern to a category-testing agent to probe for boundary cases, without the professional initiating each step. The professional’s role could shift from designing individual configurations to shaping the conditions under which agentic systems discover productively. Such moves will compound in ways that are difficult to fully anticipate now.

What won’t change is the underlying skill. Directing intelligence draws on capacities that have always distinguished exceptional professionals: the ability to frame a problem so that new things become visible, and to hold contradictions open long enough to learn from them. Agentic AI does not replace those capacities. It gives professionals a more powerful medium through which to exercise them. The professionals who thrive will not be the ones who automate the most. They will be the ones who understand that discovery is the highest-value thing a professional produces, and who have the judgment to direct intelligence toward it deliberately.

Since early 2025, organizations seeking to curry favor with the current U.S. administration have disavowed earlier commitments to workforce diversity, equity, and inclusion (DEI) and proclaimed that their hiring practices are therefore meritocratic. But framing DEI and merit as antithetical dichotomies, as various executive orders have done, does not mean that abandoning the former results in the latter.

Good DEI practice is designed to broaden the pool from which companies seek to recruit top talent rather than, say, limit their searches to members of reliable old boy networks. Good DEI practice does not replace hiring criteria like demonstrated talent and achievement with demographic criteria. And supposedly meritocratic hiring, in practice, does little to ensure that individuals are selected based on their likelihood of success on the job. Indeed, unbeknownst to many proponents of meritocracy, merit-based practices can be both elusive and illusory.

Merit is a compelling principle. However, many so-called meritocratic practices are fraught with error, even if done with the best intentions. Meritocrats often advocate for practices that they genuinely believe are merit-based but are not — what I call the merit mirage. They may also disparage practices that they believe are not meritorious but are (that is, they demonstrate merit skepticism). Understanding where many attempts at meritocracy go wrong will help leaders better navigate the alleged conflict between DEI and merit.

What Validity Means in Hiring, and Why It Matters

What laypeople call “merit” is referred to as validity by industrial-organizational psychologists who are experts in the technical science behind employment selection. Validity means that the selection practice is job-related: It cannot be assumed; it must be demonstrated according to accepted validation methods. A truly valid hiring process can select higher-performing employees. In contrast, face validity refers to hiring practices that appear valid (or job-related) to the layperson but provide little direct benefit to the organization beyond the appearance of fairness.

Some assessments that lack empirical proof of their validity have face validity and might be passionately supported by individuals who believe in merit due to the merit mirage. Many managers who are too busy to properly vet these types of measures fall prey to the use of such assessments. In addition, applicants who perform well on assessments that have little predictive value truly see the outcome as evidence of their merit, even though such measures are not valid. These types of assessments often advantage majority groups.

Other examples of the merit mirage include the following practices:

• Using educational requirements that are assumed, but have not proved, to predict job performance.
• Relying on “years of experience” requirements but failing to recognize that quantity is not the same as quality, or that after a certain number of years, there are typically significant diminishing returns on years of experience.
• Assessing qualities that are not job-related or are relatively unimportant. Common examples include overvaluing first impressions, nonverbal communication skills, and professional dress. Polished interview skills often do not translate to strong job performance.
• Confusing rigor for relevance. A challenging assessment process is no substitute for one that is more relevant and more comprehensive. Holistic assessment is often more effective than arduous assessment.
• Evaluating group characteristics instead of individual characteristics, such as identity-based stereotypes (including positive ones), or selecting based on the reputations of previous employers or educational institutions attended. Individual merit must be based on individual qualities.
• Using subjective assessments of motives or level of motivation. This is easy to fake; further, motivation to get a job is often incorrectly assumed to predict motivation to perform a job.
• Assuming that a selection practice that is frequently used by others or has shown to be valid for other jobs or organizations is valid for the job you are using it for.
• Incorrect assumptions about what is being measured. A common example is the use of credit checks. One typical belief is that people who have good credit are responsible and those who have bad credit are irresponsible or are more likely to engage in unethical behavior. There is no empirical justification for either assumption. Valid measures of conscientiousness and integrity would better assess responsibility and ethics, respectively, and would be more meritorious compared with credit checks.

The Merit Mirage in Practice

To achieve true merit-based hiring, it is important to use assessments that have proved to be valid/job-related, standardized, and reliable. However, this requires a level of human resource management expertise that most hiring managers do not possess; in fact, even many HR professionals err.

For example, a large organization developed and validated a rigorous and thorough exam for entry-level selection. However, when it started using the exam for making selection decisions, it did so in a manner that differed from how the exam was used in the validation study; the assumption was that it would make no difference because the exam itself had not been modified. After using the exam for 12 years, the organization finally investigated its validity as used and found that it was not valid, because the correlation with job success was not statistically significant. It had wasted more than $17 million administering this rigorous but invalid exam to thousands of applicants. The organization (and thousands of selected applicants) thought selections were based on merit, but they were essentially random — it was a merit mirage.

Perhaps the biggest cause of the merit mirage is a stubborn reliance on intuition and subjectivity, and the incessant use of haphazard selection procedures. For more than four decades, there have been significant advancements in developing selection procedures that can achieve both merit and diversity. Yet these tools are not widely used. One reason is lack of awareness of the tools or how to properly use them. However, another reason is that many recruiters and hiring managers prefer to rely on their own discretion and intuition. They rely on the myth of selection expertise, believing that their knack for identifying merit is more effective than standardized assessments, despite widely available research to the contrary. For example, many interviewers (whether managers or HR professionals) understand that unstructured interviews are ineffective and that structured interviews (which are more valid, reliable, standardized, legally defensible, and less prone to bias) are far more effective. Yet, paradoxically, they are relieved to know that semi-structured interviews (which are moderately effective and allow for some discretion) are another option, and they are strongly inclined to use this option even though it is less effective compared with structured interviews.

The Roots of Merit Skepticism

Other selection methods may be shown, through scientific empirical studies, to possess validity and yet may lack face validity and thus appear invalid to the layperson. It can be difficult to convince managers to adopt these types of measures or to communicate their value to applicants who perform poorly on them. These measures help organizations hire the most qualified applicants, even if such approaches may be opaque and even upsetting to applicants, or viewed with skepticism by managers. These types of assessments typically lessen the majority group’s advantage (which can be misinterpreted as favoring the minority group) and often result in demographic parity.

Merit skepticism often seeks to dismiss personality assessments and biographical questionnaires that surface candidates’ life experiences — but some organizations instead choose to embrace them. For example, a large global company that is very successful, has a strong culture, and is ranked highly on several “best places to work” lists, relies, in part, on personality, values, and strengths assessments in its entry-level selection process. Applicants who are not hired frequently groan about the long selection process and complain about the portions of it that they perceive to be irrelevant or unrelated to the job or to pertinent experience and education. Even though the selection process lacks face validity to applicants, it is valid and instrumental to the organization’s effectiveness. It is consistent with merit principles, even if applicants do not perceive it to be.

Many HR professionals and corporations recognize the value of personality and biographical measures for employment selection because such measures have a long history and robust empirical support. However, the U.S. government has had challenges embracing them. In 2014, the Federal Aviation Administration began using a biographical questionnaire for air traffic controller hiring, but that led to a class-action lawsuit. The validity of the measure is yet to be determined because the case is still pending, but it is notable that most of the critics of the biographical questionnaire attacked it based on its lack of face validity (because laypeople could not see a rational relationship between the items and job performance) rather than its validity (or lack thereof).

More recently, in September 2025, the U.S. Army ended its use of the Command Assessment Program. This was a rigorous, holistic approach to making promotion decisions and included a variety of assessments, including psychological measures. Secretary of Defense Pete Hegseth terminated the program, calling it a failed “woke” experiment. He declared that promotions will now be based only on merit and performance, and the Army reverted to the Centralized Selection List process for making such decisions. That process was first adopted in 1975 and consists of a review of personnel files that lasts only a matter of minutes. In sum, merit skepticism can result in lawsuits and less meritorious solutions.

It’s critical for leaders to promote the use of proper validation methods so that their organization’s candidate selection practices yield genuine merit rather than the merit mirage. Such a rigorous approach may give companies a stronger position to defend against charges of discrimination than flawed merit or diversity initiatives. It requires investment in developing greater HR management expertise so that, for example, assessments can be thoroughly vetted for validity. Perhaps, more importantly, managers need to possess (1) the humility to recognize that their discretion is likely flawed, (2) the openness to use alternative methods, and (3) the wisdom to rely on empirically supported practices.

Many of the selection methods that achieve true merit (such as valid structured interviews, reviews of work samples and biographical data, and some assessments of integrity, and some personality traits) also yield diversity. Thus, it is possible to both maintain DEI commitments and hire based on merit: What some present as two opposite goals can in fact coexist.

Marketing capabilities — the complex bundles of skills, processes, and organizational know-how that enable companies to implement customer-related activities and adapt to marketplace changes — are rated by marketing professionals as important to business success. At the same time, artificial intelligence is rewriting the rules of content creation, customer targeting, and performance measurement. Responsibilities such as managing generative engine optimization (GEO) are emerging as vital online capabilities that did not exist even two years ago.

Eighteen years ago, one of us (Christine) launched The CMO Survey, polling marketing executives about the state of the industry and their organizations. In January 2026, we conducted the 35th edition of the survey, which received 308 responses from marketing leaders at for-profit U.S. companies. The results are troubling.

It’s our assessment that two critical things are happening right now. The first is that the requirements of effective marketing are shifting faster than at any point in The CMO Survey’s history, led by the need to figure out where and how to incorporate AI capabilities. The second is that the state of the marketing profession is not ready for this moment. Instead, marketing teams are systematically undermining their own ability to build the capabilities important to their success.

This is not a minor inconsistency. We see a pattern — visible across budgets, hiring, organizational behavior, and strategic priorities — that raises fundamental questions about how companies today are making decisions about investing in marketing capabilities. Our prediction is that this soft commitment to what we believe is critical marketing know-how will cost organizations competitively if it is allowed to continue as it is today.

Underinvestment Looms Large

Nearly 60% of marketing leaders reported that their primary approach to building marketing capabilities is through internal training and hiring rather than via external partnerships or acquiring other companies. This preference hasn’t changed since we last asked about it in 2020, despite six years of dramatic shifts in what successful marketing requires. And despite this reliance on internal resources — and surveyed leaders’ belief that marketing capabilities are important to business success — getting financial support for capability building remains a challenge.

In particular, when we look at what companies are actually doing to support that stated commitment to training and hiring, the details tell a story of underinvestment. Training and development budgets have declined steadily for years and now stand at just 3.8% of marketing spend — down from a pre-pandemic high of 5.8% in 2019. Marketing head count growth has dropped sharply, falling more than 50% from last year’s rate. And when asked what capability is most lacking in their organizations, the most common response from marketing leaders was not a skill deficit but inadequate resources: not enough people, time, or budget to make existing capabilities function effectively.

Strategic intent and resource allocation point in opposite directions. Companies say that they build their capabilities through people, but they are systematically reducing or slowing their investment in those same people. We call this disconnect the marketing capability paradox.

A closer look at agility and skills investment illustrates this contradiction. Seventy-one percent of marketing leaders in our survey said that agility is key to their organization’s success. Marketers reported performing reasonably well at this, able to quickly revise priorities and shift resources in response to change. But at the same time, they reported that their weakest-rated activity across all agility dimensions is “building the capabilities that facilitate agile marketing actions.”

Companies see themselves as good at reacting to change, but they are weaker at building the organizational foundation that would make those reactions less costly and more effective. This distinction — between responding to the present and investing in the future — is a troubling signal that runs through every dimension of how marketing capabilities are managed in organizations.

Seven Barriers to Capability Building

Cuts in training and declines in head count are the most visible symptoms of this paradox, but other data hints at larger structural challenges. From our survey results, we identified seven interconnected forces that are working against capability development. Understanding them together reveals why this problem is so resistant to easy solutions.

There is a serious gap between the adoption of technology and marketing teams’ preparedness for using it. Technology adoption is outrunning organizational readiness. Companies are investing in technology faster than they are building the human capacity to effectively use it, which means each new tool widens the capability deficit rather than closing it. One survey respondent succinctly described their biggest barrier to maximizing the impact of marketing technology as “attracting the right talent, retaining them, and keeping them up to speed on changes happening.”

Asked to rate elements of their marketing technology activity on a 7-point performance scale (where 1 is going “poorly” and 7 is doing “very well”), survey respondents didn’t give even one activity a score above 5. Performance levels have not improved over the past two years, even as marketing departments have scaled up their deployment of AI tools. The two lowest-rated activities — hiring employees to manage marketing technologies (3.7) and training employees on emerging marketing technologies (3.9) — are the very ingredients needed to close the gap between deployment and effective use of technology.

This is not a minor issue. AI use in marketing activities has nearly doubled since 2024, from 13.1% to 24.2% today. Respondents projected that AI will account for more than 50% of all marketing activities within three years as it increasingly delivers measurable improvements in sales productivity, customer satisfaction, and marketing overhead costs. Gains from AI are rising year over year, but the people power to keep pace is not.

Too many marketers have a structural orientation toward the present. Every year since 2019, marketers have reported in our surveys that they devote roughly 68% of their time to managing the present and 32% to preparing for the future. This ratio has held constant across the COVID-19 years, the digital transformation era, and now the AI revolution. This tells us that the ratio is not a situational response to any particular economic pressure but a structural orientation that is remarkably resistant to change.

But capability building is inherently a future-oriented investment. It requires sustained attention, a multiyear commitment, and a willingness to accept near-term costs for long-run returns. In an environment where organizations have consistently prioritized the present over the future for the past seven years, that kind of investment cannot take root. As one respondent noted, “There is little time for future thinking. We have a strategy, but our actions are tactical and short term, making it difficult to prove effectiveness.”

Current economic pressures are intensifying a condition that predates them and have pushed 70.6% of marketing leaders toward short-term impact over long-run gains and 26.8% toward explicitly emphasizing spending over building capabilities, according to our survey data.

Marketers aren’t highlighting strong data around impact and retention. Our survey found that marketing’s impact on customers is growing more durable, meaning that payoffs are being felt further out in time than previously was the case. The median duration of marketing’s impact has lengthened from several months in 2022 to six months in 2026, with a meaningful shift toward effects lasting one year or longer.

In addition, customer retention, the metric most directly linked to the depth of marketing capabilities, is growing at 12.8%, well above the growth of customer acquisition, at 7.4%.

In spite of this, marketers continue to focus on short-term impact. This is a missed opportunity: The cumulative value of sustained marketing investment may be greater than short-term measurement approaches currently capture.

Ties with the C-suite are weak. Building and sustaining marketing capabilities requires organizational buy-in and support that extends beyond the marketing function. Yet the partnership between marketing leaders and CFOs, measured on the 1-7 scale, stands at just 4.5 for building a business case for marketing spending — barely a change from 4.3 in 2021. Without a strong CFO relationship, marketing leaders will find it difficult to make a credible internal case for capability investment. This is key to why training budgets and head count suffer.

The result is a vicious cycle. A weak CFO partnership leads to underinvestment in capabilities, which weakens marketing’s ability to demonstrate value, which further erodes the CFO relationship. This deficit also shows up in weak collaboration with technology leaders: Only 55.7% of marketing leaders reported collaborating with their CIO or CTO on digital activities. That leaves the rest without the technical partnership that adopting AI tools increasingly demands, especially if they’re to be adopted at scale.

A rigid build-versus-partner mindset stifles development. AI-related capability gaps spanning analytics, generative AI, GEO, and demand generation were cited by a combined 35.7% of marketing leaders as their most pressing unmet needs. These are precisely the areas where external partnerships and acquisitions might accelerate capability building more effectively than internal development. Yet the build preference persists, even as the resources available to execute it continue to decline.

It’s genuinely surprising that despite six years of dramatic change in what marketing capabilities are required (particularly the growing centrality of AI, analytics, and technology skills), how companies approach capability development has remained unchanged since we started conducting our survey in 2020. The overwhelming preference for building capabilities internally through training and hiring — the choice of nearly 60% of our surveyed marketing leaders, as mentioned earlier — was a reasonable strategy when capability requirements were relatively stable. It is a much more problematic strategy when the technology capability landscape is shifting as rapidly as it is today.

The foundation is too weak to sustain new initiatives. Perhaps the most telling finding in this year’s survey is that the most cited capability gap is the inadequate resourcing of existing capabilities — not enough people, time, or budget to make what companies already have function effectively. Companies are not failing to build the right capabilities; they are failing to sustain the ones they already have. The implications are significant: Even if companies were to invest in the right new capabilities, they would be building on a foundation that is already crumbling.

Marketing’s essential purpose has a framing problem. How marketing leaders think about the value of skill building may be contributing to their underinvestment. Nearly 4 out of 5 surveyed marketers (78.2%) said capabilities matter primarily because they deliver higher ROI for every marketing dollar spent. Fewer named reasons that are more strategically durable, such as increasing the effectiveness of managing customers (cited by 52.1%), making it more difficult for competitors to imitate them (37.0%), or attracting and retaining top talent (33.3%). When capabilities are justified primarily on short-term ROI grounds, they will always lose the budget argument to investments that demonstrate returns more quickly and more visibly.

The deeper strategic case for investing in marketing capabilities, including the argument that they build organizational resilience, create competitive barriers, and compound in value over time, is largely absent from how marketing leaders think about capabilities and communicate them to others. This framing problem affects more than the conversations marketers have with CFOs. It shapes how marketing capabilities are more broadly prioritized internally, how they are resourced, and how quickly they are cut when financial pressure mounts. Leaders who cannot articulate why capabilities matter beyond their immediate financial return will find it difficult to protect them.

What Companies Should Do

What makes this situation particularly concerning is that the seven forces are not independent. They reinforce one another in ways that make the capability deficit self-perpetuating. Short-termism reduces the organizational appetite for capability investment. Reduced capability investment weakens marketing’s ability to demonstrate value. A weakened ability to demonstrate value increases pressure from CEOs, boards, and CFOs — which drives more short-termism. Meanwhile, technology adoption accelerates the demand for corresponding capabilities; the CFO partnership remains too weak to fund a response; the build strategy persists even as its resources erode; and the framing of capabilities as an ROI tool rather than a strategic asset ensures that they will always be outcompeted for budget by investments with more immediate and visible returns.

Our survey data suggests three priorities for marketing leaders and their organizations.

First, executives across the C-suite should wake up to the need to decouple capability investment from short-term financial pressure. Marketing training budgets and staffing are currently treated as variable costs that can be cut when profits disappoint. Indeed, our surveyed executives reported that marketing expenses are cut 45.4% of the time — more frequently than other areas of the company — when executives face profit shortfalls. But treating capability investment like discretionary spending mischaracterizes its nature entirely. Capability investment is analogous to R&D: Cutting it saves money in the short run while compounding the capability deficit in ways that are costly to reverse. Companies that protect capability investment through economic cycles will be better positioned when conditions improve.

Second and relatedly, marketing leaders need to reframe the case for capability investment. Marketing leaders who justify it primarily on ROI grounds are making their own investment case harder to win. The more compelling and durable argument connects capability investment to competitive advantage, organizational resilience, and the ability to attract and retain the talent that the AI era requires.

Third, markers must revisit the build-versus-partner assumption. The stability of the build preference across six years of surveying in an age of dramatic change in marketing’s requirements is striking. For AI-related capabilities in particular — where the speed of change and the specialized knowledge required make internal development especially challenging — a more aggressive partnering strategy is likely to be more effective. This does not mean abandoning the build approach, but it does mean subjecting it to the same scrutiny applied to any other strategic assumption that has not been tested in years.

The capability paradox is not just an operational problem but also a strategic one. Companies are underinvesting in the very capabilities that the evidence suggests are generating their most durable and valuable results. In a competitive environment being reshaped by AI, where the capability gap between leaders and laggards will widen rapidly, that is a costly mistake to make and a difficult one to reverse.

Over the past few decades, there has been burgeoning interest in how leaders and organizations can support employees in the aftermath of traumatic events.1 But there is another pervasive and often hidden source of trauma that originates well before individuals enter the workforce. Childhood adversity, referred to as adverse childhood experiences (ACEs), is widespread and affects the well-being of a large portion of the workforce.

Recent estimates suggest that 60% of adults worldwide have faced one ACE, and 16% have faced four or more.2 ACEs shape employees’ self-perceptions, job attitudes, behaviors, and workplace relationships, typically in ways that undermine their well-being and functioning. In light of this, we are sharing a new, evidence-based toolkit for organizations and leaders seeking to design healthier work environments that can attenuate the enduring effects of childhood adversity for their employees.

How Childhood Adversity Can Resonate in the Workplace

ACEs encompass a range of early-life hardships. They include interpersonal problems, such as abuse or neglect; household dysfunction, such as divorce, domestic violence, criminality, or substance abuse; the death of or separation from family members; and witnessing or being the victim of community violence. ACEs also include physical or mental health issues in the home and economic adversity. In short, ACEs cause both direct and indirect harm to children. Such situations are typically experienced as harsh, unpredictable, dysregulating, and shaming, and are characterized by a lack of agency and control.

ACEs lead to psychological and physiological changes that persist into adulthood. They include blunted cortisol responses to stress, or the body is not releasing cortisol as needed; heightened reactivity of the amygdala, which leads people to perceive threats where there are none; and diminished hippocampal gray matter volume, which is linked with memory impairment, cognitive and emotional difficulties, and mood disorders.3

The workplace implications of these changes mean that ACE survivors are more likely to experience low self-worth, risk aversion, turnover intentions, emotional distress, job strain, low job performance, and dismissal and are often the targets or perpetrators of workplace mistreatment. These outcomes can leave adults feeling chained to their traumatic pasts.4 As American author William Faulkner wrote, “The past is never dead. It’s not even past.”

Despite the potential for poor outcomes for ACE survivors, neuroscience research has found that even adult brains can change as people learn and grow — a phenomenon known as neuroplasticity.5 While scientists once thought that the brain was largely fixed after childhood, they have since determined that the adult brain remains dynamically adaptable and is capable of forming new neural connections, strengthening or weakening existing pathways, and reorganizing functional networks. The brain can update prior learning through repeated, meaningful adult experiences that counter earlier, adverse experiences. Importantly, this means that ACE survivors’ professional experiences can themselves offer a platform for recovery. How work experiences might facilitate neuroplasticity in the brain to help people overcome the deleterious effects of ACEs is a key question we hope to answer for leaders concerned with employee well-being.

Critically, whether neuroplasticity occurs depends on the quality of an experience. It is most strongly promoted by situations that are emotionally meaningful, have a social/relational component, and repeatedly reinforce new learning, thinking, or habits. Research also shows that predictable routines, supportive social relationships, opportunities for autonomy and mastery, and experiences that reduce chronic stress all facilitate adaptive neural change. They do so by dampening hyperreactive threat responses, strengthening regulatory and executive functioning, and restoring the healthy neuroendocrine functioning needed to regulate stress and mood.

Over time, these healthy conditions enable individuals to replace survival-based responses learned in adverse and threatening environments with more flexible, regulated, and agentic patterns of thinking, feeling, and behaving. Neuroplasticity is thus a practical mechanism through which the workplace, often one of the most stable and recurrent environments in adulthood, can meaningfully contribute to recovery, resilience, and long-term well-being among ACE survivors.

It is important to acknowledge that claims about neuroplasticity are sometimes met with skepticism, particularly when they are interpreted as suggesting that the brain can be easily or rapidly “rewired” or that early adversity can be fully undone through later experiences. The evidence does not support such a simplistic view. Rather, neuroplasticity reflects a gradual, experience-dependent process that unfolds over time, varies considerably across individuals, and is shaped by factors such as chronic stress, developmental timing, and the consistency of environmental input. Importantly, plasticity does not imply erasure of earlier learning but, rather, the capacity for new neural pathways and regulatory patterns to emerge alongside the influence of earlier adaptations — and, in some cases, attenuate them. Framing neuroplasticity in this way situates it not as a promise of quick transformation but as a scientifically grounded mechanism through which sustained, supportive experiences, including those in the workplace, can incrementally support adaptive functioning among ACE survivors.6

How Healthy Workplace Experiences Lead to Positive Change

Neuroplasticity is facilitated by experiences that contradict what ACEs have led survivors’ brains and nervous systems to expect. Benevolence and stability can shift the core belief that others, especially authority figures, are harsh and unpredictable. Relationships through which survivors learn to better manage their emotions and behavior, and through which they receive validation of their feelings, can counter emotional dysregulation and shame. Those who have felt helpless and unable to act can gain confidence by being empowered to claim agency and control through their work.

Importantly, the kinds of experiences that may be seen as therapeutically corrective are recognizable hallmarks of organizational cultures that many healthy companies now strive to foster. What promotes healing and recovery for survivors of ACEs is also increasingly seen as promoting employee well-being broadly, leading to greater engagement and higher performance. Leaders and organizations play a crucial role in all this. In what follows, we’ll explain specifically how and why such positive workplace experiences can be helpful.

Reduce anxiety, fear, and defensiveness. Children who are targets of or witnesses to violence or conflict in the home may have caretakers who lack healthy emotional regulation and self-control, and they may be punished when they voice their own emotions.

Experiences with this type of caretaker as a child can foster many negative outcomes in adulthood, including a greater likelihood of being the victim or perpetrator of workplace violence or physical abuse. Survivors may lack the skills needed to process feedback from, or even simply disagree with, others in their organizations. Many adults with histories of childhood trauma struggle to regulate complex thoughts and emotions, especially anger or hurt, that were not safely processed earlier in life. As a result, they may respond defensively, reactively, or with hostility when they do not understand others’ decisions or opinions. Feedback — even when it is constructive, helpful, and/or warranted — can be threatening and difficult to process.7

Such responses originate in changes to the brain caused by repeated exposure to volatility, conflict, or punishment. The neural systems responsible for detecting threats, particularly the amygdala, are sensitized, while regulatory systems in the prefrontal cortex that support reflection, impulse control, and emotional regulation are weakened. In parallel, chronic activation of the body’s stress response system alters cortisol signaling in ways that bias the brain toward hypervigilance or shutdown. Over time, these adaptations can become default responses, such that even neutral or mildly challenging interpersonal interactions in adulthood are unconsciously experienced as dangerous, triggering fight, flight, or freeze reactions rather than thoughtful engagement.8

When adults later encounter environments that are regularly calm, stable, and emotionally consistent, these same neural systems are exposed to a markedly different set of inputs. Repeated experiences of kindness and clarity, such as measured feedback, transparent expectations, and respectful disagreement, reduce the frequency with which the brain’s alarm system is activated, allowing prefrontal regulatory regions to exert greater influence over emotional and behavioral responses. Over time, this shift facilitates neuroplastic change: Stress-related circuits become less dominant, while neural pathways associated with safety, trust, and cognitive control are strengthened. Importantly, these updates occur not through isolated moments of support but through sustained patterns of predictable and compassionate interaction that enable the brain to revise earlier threat-based learning.9 Thus, these relationships between colleagues at work are critical to helping ACE survivors learn to more effectively process interpersonal interactions, particularly those involving criticisms or conflict.

Foster self-worth and healthy boundary-setting. Children who grow up with emotional abuse or neglect often lack support for developing healthy boundaries and blame themselves for what they have suffered, which erodes their sense of self-worth. When distress is ignored, punished, or met with criticism, neural circuits responsible for soothing arousal and integrating emotional experiences are underdeveloped or inconsistently engaged. As a result, emotional states may feel overwhelming, confusing, or threatening in adulthood, often triggering excessive self-blame, heightened sensitivity to feedback, or compulsive efforts to seek reassurance.10

In adulthood, this can give rise to a constant need for external validation from others in the workplace, such as managers, coworkers, or customers, and individuals’ inability to separate their self-worth and self-esteem from their job. Often, these employees have trouble setting boundaries or saying no at work, which can lead them to take on more than they can shoulder. In turn, they are left feeling overwhelmed or experience sudden burnout.11 Their sense of self is fragile, and they tend to place the needs of others above their own to an unhealthy degree.

A different pattern emerges when individuals repeatedly experience co-regulation and healthy validation in relational contexts, including at work. When colleagues or leaders respond to distress by acknowledging emotions without amplifying or dismissing them, the individual’s brain is afforded repeated opportunities to practice regulation in connection with others. Over time, these empathetic interactions strengthen neural pathways that integrate emotional awareness with cognitive control, supporting individuals’ greater tolerance of internal states and allowing them to engage more adaptively with boundary setting, uncertainty, and interpersonal demands at work.12

Restore agency and control. When children who experience adverse events learn that they lack control over their situation, their motivation to try to exercise control diminishes — a conditioned response called learned helplessness.13 This pattern can continue into adulthood, even showing up in situations where control is possible. Adults who have survived ACEs may struggle with seeing clear paths forward when encountering challenges at work and are more likely to demonstrate cognitive distortions psychologist Martin Seligman dubbed the “3 P’s” of learned helplessness: personalization (blaming themselves for setbacks rather than taking into account external factors); permanence (believing that the setbacks reflect an unchangeable reality); and pervasiveness (generalizing from one negative outcome to predicting failure in all parts of their lives). Brain changes associated with lack of control or power can bias an individual’s attention toward potential risks, errors, and negative outcomes, promoting caution, withdrawal, and behavioral freezing.

These patterns are not fixed. When workplaces offer meaningful autonomy, amplify employee voices, and give them influence, the brain receives repeated signals that effort can shape outcomes. (A considerable body of research also links autonomy on the job to intrinsic motivation and employee well-being generally.) Under these conditions, inhibition systems quiet, stress hormone levels decrease, and neural resources shift toward approach-oriented networks that support motivation, learning, and adaptive problem-solving. When an individual has sustained experiences of agency, neuroplastic changes recalibrate the balance between vigilance and engagement, reinforcing a sense of personal efficacy and control that was often unavailable to them earlier in life.14

Management Imperatives for a Healthy Workplace for All

Managers are unlikely to have insight into employees’ childhood experiences (nor should they ask anyone to disclose them), so the suggestions we offer below are not targeted interventions. Rather, they reflect healthy work design and leadership principles that we argue can help the many people whose well-being has been affected by ACEs. We offer five recommendations.

Establish norms around respectful interaction. Leaders can set expectations for how people in the workplace interact by consistently modeling respectful engagement: being present, listening attentively, and communicating in ways that convey respect and regard for others’ worth. Furthermore, they can explicitly articulate that this behavior is expected and make consideration and mutual respect a dimension of performance.

Design work to help others build agency and influence. Regularly soliciting ideas and feedback from employees can not only foster better decision-making but also help employees build self-efficacy and a sense of influence. Leaders can further reinforce agency by ensuring that each employee has ownership over a clearly defined project or area of responsibility.

Model emotional steadiness. Leaders can shape emotional climates by demonstrating calm, measured responses in high-pressure situations. When leaders manage their emotions effectively in public settings, they demonstrate that stress can be navigated without toxicity or blame, creating the conditions for co-regulation.

Show appreciation and respect publicly. When people feel as though their contributions are significant in the eyes of others, their sense of self-efficacy and performance increase. Leaders can convey worthiness and affirmation by highlighting others’ contributions, strengths, and responsibilities in visible and specific ways.

Use strengths-based feedback to expand employees’ sense of possibility. By taking a developmental approach to performance that builds on employee strengths rather than seeking to correct shortcomings, leaders can help employees shift to describing themselves with more positive, empowering, and secure internal narratives.

Making change does not necessarily require grand gestures or large-scale initiatives. Rather, small efforts — micro-behaviors embedded into everyday organizational functioning and repeated over time — are the key to building a healthier workforce. Engaging with employees empathetically, respectfully, and supportively requires awareness and sensitivity but not time or money. For survivors of childhood trauma, these everyday interactions can play a critical role in rewiring stress responses and steadily repairing the lasting effects of adversity.

The online revolution enabled frictionless access to virtually every type of content — including adult content. Generative AI has added a whole new, and potentially more dangerous, dimension to the boom in explicit content. In the absence of strong safeguards, AI users can transition from consumers to creators of their own explicit media with shocking ease and speed. National governments around the world as well as the U.N. have issued grave warnings about wide-scale production of AI-generated illegal deepfakes, including images involving minors.

Yet Big Tech does not seem to be responding with the same level of seriousness. Earlier this year, Sam Altman, CEO of OpenAI, announced his intention to introduce an “adult mode” within ChatGPT that would allow users to carry on X-rated conversations with the chatbot. The ensuing furor led to the idea being sidelined — for now. Yet Altman’s moment of candor signals that tech leaders are potentially still toying with adult content as part of AI’s value proposition.

Our ongoing research suggests that the human consequences of such a decision may be greater and more direct than the “tech bros” expect. Opening the floodgates to AI-generated adult content could, in fact, invite sharp increases in severe sexual criminality.

We took advantage of a natural experiment in response to a schism in the scholarly literature. Some psychology researchers theorize that fantasies in online environments have a cathartic effect for potential criminals, keeping their transgressions confined to the digital sphere. But an opposing strain of social learning and stimulation theory has it that online fantasies stoke rather than satiate the desire to commit sexual crimes.

We had a unique opportunity to explore this question due to the fact that, unlike many other countries, Japan’s National Police Agency (NPA) publishes detailed monthly crime statistics, including categories of offenses and ages of victims.

We accessed NPA statistics for the period January 2022 to October 2024. Smack in the middle of this time frame was the mid-2023 Japanese launch of ChatGPT’s mobile application. We treated that launch as an external shock that materially reduced access frictions in a mobile-first market. (In 2023, 72.9% of internet users in Japan accessed the web via smartphones.) That same period also saw major improvements in image-generation tools, including a July 2023 Stable Diffusion update that made high-quality image creation easier for novice users. So it made sense to use mid-2023 as a technological turning point for before-and-after comparisons.

Our analysis found that Japan experienced a significant increase in reported rape cases after mid-2023, with the monthly national average rising from 148 to 265 between the first and second halves of our observation period. Using difference-in-difference techniques designed to create clean comparisons between rape as the “treated” offense and murder as a “control” offense (chosen because both are classified by the NPA as serious violent crimes and follow broadly comparable institutional processes), we found that the increased incidence of rape consistently outpaced that of murder over the same period by approximately 12 cases per month. Before mid-2023, there was no statistically significant difference in the monthly fluctuations of the two types of offense. The same basic pattern held when we compared rape to the other offenses Japan classifies as serious violent crimes that are not necessarily sexual in nature, such as arson and robbery.

Still, it could have been a case of correlation rather than causation. Viewed purely in terms of the timeline, the unfortunate spike in rape cases could have been related to all sorts of social factors unobservable in our data. To strengthen our analysis, we obtained data from one of Japan’s largest and most popular online art communities, where users can upload, view, and vote on publicly shared artworks. The community attracts approximately 3.6 million monthly visitors and roughly 2.9 billion monthly pageviews. The platform’s primary language is Japanese, and external web traffic estimates indicate that roughly two-thirds of visits originate from Japan (whose population numbers approximately 130 million). Given this scale and concentration of Japanese users, the platform provides a meaningful window into national-level shifts in online behavior. Importantly, the online community does not prohibit images that are adults-only and/or algorithmically generated, but community members are empowered and encouraged to tag images as such, whether or not they created or uploaded the image.

We discovered that periods of peak engagement (likes, bookmarks, and views) with material tagged as AI-generated and adults-only were also those in which the increases in rape relative to murder were particularly high. This statistical relationship manifested only after mid-2023, when generative AI became broadly available in Japan.

The totality of this evidence points to a scenario in which AI-generated explicit content directly contributed to a rise in sex-related crime. Rape is one particularly heinous example of such a crime, but there is no reason to expect that this scenario would not also apply to other sexually motivated offenses.

The implications of our results became especially alarming when we narrowed the focus to content and criminality involving minors. Engagement with explicit images that were tagged by users as featuring minors was associated with an even larger increase in rape relative to murder. When we zeroed in on rape cases with victims under the age of 20, the estimated amplification was larger still. Although these results do not establish a one-to-one pathway from specific images to specific offenses, they are consistent with a disproportionate increase in offenses involving younger victims. Subsequent studies clarified that the additional crimes mainly consisted of adults offending against minors rather than minor-on-minor crimes.

Our Japan-specific findings appear to dovetail with patterns noted elsewhere. For example, observers were shocked and stymied when the CyberTipline, a reporting system for online child sexual exploitation run by the National Center for Missing and Exploited Children in the U.S., announced that in 2023, reports had gone up 12% over the previous year. The figure’s correlation with the timing of the diffusion of AI tools for the general public is highly suggestive of a connection in line with our findings.

To be sure, our research merely documents an apparent causal connection. Our findings do not reveal exactly how the dissemination of, and engagement with, AI-generated adult images fueled criminal behavior, nor can we state with confidence that these images were solely responsible for the increase. Certainly, we do not claim to have definitively settled scholarly disputes over whether explicit online content acts as a catalyst for criminal behavior or facilitates an essentially harmless cathartic release.

However, the context-specific connection we have identified is serious enough to justify concern about Big Tech’s inclination to integrate explicit content into popular AI models. Against the seeming rationality of Altman’s avowed aim to “let adults be adults,” we must juxtapose the probable impact on real-world sexual crime, especially as it pertains to the most vulnerable among us: underage victims.

So far, the most aggressive regulatory attention in this area has targeted “revenge porn” deepfakes, as is the case with the TAKE IT DOWN Act signed into law by President Donald Trump in 2025. When it comes to AI-generated explicit content that does not depict identifiable individuals (including content involving minors), current legal frameworks have not caught up to the generative capabilities of AI. As of this writing, for instance, European Union legislators are mired in debate, weighing online privacy concerns against the obligation to confront the sexual abuse of minors, the result of which is an enforcement impasse. While societies work out how to respond to newly emerging threats, websites and apps for the express purpose of producing explicit content have proliferated.

Authorities and policy makers are not helpless, however. Bare-minimum, relatively uncontroversial safeguards that could be brought to bear in the short term include these options:

• Watermarking and provenance tracking, which could assist in holding providers accountable by linking content to the platform or tool that created it.
• Age verification, which, if rigorously applied, could at least restrict minors from taking part in the production of explicit material.
• Tighter enforcement of laws that have already been adopted by 45 U.S. states, such as those criminalizing online child sexual abuse material.

Steps such as these would be an appropriate starting point. But the scale of the issue — and the real-world implications for criminal behavior revealed by our research — demands that lawmakers redouble their efforts to close legal loopholes that allow AI-generated explicit content to propagate unabated. And companies would be wise to consider the reputational harms of being seen as part of the problem.

“The ChatGPT moment for robotics is coming,” declared Nvidia CEO Jensen Huang at the Consumer Electronics Show in January 2025. It’s a widespread expectation: that humanoid robots will follow the same explosive adoption curve as generative AI. Our research suggests the opposite. Humanoid robotics will be adopted unevenly, across diverging use cases and geographies. Leaders need to reset their strategies for a more complex, jagged path to scale.

Humanoid robots are developing at pace. Engineering advances and new powerful models of physical intelligence, including vision-language-action models and improved locomotion systems, are converging to create a robotics super-cycle. But leaders who expect the path of humanoid adoption to mirror that of generative AI are misjudging the strategy required.

A Less Predictable Pace

Our research shows that three fundamental forces are shaping a very different adoption curve.

Force 1: No One-Size-Fits-All Model

Some humanoid manufacturers envision general-purpose robots working across multiple activities and industries. But for the foreseeable future, humanoid robots will not be commercially deployed as universal productivity tools.

Consider the multitude of roles that humanoids could theoretically take on: carer, guard, soldier, inventory picker, assembly line worker, hospital assistant, or concierge, to name but a few. Each role, and often each separate task of that role, imposes distinct and often incompatible requirements.

A warehouse robot, for example, may need to lift up to 132 pounds, requiring sophisticated actuators — the equivalent of human muscles — to complete its task successfully. Those actuator systems will make up 40% to 60% of its cost. But a care assistant robot requires different capabilities: subtle facial expression, fine motor control, and emotionally sensitive interaction. In this case, the technology build will tilt more heavily toward perception and haptic technologies. The diversity of humanoid roles implies fundamentally different technology build specifications, operating requirements, and ultimately, business cases for deployment.

This divergence extends beyond the physical build into software and connectivity. A security robot will use low-latency edge-computing data processing close to its physical location — so it can rapidly respond to environmental changes, such as an incursion by an unknown person or vehicle. By contrast, a humanoid hospital assistant may rely on large graphical models to navigate hospital facilities and create 3D images of patient charts and X-rays. Each scenario requires different computing architectures. This specialization, in turn, supports an array of humanoid variations, such as fully mobile bipedal robots or wheeled torsos, reflecting the different physical demands of each role.

The result is the de facto specialization of competing humanoid manufacturers, each prioritizing different market niches. Analysis of these companies shows heavy focus by market segment — manufacturing, warehousing, care, service, and home — in market strategy and customer cases.

Humanoid robots will therefore come in a variety of shapes and specifications. They are being built as specialists, not generalists, and will be scaled role by role.

Force 2: Geographic “Islands of Viability”

Robot costs are dropping steadily, but every humanoid deployment must be justified with a clear return on investment — a determination that will vary sharply by location due to workplace regulations, labor market dynamics, and demographics, among other factors.

Consequently, early humanoid business cases will be concentrated in a few “islands of viability” where conditions are ripest for first deployments. ROI will be strongest in locations that have high labor costs and acute workforce shortages, or that have existing infrastructure and potential for immediate mass-market scale. There will be fewer compelling business cases in parts of the world with abundant and cheap labor, or limited humanoid manufacturing ecosystems.

Japan is arguably the world’s most pronounced island of robot viability for care and manufacturing tasks. Facing a projected 11 million worker shortfall by 2040, the Japanese Ministry of Economy announced new procurement targets to drive robotics adoption and recently announced it would provide nearly 400 billion yen (approximately $2.4 billion) in aid to build an AI system to control robots. South Korea faces similar demographic pressures, with a rapidly aging population and the world’s lowest birth rate. Both countries are seeing rapid humanoid investment. For instance, Hyundai plans to invest $6.3 billion to build a new robotics manufacturing complex in South Korea as the company shifts its strategy from automotive manufacturing.

Other islands of viability are centered around manufacturing. Germany has a documented skills shortage, while Singapore has a limited domestic labor pool and strict foreign labor quotas — conditions in which humanoid deployment can generate returns for companies deploying them.

China presents a different dynamic. Its full-stack supply chain, competitive manufacturing base, and mass-market scale allow manufacturers to build humanoids at a fraction of the costs faced by competitors. It is estimated that China built 90% of the humanoid robots produced globally in 2025. Two companies, Agibot (based in Shanghai) and Unitree Robotics (in Hangzhou), together shipped over 10,000 units in 2025, while U.S.-based peers like Figure AI and Tesla remained in the low hundreds for actual customer deliveries. The Lunar New Year celebrations in 2026 produced by China Central Television, featuring dancing Unitree robots, was a national declaration of intent for this sector, reflecting long-term government investments.

Regulation adds another layer of geographic complexity, as countries take divergent approaches to safety, privacy, and liability, especially in sensitive settings such as health and social care. As with semiconductors and AI software, humanoid robotics will likely be entwined with the geopolitics of technology, which could result in parallel humanoid ecosystems in the U.S. and China.

The message is clear: For years to come, where you locate humanoids will be fundamental to their viability.

Force 3: The Human Factor

The third factor shaping humanoid adoption is the least understood and potentially the most consequential. People are physiologically wired to react to the human form. When a humanoid enters a workspace, care home, or shop floor, its human resemblance triggers instinctive and visceral responses that other technologies don’t.

Human reaction to humanoids will be an unpredictable but crucial factor in the success of their rollout. Most attention focuses on the potentially negative user reactions of humanlike robots. The uncanny valley effect, first identified in 1970 by Japanese researcher Masahiro Mori, captures how encounters with humanlike robots can leave people unsettled and uneasy. Yet the full picture is more complex.

A growing body of research investigates the diverse and often polarized reactions that humanoids engender. For example, in care settings, a 2025 University of Cambridge study found informal caregivers became increasingly comfortable sharing their emotional struggles with a humanoid robot, and subsequently experienced measurable improvements in loneliness and mood. Some research points to more ambivalent reactions; one study in the hospitality industry found that while two-thirds of guests positively reviewed experiences with robot servers, 28% reported discomfort — with little middle ground.

Human personality types also affect the quality and effectiveness of human-robot interactions. Emerging research suggests that humans tend to prefer humanoid robots with similar personality types to their own. Put simply, extroverts will likely prefer humanoids with more social personalities or exaggerated behaviors, while introverted individuals may be more skeptical or less trustful of humanoid interactions.

Recognizing this complexity, researchers in the emerging field of human-robot interaction are working on how to build trust between humans and humanoids. One focus area is embedding empathy in robots. For example, the whEE (when and how to express empathy) framework is a new approach for adjusting the empathy level based on the situation. Another focus is how to effectively blend different personality types to achieve the best outcomes.

While much remains to be understood about human-robotic interactions, it is clear that humanoid robots must work with the full spectrum of human personalities, as not all users will react in the same way. For business leaders creating or deploying humanoid robots, this means being aware of how easily the experience for employees and customers can shift from positive to negative.

Preparing for the Humanoid Wave

None of these factors will stop the rise of humanoid robots, but these three forces suggest a near future in which humanoid adoption will be uneven and dependent on their role and location, and our complex human responses to humanlike forms.

Few organizations are prepared for this asymmetric adoption. Most risk either accepting visionary promises of productivity that prove unrealistic or missing out altogether on the potential business benefits of humanoids. To succeed, forward-thinking leaders across sectors need to engage on three fronts:

Develop a role-based, location-centric portfolio. The first priority is to identify and prioritize a tight portfolio of high-potential humanoid roles within the organization. Ask which roles could benefit from humanoid involvement. Give each role its own business case, with separate outcomes, risk profiles, and scaling paths. The case for each will not be just about pure labor substitution gains but also benefits such as safety, auditability, and availability.

Determine a geographic market. The geographic variability in workplace regulations, demographics, and supply chains means that focusing on only one country is risky. Your humanoid strategy needs to build in geographic options and partnerships. This might involve running a pilot in a high labor cost market, such as Germany, where the ROI case is strongest, while investing in a learning partnership with a university or research lab in Silicon Valley or a manufacturing operation in Hangzhou. Use these interactions to accelerate learning, even if primary operations sit elsewhere.

Your partnership shortlist should span humanoid-native companies and adjacent sectors such as automotive or consumer electronics ecosystems. Companies in these sectors are already wrestling AI into mass-market physical products. Even if the business challenges of AI and robot implementation differ, you can tap into the accumulated expertise within these sectors for transferable deployment lessons.

Consider the human reaction. Forget about the long history of disappointing and flaky robots. Plan, design, and test for the new chemistry of humans and humanoids — the frontier of advanced AI adoption.

Humanoid deployment isn’t a purely technological transition. It must also involve preparing humans to understand and benefit from this new wave of humanoid counterparts. Above all, articulate the benefits of the next generation of these robots: always available, fully aware of their surroundings, and fluent and empathetic in communication. Throughout the deployment, measure and closely monitor the impact on trust among your employees and customers, and be prepared to adapt rapidly.

The humanoid wave is gathering force. Advanced robotic capabilities exist now, even if more remarkable and superhuman capabilities seem distant. As a result, humanoids will disrupt markets faster than many leaders expect — and more unevenly than anyone is planning for. Success will belong to those who start on their own path to adoption now.

The executive team at Medita Holdings was overstretched. Leaders struggled to decide where to focus their attention — and the company’s shrinking resources — across a patchwork of unrelated ventures. Rivals were pursuing growth opportunities and creating new partnerships, but Medita had stalled.1

During team meetings, leaders were fully engaged and appeared to get along well. But the head of HR had noticed that leaders tended to engage in unproductive discussions, such as questioning earlier decisions without resolving any of the issues they raised, or allowing conversations to drift away from the topic at hand. She began to suspect that the core problem was not the strategy at all. Was the CEO a weak leader? Or was there some other explanation for the team’s chronic inability to focus and follow through?

It turned out that the endless debates sprang from a personality trait the group had in common: strong intellectual curiosity. When teammates struggle to collaborate and make decisions, their behavior can often be traced to similarities and differences in their personalities.

But systematically paying attention to how different personality types affect how teams interact and perform is rare. In fact, the structure and work processes of modern teams make it harder for teammates to get to know one another well and build rapport. Companies increasingly form ad hoc teams as new challenges and opportunities arise. Virtual meetings, now routine, tend to be task-focused and offer few opportunities for team members to connect on a personal level. In this context, “slogging your way to a better relationship with colleagues” through trial and error is painful and inefficient, as Ian Roberts, the CTO at Swiss plant equipment manufacturer Bühler, told us.

Over eight years of working with thousands of executives on hundreds of teams, we developed a way to improve team dynamics more easily using a familiar tool: a personality assessment. Specifically, we used the widely studied and accepted Big Five personality test to help reveal the psychological differences that can sap collaboration and team productivity. The assessment highlights individual differences in how people think, act, and feel across 30 personality traits in five broad dimensions: need for stability, extraversion, openness, agreeableness, and conscientiousness. This framework has dominated personality research because of its high validity and robustness over time.2 (See “The Research.”)

The advantage of using personality assessments as diagnostic tools is that they open up rich conversations. Through facilitated discussions, participants can connect their collective personality traits with team tensions or difficulties — whether latent or overt — and defuse them in the process. And for newly formed teams, the same exercise can provide insights that accelerate cohesion. Whether team members collaborate effectively is often rooted in their ability to work through, accept, and capitalize on their differences.

Know Your Team DNA

When we consulted with Medita Holdings, we suspected that the reasons for executives’ indecisiveness went deeper than weak leadership. To develop a more rounded picture of the team dynamics, we read the most recent 360-degree feedback team members had received, interviewed them individually, and asked each of them to complete a personality assessment. We evaluated this information to determine whether their self-perceptions matched how their colleagues saw them and to identify personality traits that might not be apparent to themselves or others.

Next, we set up a workshop to discuss the results. To prepare, we plotted the personality scores of the five team members, plus the leader, on a single sheet of paper, like constellations. We used a different color for each person but did not identify them by name. The illustration revealed the traits where team personality preferences strongly clustered or diverged, giving participants a sense of what we refer to as the team DNA. Each pattern carries risks and benefits. (See “Visualizing Team DNA.”)

Take extraversion, for example. When a team clusters at the low end of this dimension, their discussions tend to remain structured and everyone is likely to be heard. But their meetings may lack energy. Conversely, when a team clusters at the high end, members may enjoy lively interaction, but they may talk over one another and listen less. Further, when a team is split across the extraversion spectrum, it can potentially combine reflection with action. But the team may struggle with tension between the quieter and more outspoken subgroups, who might see each other as passive or domineering.

The Medita team DNA plot revealed a striking cluster at the high end of the intellectual curiosity continuum, indicating that the team members were extremely open to exploring new ideas. Observing a lower-scoring outlier on every trait related to agreeableness initially (and naturally) prompted them to speculate about who it was. But as we delved more deeply into the specific traits within each dimension, they turned to discussing the challenges for this still-anonymous individual. They recognized, for example, that their colleague might grow exhausted in their role as the lone challenger of the consensus and stop participating. As one team member put it, “You begin to sound like a broken record.”

Further, we explored how individuals scoring very high or low on specific traits may help the team. Participants noted that their colleagues who scored low on conscientiousness may be more flexible when business objectives shifted and that those who scored highly on anxiety may be quicker to detect emerging risks.

Once a team has explored its dynamics in the abstract, we consider revealing individuals’ identities — provided that the members all agree to it. When a team opts for disclosure, the facilitator must invest more time upfront setting the ground rules and boundaries for a respectful discussion. Concealing and revealing people’s identities can both generate rich insights, as the examples that follow show.

Three Patterns to Look for

Once the team understands its profile, members can reflect on the implications of the most extreme patterns — specifically, which perspectives they might be overlooking and where they can expect disagreements.

Three patterns warrant particular attention: a skewed cluster, where scores are tightly grouped at one extreme; a dispersed distribution, where the team diverges widely on one trait; and a grouping with a strong outlier. Left unmanaged, these configurations can create blind spots or tensions, leading to three traps that can derail teams.

1. The like-mindedness trap. The Medita team was proud to see that every member scored high on intellectual curiosity, feeling that it reflected intelligence, creativity, and a learning orientation. But when we started probing the consequences of their collective openness to ideas as indicated by the skewed cluster, they realized that it was the source of their core problem: their frequent digressions and endless discussions.

There was no one to counter their exploration of any decision. “Each new comment opens a Pandora’s box,” noted the CEO. “It’s sometimes impossible to just put a full stop on one item and move on to the next agenda point.”

Because leaders choose team members with qualities they especially value in followers, they may inadvertently drive that convergence of scores. But like-mindedness can lead to blind spots that are difficult to detect until the team fails to make decisions or makes poor ones. The blind spot for the Medita team was members’ inability to see when their free-ranging discussions became unproductive. After surfacing this missing factor, the team designated a rotating “focus keeper,” whose role was to signal when the conversation drifted and get them back on track.

2. The synergy trap. On some teams, personalities may be polarized, with two or more people scoring at opposite extremes for a specific trait. These widely dispersed distributions reveal a potential for synergy, where better decisions arise from integrating diverse perspectives. But that does not occur automatically. As long as personality differences remain unexplored, they are hazardous. Diverging on a single trait can create irritations that ruin relationships and ripple through the team rather than bringing people together. The group may fragment, and its performance may decline. In the worst cases, polarization leads to conflict and turnover.

In a Nordic food processing company we advised, the top team was perturbed by tension between the two most powerful figures below the CEO: the chief strategy officer (CSO) and the CFO. The distress call we received from the CEO went something like “Please do something before I fire them both! They are killing our team meetings.”

When we looked at their personality profiles and listened to how each executive viewed their relationship, a plausible explanation for their tension quickly emerged. The CSO had a high score for gregariousness, while the CFO’s same score was low. “I can’t stand his constant talking,” the CFO told us. “He thinks out loud, and whenever there is a small thing to say, he wants to meet.” From the CSO’s perspective, the CFO was “secretive” and “unwilling to share” because he was often silent and unresponsive. “He just wants to make decisions on his own,” the CSO complained. Their simmering frustrations seeped into team meetings, where the pair pointedly avoided eye contact or direct exchanges. The discomfort radiated outward, dividing colleagues and putting everyone on edge.

Simply pinpointing the source of their differences was a revelation. They pledged to stop venting about each other to colleagues, and they chose to focus instead on their similarities — notably, their high conscientiousness scores. They were both strongly motivated to achieve their goals and confident in their abilities.

A whole team can be polarized too, which can lead to a deep fault line. This happened at a European engineering company. The board chair called us in after informal chats with members of the top team led him to conclude that some executives were disengaged and at risk of leaving.

Another board member described the problem to us. “Everything gets decided by the CEO and these two people,” she said. “It’s like the other four have nothing to contribute.” Worse, she sensed that the CEO’s two “lieutenants” were not the most capable members of the team.

The CEO’s extremely low score on measures of moodiness indicated that he maintained a consistently positive outlook, which cleared the way for broaching this issue. He described himself to us as an incurable optimist, and his favorite team members’ mood scores most closely matched his. The rest of the team was loosely grouped at the higher end of the mood scale, meaning they tended to be more measured in their outlook.

Pointing out the CEO’s fixation on positive energy enabled us to discuss his unconscious bias against the four less upbeat team members and the risks of unchecked optimism. It also clarified why the four seemed increasingly disengaged. He had assumed that their withdrawal had led him to favor the other two team members, but in reality, the four participated less because he favored the other two. Recognizing this helped the CEO to appreciate their value as counterweights to his exuberance and led him to plan an offsite to reset the relationships.

3. The outlier trap. Teams fall into the third trap when the entire team is unified on a single trait except for one extreme outlier. This outlier may be ignored or dominate the team, in both cases preventing the group from making full use of its competencies.

In the first scenario, the outlier’s dissenting voice, though valuable, is disregarded by the rest of the team. Consider the case of a self-managed project team struggling to make progress on a strategic consulting task. After we debriefed each individual on their personality profile, we asked the group to articulate some rules of engagement and explore their team dynamics without a facilitator. (We wanted to see whether teams could run the exercise themselves; we talk more about that below.)

They quickly homed in on their starkest differences and figured out how they affected their interactions. All of the team members were working on the project alongside their usual responsibilities, and they clashed repeatedly over how often to meet. One person insisted on frequent and regular meetings. The more she pushed, the more they resisted, dismissing her as a “pain” and exacerbating her frustration.

“And this is why,” they said, pointing to their scores for conscientiousness. While most of the team had scored somewhere in the middle on this set of traits, the woman who wanted more meetings had scored extremely high on both achievement-striving (that is, focus on reaching her goals) and self-discipline.

This insight helped them all realize the source of her deep concern about scheduling and progress. They came up with a compromise that they could all commit to: Instead of scheduling more meetings, they would hold brief end-of-week check-ins to stay aligned.

Beyond ignoring the “odd one out,” the team can also become “odd one led,” notably when the outlier is either the formal leader or the opinion leader in the group.

Take the case of a high-profile but underperforming European opera house. To turn it around, the board had appointed a former accountant as director general. While the new director general brought commercial discipline to the operation, the opera house’s artistic productions continued to underwhelm critics and audiences. The four-person executive team seemed unable to break the cycle of turnover among directors, designers, and performers.

Interviews with members of the executive committee and other senior leaders revealed that the director general had reinforced the existing siloes between the artistic and commercial functions and that the financial focus had come to dominate their discussions.

The personality profiles of the four top executives revealed that the director general had a strikingly low score on adventurousness, whereas the scores for the rest of the team were bunched in the middle. The director general’s preference for the habitual seemed to curtail efforts by the rest of the team to introduce new thinking and explore how other performing arts venues were reviving their fortunes.

His score opened a wide-ranging discussion about the opera house’s need for fresh ideas. Later, the executive team was restructured to include the artistic side of the organization, giving it a greater voice in decisions.

Although it’s too early to know whether those steps will improve the opera house’s financial and artistic health, the changes have reduced frustration within the organization and brought forward some fresh initiatives.

The Ultimate Icebreaker

Personality is not the sole cause of team dysfunction and tension, but it plays a significant role. It is a guaranteed differentiator, even in the most homogeneous teams. This makes a workshop focused on personality types a rich and intriguing entry point for discussing team dynamics.

“Talking about personality kicks off conversations and lets us see each other’s struggles and underused superpowers,” Medita Holdings’ head of HR said. She noted that extreme scores might not surprise anyone but that discussing them candidly enables participants to find value in traits they otherwise see as frustrating.

Further, many executives have experience using the Big Five personality test for their personal development, so they understand what they can learn from it. The International Personality Item Pool (IPIP) is a free, open-source version of the instrument that includes a comprehensive report explaining the scores and the option to plot comparison charts. Additionally, the IPIP can be adapted freely and does not need to be administered by a certified facilitator.3

Debriefing the exercise is straightforward for any facilitator, coach, or consultant who is familiar with Big Five assessments and can set and enforce behavioral boundaries, help teams reflect, and keep discussions on track. The team does most of the work, by identifying the dimensions where they struggle or that resonate most with them.

Team leaders can facilitate the process themselves, role-modeling and enforcing the behaviors required for an open discussion. To succeed with this approach, however, they should disclose their own personality assessments and participate in the discussion, as happened with the self-facilitated project team mentioned earlier. Disclosure allows team members to question the leader’s preferences and blind spots, connect their insights to observed behavior, and make practical adjustments.

However, even when team leaders are willing to share their assessments, other team members may hesitate, concerned that revealing their own preferences will expose them to judgment or misinterpretation. For this reason, the exercise often works best when facilitated by someone from outside the group who can credibly reassure participants about the purpose and process of the discussion. It can take time to grasp that there are no “bad” profiles and that individuals can safely reveal their identities. An external facilitator can also point out dynamics involving the leader without triggering defensiveness.

Before starting the exercise, the facilitator must lay the groundwork to help participants cocreate the rules for interaction, understand the payoff, and buy into the process. A half-day session allows for this preparation plus two or three hours of discussion and reflection. It is important not to rush any step, so that everyone has a chance to contribute. Researchers find that conversational turn-taking boosts psychological safety and team performance.4 (See “Guidelines for a Productive Discussion.”)

Mutual agreement on the ground rules creates clarity and trust by addressing three key questions: (1) What will be important to achieve in this conversation? (2) What topics or behaviors are off-limits? and (3) How will we call out unacceptable behavior? Facilitators can foster candid and respectful discussions by requiring participants to focus on a common goal, disagree respectfully, stick to facts and evidence, acknowledge their biases and knowledge gaps, and ensure that everyone has a voice.5

Teams do not need to wait for problems to arise to engage in the process. We know a couple of leaders who shared their personality profiles unilaterally with teams they had inherited, to help their new followers adapt faster to their quirks and leadership styles. Inspired by this proactive approach, we expanded our research to new teams and teams with new leaders. We found that personality assessments can help them jell more quickly.

Using this exercise when team members first begin working together has several benefits. It provides them with a common vocabulary to talk about their individual and collective strengths, sensitizes them to the limits of those strengths, and highlights the potential value of having contributors with unusual traits. More broadly, it jump-starts team collaboration by getting colleagues used to disclosure, information sharing, and mutual respect.

After we ran this exercise with the top team at a French fintech group and an internal coach, they decided that the entire staff would take the Big Five personality assessment to prepare them for joining any new team. “Discussing hidden differences as a team … changed our view of colleagues and the way we talked to each other,” the CEO said. “It saved a lot of time and misunderstandings.”

Reducing the Guesswork in Teamwork

Whether the aim is to fix problems or prevent them, comparing personality assessments benefits individuals and their teams. Enhanced self-awareness makes people better team players, while team awareness improves group interaction.

Even if someone has received personality feedback before, when they compare their results with those of their teammates, they see how distinctive they are. Enhanced self-awareness enables individuals to better recognize how their traits work for or against them in different situations.

For example, a Danish business leader we coached scored lower than he expected on altruism — specifically, on his consideration for others. Yet, as an accomplished manager, he knew that he had to take care of the people on this team. Because he lacked the intuition to guide his interactions, he developed a habit of checking in regularly with his direct reports. As a result, they viewed him as considerate of their needs, despite his underlying self-absorption.

Although it’s difficult for people to modify their traits, anyone can stretch their repertoire of behaviors, as the Danish leader did. Individuals can develop routines to keep their traits in check, such as resolving to speak last if they score high on assertiveness and tend to dominate conversations.

Another option is to find others with complementary profiles. For example, in a Financial Times interview when he was CEO of VMware, Paul Maritz was candid about his Achilles’ heel. “I don’t like confrontation,” he said. “I need other people to keep me honest on that front.” He appointed an “enforcer” to his team to keep him focused on tough decisions he might otherwise dodge.6

Self-awareness is only half the battle, however. Realizing that colleagues have different ways of thinking, doing, and feeling is the other half. The team DNA chart highlights areas where the entire team needs to be vigilant. A team that is aware of its collective tendencies can take steps to compensate for its potential vulnerabilities.

In one exercise, the CTO of a watch manufacturer was surprised to see that the entire team, including himself, had midrange scores on imagination. He had perceived himself as highly innovative, but when he challenged the validity of the results, his team members disagreed. “No, you have initiative,” a colleague said. “But that’s different from innovation. You start things, and you give some impulse, but then your ideas are not … .” The sentence trailed off.

That feedback was tough for the CTO to take, because he oversaw innovation. But it prompted an overdue discussion about how the company could resuscitate its innovation capability and get out of its slump.

With teams in a state of constant flux, leaders need better tools to bring new members up to speed, facilitate productive exchanges, and quickly create rapport. As the chief operating officer of Medita Holdings observed, “They’ll find out about each other anyhow. May as well do it upfront” while establishing principles and good habits for communication.

Exposure to the range of personality differences within a team helps executives better understand where colleagues are coming from. It makes it easier to value and solicit their input and to anticipate their quirky reactions. Antagonists come to realize that a teammate’s thinking is not stubborn or wayward but predictably different — and they can stop taking the opposition personally.

Paradoxically, knowing colleagues’ personality traits depersonalizes discussions. Above all, it boosts people’s appreciation for what each party can bring to the table. As Maritz memorably observed, “Really great teams have team members who know who they are and who they’re not, and they know when to get out of the way and let the other team members make their contribution.”7

Erik Brynjolfsson has a challenge for anyone worried about artificial intelligence: Stop asking what AI will do to us, and start asking what we will do with AI. In this episode of Me, Myself, and AI, the Stanford University economist explains why technology isn’t the biggest barrier to progress — people, organizations, and institutions are. Drawing on new research into AI’s impact on jobs, productivity, and economic growth, he argues that the future isn’t predetermined: It will be shaped by the choices we make today. This is a timely conversation about human agency, shared prosperity, and why the most important AI breakthroughs may have less to do with technology than with how we use it.

Subscribe to Me, Myself, and AI on Apple Podcasts or Spotify.

Transcript

Allyson Ryder: Today’s guest has a bold provocation: AI isn’t being held back by the technology. It’s being held back by us. Curious how? Find out now.

Erik Brynjolfsson: I’m Erik Brynjolfsson at Stanford, and you’re listening to Me, Myself, and AI.

Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review exploring the future of artificial intelligence. I’m Sam Ransbotham, professor of analytics at Boston College. I’ve been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 13 seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success.

Today I’m talking with Erik Brynjolfsson, who runs the Stanford Digital Economy Lab and studies how people use technology in general and now AI specifically. He’s trying to think about how technology is affecting [the] economy and work. His book The Second Machine Age shaped a lot of this debate. And his team recently published “Canaries in the Coal Mine?”, a research paper about what’s happening to entry-level workers. Erik and I bump [into] each other a few times a year at the National Bureau of Economic Research, and he always brings up something I hadn’t considered. No pressure, Erik, but I’m expecting the same today.

Erik Brynjolfsson: [It’s] good to be here, Sam.

Sam Ransbotham: Welcome to the show. Let’s start with the Stanford Digital Economy Lab. Can you give us a quick view of what the lab does?

Erik Brynjolfsson: Sure. We study the digital economy. … I loved my time at MIT. I was there for over 25 years, but now I’m out here in Silicon Valley, the epicenter of the AI revolution. We’re focusing on how AI and other digital technologies are changing the economy. Kind of the premise of the lab and of my work, my career, is that technology is advancing very rapidly. The capabilities are amazing. At the same time, our economic understanding is not advancing nearly fast enough. Our economic institutions, skills, organizations, they aren’t keeping up. My job is to try to close that gap.

Sam Ransbotham: It’s those pesky people. The technology moves fast. Organizations and people slow us down, I guess, is the summary there.

Erik Brynjolfsson: That’s right. A lot of your work has highlighted that, too, and we’re doing what we can to keep up.

Sam Ransbotham: I was thinking [about] the Race Against the Machine book and … “Canaries” today. What connects all of it?

Erik Brynjolfsson: “Race against the machine” … was the headline. The conclusion was that we should race with machines, not against machines. And that’s what I’ve continued to emphasize — there’s an opportunity for humans and machines to work together. It’s not automatic. There’s a lot of choices that we need to make, but the technology is enabling all sorts of new possibilities. Trying to invent and discover those is a big part of what we humans need to do.

Sam Ransbotham: I like the choice part because I think it’s so easy for us to slip into [the] vernacular of saying, “AI does this, technology does that.” I know you’ve been against that.

Erik Brynjolfsson: That’s exactly right. It’s probably one of the most common questions I get: “What’s AI going to do to us? What’s AI going to make happen next?” I’m like, “Wait a minute, the premise of your question is wrong. It’s ‘What are we going to do with AI?’”

This is an incredibly powerful tool, arguably the most powerful tool that we humans have ever had. Almost by definition, that means we have more ability to change the world than we ever had before. So let’s think about how we want to use that. It turns out that our values, our choices matter more now than they did in the past because we do have this ability to make a really big dent in the universe, for better or worse.

Sam Ransbotham: Yeah, for better or worse. I think that’ll probably come up a couple of times as we talk here.

I was thinking about how much you got right in the early Race Against the Machine book. But that’s not interesting as an academic. What do you think you got wrong? What were you surprised about? What’s changed differently than you thought 16 years ago or 15 years ago?

Erik Brynjolfsson: Well, first off, since we’re talking about what we got wrong, I should highlight that this is cowritten with Andy McAfee, along with The Second Machine Age. He’s been a partner on a lot of my projects.

What did we get wrong? We were looking at some advances — I’d highlight particularly in The Second Machine Age, we started that with a ride in Google’s self-driving car. Actually, it was 2012, right after we wrote Race Against the Machine, we rode from Mountain View up to San Francisco. I’ve got to say I thought, “Wow, self-driving cars are just around the corner,” metaphorically, and I thought it would happen very quickly. It’s obviously taken longer. I do ride around in self-driving cars a fair amount here in the San Francisco Bay Area, up in the city. They’ve had them for a while, and now they’ve come down to Palo Alto as well, and you can ride them all the way on the highway. But that’s 15 years later.

It’s taken a while, and I think I was overoptimistic about that. That said, I think I underestimated how fast the technology would advance in other ways. The way you and I can talk to [large language models], whether it’s ChatGPT or Gemini or Claude, and have them do work, I think I would not have expected that to have happened that quickly. I mean basically I would have considered this [artificial general intelligence] if you had asked me in 2012 or 2015. So that’s pretty cool that you can get really good advice from them. We all know they still hallucinate and make mistakes, but so do we humans. On average, they’re really quite good. So that happened faster. We got that wrong a little bit.

Then the most disappointing part is, look, I knew that we were just talking about how human institutions change more slowly than technology. But oh my God, I didn’t expect it to be this much slower. At times it’s almost like it’s moving backward. So that’s been pretty disappointing, that our political institutions, our businesses, organizations, they aren’t keeping up. Productivity is not growing any faster than it was at the beginning of this AI revolution, maybe a smidge if you kind of squint. But we’re not really translating these capabilities into better business performance or the kinds of benefits that we all hope for.

Sam Ransbotham: I rode one of the automated cars out in Palo Alto at the last meeting where I saw you at. It very quickly got boring. Now I was fascinated for a few seconds and then bored very quickly. You touched on that would have been AGI a few years ago, or LLM stuff would have been AGI a few years ago, but it’s not anymore. How much of this is sort of a boil frog, we are getting used to these technologies, it takes even more to impress us? Is that part of it? Is it a measurement part? Is it a J-curve, which we’ll come back to in a second? All the above? None of the above?

Erik Brynjolfsson: All of the above. Part of it is, like you said, getting bored in the self-driving car; the first few minutes are kind of scary and thrilling, and then it’s exciting. And then within half an hour you’re like, “OK, I kind of get this.” These cars, they kind of drive like my grandmother, very carefully and slowly, which is, I guess, the right thing to do.

Sam Ransbotham: That’s why you left Boston maybe.

Erik Brynjolfsson: Exactly. Their accident rate is 90% lower than humans. I’m looking forward to the day that the cars are all like that. Right now they are a little slower point to point. Sometimes when I’m in a rush, I’ll actually get an Uber because I know that the driver is more willing to cut some corners, so to speak. But part of it is that we kind of take for granted things that initially were eye-popping.

I guess our brains are designed to get used to things. But the other two things you said, I think, are more important and more interesting. One is the measurement issue. Our economic metrics are just not designed to capture a lot of the benefits of AI, especially the benefits of free digital goods. So we’ve got a whole methodology for addressing that. Maybe we can talk about that later.

The other part is the J-curve. This is what we started the conversation about — there really is a gap and a difficulty in translating capabilities into real business change. It’s because there [are] so many complementary assets and investments that need to be made. Many of them have to be invented, and we don’t know what they are, these complementary co-inventions of new business processes, new skills, whole new business models and ways of working. They’re hard and we have entrepreneurs and managers and business school professors and consultants all working on figuring them out.

In earlier work I did with [University of Pennsylvania’s] Lorin Hitt and Prasanna (Sonny) Tambe and others, we found that the investments in those intangible assets are about 10 times bigger than the direct investments in computer technology. But since they’re largely intangible, largely invisible, we tend to not appreciate them as much, and we don’t measure them, and people don’t even realize that they’re there. But we’re trying to make them more visible in our research and create an appreciation that you have to make those investments as well.

Sam Ransbotham: Actually, the measurement thing is huge and difficult. I remember you and [Carnegie Mellon University’s] Avinash Collis were talking about the measurement of value from Wikipedia and from Facebook and these other technologies. All this stuff is just superhard to measure. And when we don’t measure stuff, we do a bad job, I think.

Let’s switch maybe to measurement because I think one of the cool stories about your “Canaries in the Coal Mine” study … I think your headline finding was that people ages 22 to 25 were the most exposed [and] lost about 16% of their employment. But you’re using ADP payroll data to find that. That’s a very interesting signal of information. Tell us about that study.

Erik Brynjolfsson: Like everybody, we were seeing all these headlines about AI eliminating jobs or other headlines [that] AI is creating jobs. In any given month, there [are] hundreds of thousands of people who gain or lose jobs. A half-decent journalist can easily find examples of either one and build a narrative around it, or just a man in the street they can talk to. To me at least, I don’t really know how to aggregate that. What’s the real story? So we wanted to get large-scale data.

When we looked at the current population survey, we didn’t really see much, and when we looked at the top line of ADP, which is the world’s largest payroll processor, they shared the data with us here at the Digital Economy Lab. We actually didn’t see much in the top line either. I was almost thinking, “OK, we’ll write a story about how this is all just a bunch of hype.” But then we dove in a little bit more deeply, and some of the subgroups who are having these very large effects, like you just mentioned, people who are ages 22 to 25, early-career workers, they had a noticeable fall in employment, but it really got striking when you subdivided it by how exposed they were to AI.

[OpenAI’s] Tyna Eloundou, [University of Pennsylvania’s] Daniel Rock, and others have created a taxonomy where they rank all of the tasks in the economy, 18,000 distinct tasks by how much they’re exposed to LLMs. Could an LLM help you write a memo? For sure. Could an LLM help you lift a box? Not really. If you rank every task like that and then aggregate them up to occupations, you basically have a score of whether or not this occupation is going to be affected by LLMs.

We looked at about 750 occupations. We divided them into quintiles. The most exposed quintile, a little over a hundred occupations in this youngest age group, had at first about a 12% to 13% decline. Now it’s more like a 16% to 17% employment decline relative to the other occupations. So it’s really quite striking. We didn’t see a fall in employment for the older workers, even in the exposed occupations. You really have to slice it this way.

Even when we controlled for other factors, like we controlled for interest rates, we controlled for the tech industry — you can take the entire tech industry out if you want, or control for tech overhiring — you can control for remote work, you can control for education, none of those things knocked out the core result. We continued to have this very noticeable decline.

I can say another thing, which is that, as I mentioned, the older workers were doing OK. There are a couple of other groups that were doing OK. One is the people in the not exposed occupations. So if you look at the other end of the spectrum, like home health aides, where AI is not affecting them nearly as much, they actually had growing employment, even for the young workers.

And then, the most interesting result, the one I’m most excited about, was that if you divide the way they’re using AI into mainly automating and eliminating work versus augmenting and creating new skills, new opportunities, doing new things that you never did before, the automating group had falling employment, and the augmenting group, the ones who were learning new skills, had growing employment. So it’s kind of a double win: higher productivity and growing employment. For some reason that doesn’t get picked up as much in [the] press when they write about our research, but I think it’s really important that we found that AI could be associated with higher employment for certain kinds of workers.

Sam Ransbotham: I think that ties back to how you use it, your decision-making, the choices that we all make in that. That doesn’t surprise me, because if I think about a task and, going back to this aggregation of tasks you alluded to, no job, I’m sure, runs a hundred percent down the list of tasks and a hundred percent down the list of non-automatable tasks. The composition of that task is going to leave parts of those jobs more valuable. That’s going to leave those parts more valuable.

Erik Brynjolfsson: Exactly. That’s another really important point: You can take every occupation; [each has] a bundle of tasks. In O*Net, a typical occupation has 20 to 30 tasks. A radiologist has 26 distinct tasks. [Computer scientist] Geoffrey Hinton famously said, “Reading medical images, that’s what radiologists do. That’s going to be replaced by AI.” What he didn’t factor in was there were 25 other tasks that radiologists do, and most of them were not affected by AI.

The net effect was that as reading images got cheaper and better because of AI, it actually increased the value of radiologists for doing those other tasks, and net employment grew for radiologists. I’m not saying that always happens, but there [are] definitely cases — some people call it Jevons paradox — where making something cheaper increases the demand for it, and you end up having more employment rather than less, even as it gets more efficient.

Sam Ransbotham: I think it’s the frustrating thing. I think everyone would like an answer, which is “Here’s the clean answer,” and what you’ve come across here is just a gigantic “It depends,” and it depends on an increasingly complicated set of stuff.

Erik Brynjolfsson: Well, let me try and make it a little simpler. It is somewhat more complicated than the simple story of you always eliminate jobs, or for that matter, it always creates jobs. But as an economist, a really useful tool for me — and I’ll get a little wonky for your readers, but it’ll be [a] useful time — is you can think of a demand curve. When price falls, quantity increases. Demand curves are downward sloping. Most of us intuitively know [when] you make something cheaper, people will buy more of it.

But what really matters is how steep that demand curve [is]. For some products, like apples, even if you made them 10 times cheaper, you’re not going to buy 10 times more apples. Maybe you buy a few more apples. But the demand curve’s pretty steep. So as prices go down, you end up spending less. That’s pretty intuitive. Most people think that all goods are like that.

It turns out that not all goods are like that. There are lots of goods where as the price goes down, even just a little bit, the quantity goes up a lot, like jet travel. It used to be that very few people would fly in planes across the country, but now it’s cheaper, and a lot of us fly around the country way too much. So there are goods and services like that. That’s called elastic demand or a flat demand curve.

Whenever you have situations like that, you actually have growing demand for something, and you end up having more spending. At least half the goods and services in the economy are on that side of the curve. That’s really good news. It means that as we get more productive, more efficient, some things shrink, but other things grow. I think there’s a little bit too much of a bias in the public conversation toward the side that’s shrinking. One of the things I want to do with your podcast is get more people to think about that side that’s growing.

I teach a master class [in] which we basically show people how to identify and discover these new opportunities for wealth creation, how you can use AI to do new sorts of things to create entrepreneurial ventures. The more we do that, I think the better off people are going to be. They’re going to help themselves, and they’re going to help the rest of the economy by inventing new things.

Sam Ransbotham: Apples [are] a great example. Like you say, we don’t consume them when we get more of them, but I don’t know of any company that says, “Pretty much all the IT projects that we could ever think about, we’re doing them.” You know what I mean?

Erik Brynjolfsson: Yeah, exactly. It seems like there’s almost infinite demand for solving all sorts of IT problems. When I look around the world, I don’t see a shortage of problems. Poverty, the environment, health care especially, these are all things where we could use a lot more resources, and I don’t see us running out of opportunities to address those.

Sam Ransbotham: The fantasy that we would run out of those types of problems, that’s great, but I don’t think that’s going to happen in the next couple of years though, the next couple of centuries. Let’s go back to this J-curve idea. First explain a little bit about what is the J-curve, and how should people think about that?

Erik Brynjolfsson: This is another concept I think people find very useful, and that is whenever there’s an important powerful technology, we economists call them general-purpose technologies. We used to just say GPT, but my AI friends stole that acronym. But AI is both. It is a generative pre-trained transformer, like GPT-5, but it’s also a general-purpose technology like the steam engine, electricity, the internal combustion engine, and cars. Those are the things that actually drove most economic growth, most productivity. They’re the reason we have higher living standards than our ancestors, a couple hundred years ago or even 50 years ago.

AI is the most important general-purpose technology of our era, maybe of all of history. But the thing about these general-purpose technologies is that the value really comes from the complementary assets. With electricity, it enabled light bulbs and electric motors and air conditioning and ultimately computers and a lot of other things. With AI, it’s enabling all sorts of new business processes, and it’s not just the physical technologies. It’s also these intangible assets that we talked about earlier. That’s great. The thing is that these intangible assets take time to create. They’re expensive. You have to invest in them. Whether you hire consultants or you do it yourselves, you have to reinvent how work gets done.

During that costly period, you’re spending more as you’re reinventing your business processes, but output doesn’t instantly go up. Mathematically that means more input, no increase in output, [and] productivity, at least as it is conventionally measured, goes down. So that’s the downward part of the J-curve. Then later you start harvesting that. You start using these new business processes to create more output. Now output is going up. And that’s the upward part of the J-curve.

What we’re seeing with most general-purpose technologies is that there’s an initial lull where productivity is low or even negative, and then later it takes off. Now it’s hard to see exactly where we are for AI. We’re in the middle of it. But if you look back at earlier history, if you look back at, say, electricity — I wrote [about] some of it in my Ph.D. dissertation — believe it or not, it took about 20 to 30 years of time where companies were trying to reinvent how factories were organized.

They were installing electric motors. At first they did not get much productivity. [Economist] Paul David and others documented this very carefully. For literally up to 30 years there was essentially no productivity gain in factories as they were electrifying, which is just remarkable, but, eventually, they completely redesigned how factories were organized. Instead of being clustered around a central motor like a steam engine, they had separate motors for each piece of equipment, and they distributed the work, and they laid out the arrangement based on the flow of materials.

When they did that, productivity started skyrocketing, like doubling and tripling productivity. But like I said, it took 30 years. I don’t think AI is going to take 30 years. I’m pretty sure it’s already beginning to happen. But at the same time, it takes longer than some of my technology friends, some of my AI friends expect. They think that as soon as you invent the AI, you’re done.

The reality, as you know, Sam, in your work and in my work, there’s a lot of business process redesign that has to happen. We’re in the middle of that, and I’m doing what we can to try to speed that up. My company Workhelix is very involved in showing companies how to identify the opportunities for value creation. If we can speed that up, it’ll be great, but we’re still going to have a bit of a J-curve.

Sam Ransbotham: I’m thinking back to the electricity times. I’m guessing they didn’t have the quarterly stock report pressures and the need for instant results that perhaps people deal with now.

Erik Brynjolfsson: The biggest thing, honestly, is that they didn’t really have all of our institutions. Not to pat ourselves on the back, but business schools didn’t exist. Consulting books [and] the whole science of industrial engineering and management, didn’t exist. I went back and I read some of the books. I went to Harvard Business School’s Baker Library, and they had these old books, and it was pretty primitive how they were thinking about things.

Now there’s a much bigger ecosystem for helping companies redesign their business processes. It still takes time and redesign, [and reskilling] their workforce. But my sense is managers are much more conscious of it right now, and they’re leaning in much more aggressively, and they’re sharing best practice.

At Workhelix, we have a tool that will scan through all of the opportunities and share them with the workers and look at who the superusers are who are really crushing it, and then compare them to the average worker. So it just speeds up the learning by probably 10x compared to what people would have done before they had these kinds of tools.

Sam Ransbotham: I really like that because that again ties in measurement and some other things. The annual performance review is really not helpful anymore. The idea that you could get [a] better measurement about what you’re doing and how you’re doing it helps.

Let me kind of be antagonistic a little bit. I wrote something at MIT Sloan Management Review a while back about rethinking AI objectives. You wrote something about the Turing Trap. I think the first inclination that people have is to pass the Turing test, to make the machine do what the human can do. And that leads us to incremental improvements of existing processes. It seems like measurement could accidentally feed into that as well if we’re not measuring the right things. What are you thinking about that, short term versus long term?

Erik Brynjolfsson: Well, there’s definitely a short term, long term; [those are] your objectives. I was talking to a CFO a couple months ago, and she told me, “Hey, we really need to measure the benefits of AI, the ROI.” And I said, “That’s great. You should be measuring it. Tell me about it.” And she said, “So that’s why I’m demanding that every division tell me how much head count reduction they’re getting from AI.” I was like, “Wait a minute. I’m glad you’re measuring, but isn’t that a little bit simple-minded, because, yes, there’s nothing wrong with cutting costs, but it’s really missing the bigger opportunity of doing new things, allowing your workers to create new kinds of value.”

There’s a CFO getting paid millions of dollars, and the reason they’re making big money is that they’ve got to think more creatively about value creation, not just having machines do what the humans are already doing and replacing them. That’s the classic Turing test: Can you make a machine imitate a human perfectly?

That’s a very narrow way of thinking about it. I think Alan Turing was a brilliant guy, but I think that mindset has done a lot of damage to how we use AI. I call it the Turing Trap because I think that it’s a trap to only focus on using AI to replace or imitate workers. We should also think about how AI can augment and allow us to do new things. But that requires new measures, like, how do you create new products and services? How do you have higher customer satisfaction? How do you improve quality, both the quality of the products but even the quality of the work life for the people working?

These are all things that won’t show up in your narrow replacement mindset, so this is something that also takes a longer time, like you said. Short-termism tends to focus on just cutting costs and what you’re already doing. But the ones that have the most competitive advantage, the ones that have the most lasting benefits, are the ones that think of going beyond that and doing new things, new business processes, new products and services. It doesn’t happen in weeks, but when you do achieve it, you have something that lasts for years.

Sam Ransbotham: There’s plenty of places where machines outperform us. The easiest way to tell if this is a machine or a person and pass that Turing test is that I’m not going to be able to do math very fast. Our objectives are not exactly the right standard, I guess. You mentioned that in the [beginning of] electrification people didn’t have the institutions around that. Neil Thompson [and] the MIT Initiative on the Digital Economy, [which] you were obviously involved in before you moved to Stanford, they’ve got a science article that said something like 90% of the most important new AI models are coming out of industry. We’ve had this massive switch from academia and public funding [producing] technology benefits to most of it coming out of technology companies and startups. You mentioned Palo Alto and the epicenter that’s there. All right, I’m worried. Is that the right place for that stuff?

Erik Brynjolfsson: Part of the reason it’s coming out of industry is that it’s gotten to be incredibly expensive to build these very large models. There are these scaling laws for LLMs where as you make a model bigger, if [it has] more data, more compute, more parameters, you get a predictable improvement in performance. So they went from spending millions to tens of millions, hundreds of millions, billions, now tens of billions, even hundreds of billions of dollars on training these models. No university can afford that. No nonprofit organization can afford that. So the companies are raising huge amounts of money as we’re speaking. Anthropic and OpenAI are filing to go public. Google has a nice cash engine they can pour into this as does Meta.

So that’s what’s driving that. At the same time, what I tell my academic colleagues is your comparative advantage isn’t [in] spending more money on computers. It’s in thinking creatively. There [are] some brilliant ideas that happen just from sitting in your office and thinking deep thoughts. It’s still a good strategy.

I had a conversation with Geoffrey Hinton a while back, and I asked him what kinds of hardware he uses to make his insights and discoveries. He pointed to his laptop and tapped it and said, “This is the hardware I use.” And for those kinds of fundamental breakthroughs, it’s not always a matter of having thousands of GPUs.

I think academics need to lean into their comparative advantage. That said, it’s fair to say that more and more of the frontier research is happening through a really expensive approach. In a way it’s a testament to how valuable AI is. One of the reasons industry didn’t do it before was not just that it was expensive, but it just didn’t have that big a payoff.

I’ll share a story. The first AI company I started, believe it or not, [was] in the late 1980s. That’s how old I am. I started an expert systems company with Todd Loofbourrow. Expert systems are these rule-based systems. We created a little bit of value for some banks and HR organizations, but [at] the end of the day, it was nothing like the value that companies are creating today. That brand of AI just wasn’t all that valuable, I have to be honest. It [is] only now that we have these very powerful neural networks that you really have commercial incentives that are unlike anything we had before. So in a way it’s a testament to the success of the field that industry is investing as much as they are.

Sam Ransbotham: I like that point. The U.S. government, I think, put like a billion and a half dollars or so into non-defense research spending last year, and that’s roughly what Google is putting in. You know, the [National Institutes of Health] and [National Science Foundation] budgets are small. As you point out, we are not going to be able to outspend those.

Erik Brynjolfsson: But on that, I think it’s fair. You want to have both. I mean, it’s the job of academia, it’s the job of the federal government to invest before it’s commercially viable. So much of the long-term benefits of R&D [are] not apparent, and there’s not a commercial incentive to invest. The internet, early space travel, so many fundamental breakthroughs in biology that have extended our lives, they happened in universities and government laboratories because they were not commercially viable, but then later people built on them. You can’t always tell in advance which ones are going to pay off. But if you do the fundamental research, the track record shows that eventually some percentage of them are going to really make a difference in our living standards.

Sam Ransbotham: That’s the basic applied cut. We don’t have to fund the applied stuff because the market will take care of that. And [for] the basic stuff, the invisible hand doesn’t work quite as well in those, or it works slower than everybody would like.

I think you’re going to have a bunch of academics listen to this because they’re curious what you say. What should people be looking at? What are the kinds of things that fall into that category? Maybe a critique is that a lot of the studies that I’m seeing in academia are, let’s say, consulting projects for companies. What are these basic types of ideas that people should be pursuing?

Erik Brynjolfsson: Well, there’s a lot of them in lots of different areas, and I think I don’t want to prescribe them because I think in a way part of the magic is letting people pursue their own ideas even if there’s nobody on the outside that sees the value of them.

That said, I’ll give you my own list. I’m focused on the economic side. I think that right now the biggest gap is that we don’t have a good understanding of how AI is going to transform the economy. We are creating incredibly powerful information processing entities. As an economist, you can think of a market as an information processing entity. You can think of firms and organizations as information processing entities. If you have a thousandfold or a millionfold improvement in AI, it would be a miracle if firms and markets didn’t also transform and we develop new kinds of organizations to manage work. But we haven’t figured those out yet. So [a] big part of my work is trying to understand, what does the economy look like? [What are the] firms, markets, and maybe some new kinds of entities as AI gets more powerful, as it aggregates information, as it processes information? And then how are we going to manage the concentration of wealth and power that could result?

I’m super optimistic about the potential of technology to boost productivity. I have a bet, a long bet on that with [Northwestern University’s] Robert Gordon, and I’ve written about how I expect productivity to grow. At the same time, I’m pretty worried that if we don’t manage it right, it could lead to a great more concentration of wealth, and that tends to lead to a concentration of power. And that could hurt our freedoms if we don’t figure out an economic system that balances not just wealth creation but also shared prosperity.

Sam Ransbotham: I was looking up some of your background before talking, to refresh, and I saw the phrase mindful optimist, and it kind of hit me. So what is it going to take to get us to this positive version of the future versus the negative version of the future?

Erik Brynjolfsson: Let me take a minute to define mindful optimist, because I have a very specific meaning for that. There’s a lot of people out here in Silicon Valley who are optimists, but I think too many of them are kind of blind optimists. They’re like, “Hey, Erik, don’t worry. It’s worked out in the past. It’ll work out in the future. Just chill.” There are also a lot of people who are what I call blind pessimists. They’re like, “Oh my god, we’re doomed. There’s nothing we can do.” I think both those groups make the same mistake.

It’s the one we talked about at the top of the program, which is they think of AI as something that’s going to do things to us as opposed to us having the agency to control the future. A mindful optimist is somebody who sees a future and then works toward doing it, is mindful about creating it, doesn’t just assume it’s going to automatically happen.

They’re not like kids at Christmas [who] expect presents to magically appear the next morning, but instead they’re more like kids who see a tree, and they see some boards, and they think, “Hey, we could build a tree house there.” Then they’re like, “Well, it’s not going to happen by itself. Let’s figure out how to make it.” And then they make that optimistic reality happen through their hard work and through their imagination. That’s the kind of optimism that I would like to see. I don’t see enough of it. I see way too much just passivity.

I gave the closing lecture to my class here at Stanford last Friday, and one of the things I told them was every time they hear … “AI” they should think of amplified intention, that AI is the greatest amplifier of intention ever. Whatever they want to achieve, AI can amplify it, but it’s not going to happen without that agency, without that intention.

So that’s a message I want to have for the broader world: “Let’s think about what our values are, what kind of economy we want to shape, what kind of shared prosperity we want to create.” And I mean that for everybody, not just the technologists and sending that message to them, but also to policy makers, also to citizens, to workers, to managers. Let’s think about really actively using this.

Over the next 10 years, I expect the world is going to radically change. But there’s no one inevitable future. [When] you cross the border between, say, [the] United States and Mexico or other countries, you can see that different institutions lead to very different outcomes, different levels of wealth and democracy, and that’s the same for the future. We can live in a lot of different possible futures. One of the things I’m doing here at the Digital Economy Lab is a lot of research to understand which paths are more likely to lead to those beneficial outcomes.

Sam Ransbotham: I think that’s a great way to end here. We [do not] have easy answers in either direction. The gains are real, the harm is real, and which one wins is a choice that we’re making now and something that we have control of.

Erik Brynjolfsson: More than ever. One of the interesting things is this is the time for philosophers and for humanities and for economists and people who think about what kinds of values we want to instantiate in the world. With this greater power comes greater responsibility and more potential to change the world. So we really need to think consciously about what kind of world we want to shape going forward. I don’t think there’s been enough attention to that, but the Digital Economy Lab is in part focused on that.

Sam Ransbotham: Thanks for bringing the numbers and the nuance. We’ll see you in Boston this summer.

Erik Brynjolfsson: Absolutely. Looking forward to it, Sam. Great talking to you.

Allison Ryder: Thanks, everyone, for listening today. We’re off for summer break, and we’ll be back in the fall with an exciting lineup of speakers from Instacart, GoFundMe, Dropbox, and Honeywell. Please join us then.

In the meantime, it helps our show a lot if you leave us an Apple Podcasts review or a Spotify rating. If our show has really impacted your life and work, please drop us a line. You can reach us at smr-podcast@mit.edu. We’d love to hear from you.

Balancing the daily grind of operations with the spark of innovation is one of the most persistent challenges managers face.1 Regulatory compliance, performance metrics, and the never-ending demands of customers can easily crowd out creative thinking. Work overload or mismanaged operations elevate employee stress, which directly constrains ideation capacity. This cognitive bottleneck triggers a cascade of quality incidents that require downstream intervention, further inflating workloads. The result is a self-reinforcing loop, where the lack of front-line ideas accelerates systemic stress.

Of the 150 hospital front-line workers we surveyed recently, over 80% said they wanted to engage in innovative activities. But fewer than half felt that they had that opportunity, and 70% said that they rarely or never communicate their ideas. Front-line employees often work under immense pressure caused by stressors such as regulations, staffing shortages, and unyielding time constraints. In that context, creativity can feel like a luxury — or, worse, a risk. And yet, as research has shown time and time again, the people closest to the work — front-line employees — are often the ones best equipped to improve it.2

Even as artificial intelligence increasingly shapes innovation processes, the deep contextual knowledge, empathy, and creativity of front-line employees remain indispensable to meaningful innovation. Equally critical — yet increasingly endangered—are middle managers. Although many global firms are aggressively thinning this organizational layer in anticipation of an AI-driven future, middle managers play an essential role in fostering their front-line teams’ innovation.

Our research on effective behaviors in nursing departments at several maximum-care hospitals — where new ideas can literally save lives — demonstrates how top and middle managers can help front-line workers strike a balance between innovation and day-to-day tasks.3 A critical success factor is the alignment of top and middle management around a shared ambition: the effective use of their respective roles to shape employees’ innovation identity and confidence. Below, we offer concrete recommendations and highlight common managerial pitfalls to avoid.

When There’s No Reward for Innovation

Consider the following anecdote from our interviews. When Sam, a hospital nurse, designed a new “intensive care diary” for families and patients recovering from critical illnesses, he wasn’t trying to launch a grand innovation project. He just saw that something was missing. “I thought it could help people process what they’ve been through,” he explained. “The team was open to it — some even said they’d love to try it.” Sam developed a ready-to-go concept, printed several prototypes, and even planned a short workshop to introduce it to colleagues during overlapping shifts.

Then the idea stalled. His unit’s management showed little interest, fearing that implementation of his idea might escalate into a larger project. The underlying message conveyed a disregard for his creative initiative, leading to a missed opportunity to actively shape the employee’s understanding of his creative role. Worse, it discouraged further contribution, never mind the motivation to go “above and beyond.”

For example, Sam knew that the repetitive, time-consuming checks he and his cardiology intensive care unit colleagues had to do each night to ensure that each emergency crash cart was complete and organized were largely unnecessary. And he believed that the carts could be sealed with numbered tamperproof tags so that some nightly checks could be skipped. The materials were there. The process was simple. And yet, Sam hesitated to suggest the change.

Seeing no benefit in voicing further ideas, Sam stopped doing so, leaving his potential untapped. “I still think it’s a good idea,” he said, laughing softly. “Same with a few other things — like improving the way we order medications or organize bed assignments. I just don’t bring it up anymore.” And every night, he and his nursing colleagues continued to spend (waste?) time checking crash carts.

Sam’s hesitation reflects a common tension: Employees see opportunities for improvement every day but are unsure whether acting on them is part of their role, especially if initial initiatives were not supported. The subtle hesitation — the space between noticing and acting — reveals a deeper issue: When employees don’t feel empowered to recommend improvements, or don’t feel accountable for them, creativity fades. Innovative ideas dwindle, not due to lack of interest but because of the absence of organizational structures and managerial support that validate innovation as a core capability.

Middle Managers: The Overlooked Innovation Engine

How can managers help workers strike a balance between high operational efficiency and necessary creative learning? It requires that employees develop a robust innovation identity — the understanding that generating ideas is an essential part of the job. Lacking this sense of identity, one worker explained, “It’s not in my job description to change things. My job is to do my work, go home, and everything’s fine.”

Even when employees feel psychologically safe and know they won’t be punished for speaking up, they still may not see creativity as part of their role. The question, then, becomes “What can make employees see it differently?” The answer often starts with their supervisors.

Middle managers are decisively close to front-line employees and play a large role in their sense of identity as innovative problem solvers or mere task-doers.4 Cutting management layers to “streamline” operations risks removing the very people positioned to encourage innovation. Through everyday interactions, such as encouraging inputs, backing small experiments, and recognizing contributions, middle managers can quietly build — or erode — their employees’ innovation identities while establishing idea generation as a regular and essential practice.

While having too many management layers can breed bureaucracy, removing middle management altogether can leave front-line innovation without its most important catalyst: a manager close enough to care and credible enough to champion ideas upward. AI is taking over more tasks but lacks deep, situated, tacit knowledge derived from moment-to-moment operational friction between humans. Front-line employees like Sam are oftentimes the only source of nuanced, contextualized insight necessary to identify truly nonobvious problems and generate targeted, human-centered solutions.

How to Nurture Middle-Manager Innovation Champions

Middle managers are often considered the bridge between strategy and execution, and rightly so. They translate vision into daily practice, turning abstract goals into concrete tasks. But our research shows that top managers and senior leaders play an equally instrumental yet easily overlooked role: They support middle managers’ nurturance of front-line innovation.

When senior leaders clearly connect front-line ideas to the organization’s mission — linking, for example, nurses’ suggestions directly to patient safety or operational efficiency — they shift the perception of creativity from “nice to have” to “essential.” Their role is not to micromanage but to enable innovation: providing clarity, trust, and structural support so that creative work becomes part of “real work,” not an optional add-on. One executive put it bluntly: “People always look at the leader: Do they support it? Do they care? Leadership is the decisive factor.”

Many senior leaders encourage their teams to be bold and experimental but fail to recognize that their own behavior sets the tone. If top management clings to old habits, so will everyone else. Authentic leaders demonstrate a growth mindset while simultaneously acknowledging the discomfort of operational gaps. Spotting glitches, working on them, and learning from the experience are not signs of weakness but of growth. This modeling allows middle managers to internalize a similar mindset and, in turn, further foster it.

Many organizations, however, underestimate how long it takes to build a culture in which people are not only confident to develop and communicate improvement suggestions but believe that they are expected to. One leader reflected that his organization had spent five years trying get people to understand that they wouldn’t be punished for experiments and ideas that didn’t pan out. When such a message is repeated consistently and authentically, it lays the foundation for innovation. Middle managers can acknowledge the potential of front-line ideas and be empowered to take charge, experiment, make mistakes, and continuously improve, contributing to a culture where creativity can thrive.

When Small Nudges Lead to Big Ideas: Practical Guidelines for Leaders

In balancing daily operations and innovative initiatives, timing and tactics matter. Overload your managers with new (top-down) initiatives, and creativity can collapse under the weight of “too much.” Respecting the role’s “sandwich” position means leaving enough time for mid-level managers to actively engage with their direct reports in constructive and innovative ways. One executive described the art of subtle prompting this way: “Leaders need to provide strong support to ensure that employees are allowed to participate in projects and aren’t constantly pushed back into their daily routines. It’s the supervisors who must create that framework.” Great leaders sense when to step in and when to step back, and make space for creativity without making it feel like yet another obligation.

Recommendations for Middle Managers

These actionable recommendations can help managers use their unique role to help front-line innovation thrive, by shifting from mere supervisors to coaches of problem-framing and champions of micro-experimentation. By engaging in the behaviors below, middle managers can spark a virtuous cycle that builds an appreciative culture over time and allows creative ideas to emerge as team members internalize ideation as part of their job. This shift in self-perception drives consistent idea generation, which in turn provides proof of ability and impact, further strengthening employees’ front-line innovation identities.

Allow small-scale experimentation. Use a “local-first” filter to prioritize ideas that are independent and testable at the local level. Fast-track these small, testable, and independent suggestions, such as Sam’s crash-cart tagging system, for immediate, small-scale testing. Resist the urge to immediately scale complex ideas.

Establish protected exploration. Actively carve out and protect dedicated “idea spaces” separate from daily operational demands. Use structured problem-framing discussions in team meetings: Identify problems in a first step, and then link front-line employees’ capabilities to potential solutions.

Apply process rigor. Commit to providing a concrete next step or reasoned feedback for every idea quickly, ideally within a few days. Ambiguity or slow response times can curtail new initiatives while eroding employees’ innovation identities.

Set the example of learning from failure. Ensure that every small-scale experiment that underperforms or fails is followed up with a formal, structured debrief: What did we learn? How does this inform the next iteration? It’s crucial to omit any mention of blame. Learning from experiments encourages a positive culture that accepts mistakes as necessary steps on the path to successful improvements.

Recognize and reinforce new ideas. Provide personalized, nonmonetary acknowledgment — such as a personal note or public recognition — for the act of voicing an idea, regardless of its immediate viability. The majority of employees in our study were not primarily driven by monetary rewards; they sought recognition and to be taken seriously. They valued the opportunity to contribute meaningfully to organizational improvement.

Recommendations for Senior Leaders and Top Managers

Institutional support is indispensable in shaping how middle managers cultivate front-line innovation, and top-level leaders must deliberately direct support toward empowering the organization’s middle layer.

Intentionally support and train middle managers. Provide the necessary resources — including dedicated office blocks, leadership training and coaching, and structural support — to ensure that all managers have the capacity to dedicate time to develop their team’s innovation potential. Innovation support must be an expected, institutionalized, funded function, not a secondary duty.

Institutionalize knowledge transfer and strategic coherence. Promote regular exchange sessions, such as focused meetings between department or function leaders, to share proven approaches and ideas for fostering staff innovation. By structurally supporting this exchange, your organization can establish the appreciation and formal institutionalization of front-line ideas.

Foster strategic coherence. To ensure that ideas across the organization are evaluated fairly and transparently, clearly link front-line ideas to the organization’s mission; in hospitals, for example, that might include operational efficiency and employee safety. Without this common thread and objective criteria, an employee’s idea realization — and its potential impact — may rely on chance and individual heroics.

Model discomfort. Senior leaders must openly model the discomfort and learning inherent in change, just as middle managers must for their own teams. Publicly celebrate successful experiments, but also openly discuss the lessons learned from initiatives that failed, demonstrating that risk-taking is valued. Use lessons learned to carefully teach middle managers how to walk the thin line of taking risks while avoiding unnecessary operational disruption.

The System Behind the Spark

Front-line innovation rarely happens in solo, light-bulb moments. It’s more like a relay race, with ideas passed from the front lines to middle managers, then to senior leaders and back again. Each handoff requires clarity, trust, and good timing. In complex settings, the nimble ingenuity that front-line employees bring cannot be fully replaced by AI, because such innovation depends on hands-on operations and exposure to real-life processes.

Yet that involvement creates a fundamental tension: keeping operations running while also generating ideas to improve them. Middle managers have a critical role to play in helping front-line employees strike the right balance and while gaining a sense of innovation identity and confidence. In turn, senior leaders must enable these enablers. By setting a clear purpose, granting managers autonomy, and signaling consistent support, they can shape how middle managers lead and foster innovation within their teams.

When this system works, people like Sam no longer stay silent. They speak up because they understand that contributing ideas is part of their role — an individual shift that enables organizations to move beyond managing everyday operations to shaping the extraordinary.