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.

As multinational companies implement artificial intelligence workflows and look to adopt AI across their global operations, they are increasingly running up against country-specific regulations and policies that aim to govern AI and align its use with national priorities and local cultural norms. These regulations and policies — which fall under the umbrella of “sovereign AI” — govern where data is stored and processed, whose infrastructure is used for training and operating AI models, and how algorithmic decisions are reviewed and enforced in a given jurisdiction. Many markets are now developing their own sovereign AI frameworks to reduce dependence on the United States and China, where nearly 70% of leading AI models originated.

This creates a strategic dilemma for multinationals. Relying on global AI platforms maintains operational consistency but deepens exposure to geopolitical disruption and local market access risk. Localizing data, infrastructure, and models earns regulatory trust but incurs significant cost and complexity when a company operates across dozens of jurisdictions with differing requirements. The challenge is that policies vary significantly by country and are evolving rapidly, making a single global AI strategy untenable, and fully independent local systems impractical.

Most companies are responding defensively, treating sovereign AI as a compliance obligation managed by legal or IT teams. Our December 2025 survey of 1,928 executives across 28 countries reveals a striking gap: Sixty percent of respondents said that rising geopolitical risk makes them more likely to pursue sovereign technology solutions, yet only 15% have made AI sovereignty a CEO or board-level priority, and fewer than 13% see it as a growth driver rather than a cost.

In this article, we argue that sovereignty is better understood as a continuum of choices and that the companies best positioned to scale AI globally are those that treat those choices as a source of competitive advantage rather than a constraint to be minimized.

The Sovereign AI Landscape

The regulatory landscape governing AI has shifted significantly in recent years, from relatively narrow data residency rules to a far broader set of requirements governing models, infrastructure, and how algorithmic decisions are made and enforced. Most major markets are now developing their own sovereign AI frameworks, resulting in a patchwork of locally governed ecosystems, each with distinct rules, data standards, and expectations for responsible use.

The complexity is already visible at every layer of the technology stack. Companies operating in European Union member states must conduct formal risk assessments, maintain detailed technical documentation, and submit it to national market surveillance authorities under the EU AI Act. (The AI Act is now in active enforcement, with its most comprehensive requirements for high-risk AI systems taking effect in August 2026.) Simultaneously, they must comply with prior standards and policies, like GDPR (General Data Protection Regulation), NIS2 (Network and Information Security 2), and DORA (Digital Operational Resilience Act). Companies must also prepare to align with the sovereignty package announced by the European Commission in June 2026, which includes two legislative proposals and a strategic road map to bolster the EU’s AI sovereignty.

Meanwhile, companies looking to do business in Saudi Arabia must navigate strict data localization requirements — including obligations to store nationally sensitive data within the country — alongside cross-border transfer rules that require adequacy assessments or contractual safeguards, all within a governance framework that is still taking shape. There is no dedicated AI law, and binding obligations currently flow from data protection and cybersecurity regulations rather than AI-specific legislation. The same governments driving these requirements are also pouring billions of dollars into building the infrastructure and incentives to enable sovereign AI solutions.

Companies have started to develop strategies to navigate this fragmented landscape. We know from our consulting work that three global banks are rethinking their tech strategy in the EU: They’re limiting further migration of sensitive systems into foreign public cloud systems and instead building a shared platform in their home countries.

In the U.K., senior leaders of multinational banks are exploring a domestic alternative to Visa and Mastercard to reduce reliance on U.S.-owned payment networks. These are early signals of a structural shift in how multinationals must think about AI infrastructure. This raises an urgent question for CEOs: How do you scale AI globally when the rules governing it are local, fragmented, and still being written?

To answer this question, CEOs need to make three strategic choices: where accountability for decisions should sit, how much sovereignty their operations require, and which external partners can help them execute.

1. Make sovereignty a strategic priority.

The first and most urgent CEO decision is raising AI sovereignty to the level of a strategic concern. Our survey found that most organizations delegate decisions regarding AI sovereignty to chief data/AI officers (37%) or compliance/risk officers (28%), while only 15% of organizations have made it a CEO- or board-level priority. When sovereignty sits in IT or compliance, it results in fragmented decisions across business units, inconsistent approaches across markets, and missed opportunities to turn sovereignty into local advantage. Sovereign AI is not an IT architecture choice. It is a strategic bet involving geopolitics, capital allocation, supply chain resilience, and long-term competitiveness, and the decisions it requires can be made only at the top.

What does that look like in practice? Consider BNP Paribas, one of the largest banks in the EU. Since 2023, it has built a deepening partnership with Mistral AI, Europe’s leading sovereign AI model provider, culminating in a groupwide multiyear agreement in 2024 and a renewed three-year extension in 2025 covering software, co-development research, and on-premises deployment. The bank also backed Mistral financially, participating in both its 385 million euro ($445 million) funding round in 2023 and its $640 million Series B in 2024. The partnership is driven by the C-suite with sovereignty as a key consideration. Keeping AI on-premise, under the bank’s direct control, ensures sensitive data stays within European regulatory jurisdiction. Such decisions — say, which AI ecosystem to depend on, which regulatory frameworks to operate within, how to manage geopolitical exposure — carry implications for capital allocation, competitive positioning and long-term resilience. They belong on the C-suite and board agenda.

2. Treat sovereignty as a continuum.

As such CEO-level choices demonstrate, sovereign AI does not require full independence or the wholesale replacement of existing systems. CEOs must recognize that sovereignty is a continuum. Depending on the industry, national context, and specific use case, aspects of the company’s technology stack may need varying degrees of adjustment to meet sovereignty concerns. Only about one-third of all executives we surveyed believe that their AI workloads require any such sovereign adjustment. Those actions can involve anything from implementing targeted safeguards around data residency or legal oversight to replacing entire elements of the technology stack, such as models or infrastructure, to satisfy sovereignty requirements.

The degree to which sovereignty matters to the design of a company’s AI system rests on three considerations.

The first is industry risk. Defense, health care, energy, and financial services are more sensitive industries because AI systems have implications for national security, citizen safety, and economic stability. Governments are likely to have greater AI sovereignty concerns for companies operating in those domains. In contrast, retail, tourism, and consumer services can often rely on global platforms when strong local safeguards are in place.

The second is national context. Countries pursue different strategies shaped by geopolitics and economic priorities. China has built a full-stack China-for-China model. Singapore emphasizes interoperability and cross-border trust. The U.K. and much of Europe favor hybrid approaches that combine global platforms with selective local control. Companies operating across these markets must design for variation.

The third and most decisive consideration is the use case. Even within an industry, AI that is used in high-stakes decision-making, such as credit decisions, medical diagnostics, or energy grid optimization, carries far greater risk than AI used in contexts like customer service, marketing personalization, or internal productivity. High-stakes use cases call for greater scrutiny around data governance, model transparency, and regulatory exposure.

All three considerations are relevant to AstraZeneca, a global pharmaceutical company. Pharmaceuticals is a sensitive industry, so AI use carries high stakes by default. But AstraZeneca calibrates its sovereign controls deliberately by national context and use case. In China, adverse drug reaction data must be reported to the National Medical Products Administration and remain within the country under strict governance. To address this requirement, AstraZeneca is using Alibaba Cloud’s sovereign infrastructure to deploy local AI models, such as the Qwen large language model, for pharmacovigilance in a private, locally controlled environment.

Outside China, AstraZeneca made a different calculation. For R&D and clinical development — work that is sensitive but not subject to the same regulations as pharmacovigilance — it uses the Amazon Web Services (AWS) public cloud to run large-scale AI and machine learning, including multi-agent systems that enable scientists to query complex biomedical data and generate insights in seconds. The same company, facing different sovereign AI pressures in different contexts, arrived at two different infrastructure decisions.

3. Build hybrid sovereign ecosystems.

Few organizations can, or should, build the full AI stack independently. Our survey found that 55% of organizations plan to use a mix of global and local AI providers, reflecting a shift from dependency toward flexibility.

CEOs must adopt a tailored mix of global and local AI providers, depending on use cases, the regulatory landscape, and risk tolerance.

For organizations seeking global scale, partnering with hyperscalers such as AWS, Google, Microsoft, and Oracle is an attractive option. In software and platforms, Oracle’s EU Sovereign Cloud and Microsoft’s Delos Cloud partnerships are enabling European companies to run sensitive workloads fully under EU law.

When earning local trust is a priority, partnering with country-endorsed national champions may be the right strategic move. Indosat Ooredoo Hutchison — Indonesia’s trusted national telco, jointly owned by Qatari and Hong Kong-based investors — is building Indonesia’s first sovereign AI cloud with international partners Accenture and Nvidia while ensuring that national data stays onshore to support local startups and government clients.

Companies that want to avoid full dependence on hyperscalers can access specialized capacity from AI-native players like Nebius, CoreWeave, and Lambda. These providers build infrastructure specifically for AI workloads, allowing companies to access high-performance compute more efficiently, often with regionally deployed capacity that meets local regulatory or data requirements. Helical, a Europe-based biotech company, trains its large-scale biological AI models on Nebius’s infrastructure. Nebius is headquartered in Amsterdam and operates data centers across Europe, giving it the compute performance it needs while keeping data within regional boundaries.

When the deepest level of sovereign control is required, and where competitive advantage comes from shared capability rather than proprietary infrastructure, federated consortia offer something other models cannot. The OpenBind Consortium, for example, received 8 million pounds ($10.8 million) from the U.K. government’s Sovereign AI Unit to bring together a group of partners that includes the University of Oxford, Diamond Light Source, and MedChemica to build a nationally governed data set for AI-driven drug discovery that is 20 times larger than earlier efforts. The real advantage, however, lies in the sovereign governance model: It allows companies to collaborate, share risk, and scale AI with confidence — something fragmented, ad hoc data sets cannot deliver.

The Strategic Imperative

Most companies focus sovereignty on the data and cloud layers of their tech stack. Our survey found that while 60% apply sovereignty controls to data and 46% to infrastructure, only 22% extend them to AI models themselves, leaving a critical layer exposed. As AI systems become more autonomous and agentic, the decisions and actions that matter most will increasingly happen at the model and agent layers.

This doesn’t mean that every company needs full-stack control. Companies need to apply sovereignty to the layers that matter most for their highest-stakes use cases.

In a world of persistent geopolitical uncertainty, sovereign AI has become a competitive capability.

Companies that treat it as a strategic design choice rather than a compliance obligation will shape how AI evolves in their markets. Those that don’t risk discovering too late that the intelligence driving their most critical decisions is no longer under their control.

Digital platforms and generative AI have lowered the barriers to accessing global talent, capital, and knowledge for companies everywhere while making it possible to reach customers across languages and cultures. Research my colleagues and I have conducted suggests that such tools make it easier for entrepreneurs to serve global markets and for investors to evaluate startups from afar.

Access to those technologies should result in a leveling of the global playing field that allows new ventures to thrive anywhere. Promising early-stage startups are emerging in areas like Jakarta, Nairobi, Kyiv, and São Paulo. But when it comes to scaling, the old pattern remains: Companies that scale and become category leaders are disproportionately concentrated in a handful of traditional hubs, such as Silicon Valley, while early-stage companies outside of them struggle to scale into larger businesses. Technology, it appears, is not enough to overcome the barriers to scaling.

When Technology Reduces Some Gaps — but Creates Others

Even as digital technologies reduce structural differences across locations, some companies respond to the new opportunities they create in ways that undermine the ability to scale. When entering new markets or adopting new technologies becomes as simple as a click, businesses can fall into one of two traps: either chasing every available opportunity or defaulting to what is closest and most convenient. Both responses can systematically disadvantage companies outside major hubs.

The first trap stems from the urge to go global before the company is ready. Today, companies can attract users from around the world with a single post on a digital product platform. As a result, many organizations — especially those in smaller markets under pressure to show global traction — try to pursue multiple global markets at once. But in doing so, they often overlook a critical resource: early users whose feedback they could more easily interpret because they share a common background or geography. My research shows that business leaders can more easily recognize the demand signals of local users and, as a result, learn more effectively about their company’s nascent product and refine it before expanding further.

Generative AI is making market expansion more complex, particularly for companies based outside English-speaking hubs. Research I conducted with colleagues shows that while GenAI helps high-quality ventures in these contexts stand out by improving the pitches of expert entrepreneurs more than those of non-experts, it disproportionately enhances pitch quality in English-speaking environments. As long as investors and customers rely on pitches as an input, ventures in non-English-speaking hubs may continue to face disadvantages when entering hub markets. This challenge is particularly acute for non-hub firms because they rely more heavily on text-based signals to reach global audiences.

Yet overcorrecting to avoid this trap can lead to the second trap: defaulting to what is close and familiar. This is particularly evident in startups’ technology choices. The global boom in tech entrepreneurship has dramatically expanded the set of available tools, many of which have the potential to accelerate adopters’ growth. At the same time, tool vendors are increasingly relying on GenAI to craft persuasive product descriptions, meaning that a well-written pitch is no longer a reliable signal of a tool’s value. This makes it harder for potential buyers to distinguish tools that will accelerate growth from those that are merely well packaged. When managers face too many persuasive options, they often fall back on simple rules — like choosing to adopt tools that were locally developed or are already familiar to them. Indeed, in research I coauthored, judges evaluating many polished pitches in a startup accelerator competition favored ventures from their own regions — even though the judges were no better at assessing local ventures’ quality — and passed over 1 in 20 promising startups as a result. The same bias shapes how companies choose their technologies.

Defaulting to what is local as a heuristic can particularly penalize companies in remote markets. These companies often encounter fewer locally developed or locally targeted tools — partly because tool providers themselves face pressure to cater to hub-based customers. Ongoing research by my colleagues and me shows that, as a result, genuinely useful technologies often go unseen, are misunderstood, or are deprioritized by the companies that could benefit most from them.

In this way, AI can unintentionally reinforce geographic disparities rather than eliminate them unless companies bring something that technology alone cannot provide: strategic focus.

Strategy as the Missing Equalizer

Prioritizing what is nearby can narrow the opportunity set for companies in remote locations because frontier innovations are often concentrated in hub markets. At the same time, pursuing every distant opportunity — which is often encouraged via external investor pressure — can diffuse scarce resources and weaken execution.

The solution is strategic clarity: a clear articulation of how a company intends to combine local and distant opportunities to create a differentiated position in the market. This begins with a single question: What is your competitive advantage? Are you serving a market that competitors have largely ignored? In this case, your advantage may lie in access — bringing a technology or use case to customers who have been overlooked. Or are you competing in an established market by offering a superior solution? Here, the advantage may be quality — delivering better performance than existing alternatives.

This distinction has direct implications for subsequent technology and market choices that can either widen or narrow the global scaling gap. When a company’s strategy centers on serving an underserved local market that it knows exceptionally well, the key may be to adapt an existing technology to the needs of that market.

For example, Grab and GoJek, founded in Malaysia and Indonesia, respectively, adapted the ride-sharing model pioneered by Uber to conditions that a hub-based competitor wouldn’t have easily been able to read or replicate: cash payments in largely unbanked markets, motorbike taxis suited to dense urban traffic, and an eventual expansion into food delivery and financial services that matched how people in the region lived and spent. Lack of local knowledge became a barrier to entry for global competitors.

When the advantage is quality, the strategic imperative is different: Identify distant markets where demand for a superior solution is strongest, and draw on differentiated local assets that hub-based competitors cannot easily access — such as exceptional software engineers or exclusive access to local university research labs — to deliver it. 

Grammarly, which has its roots in Ukraine, illustrates this logic well. Its founders used the country’s deep pool of local developers to build a technically superior writing-assistance tool — harnessing a local talent advantage that hub-based competitors couldn’t easily replicate. Its value proposition meant that its target market didn’t have to be local; they could go after global professional and academic users concentrated in English-speaking markets. Spotify similarly leveraged its exceptional local internet infrastructure in Sweden to create a product that targeted and ultimately transformed the global music industry.

In this way, strategic clarity transforms the search for technologies and markets from a reactive scramble to a deliberate capability that is available to companies everywhere, not just those situated in hubs.

The practical question is how to build this strategic clarity. Start by asking yourself four essential questions:

1. What is our core value proposition? How does your offering improve customers’ lives, processes, or outcomes? What makes it difficult for competitors to replicate it?

2. What is the target market that benefits most from this value proposition? Are you addressing an underserved segment — perhaps close to home? Or competing for customers in an established market, potentially abroad? Strategic focus begins with clarity about who gains the most from what you offer.

3. How will we deploy technology to reinforce our competitive advantage? If you are targeting an underserved market, harness your unique local knowledge to adapt globally available technologies to needs others have overlooked. If you are targeting established markets abroad, draw on differentiated local assets that hub-based competitors cannot easily access or replicate.

4. Where should we begin? Early adopters should resemble your eventual target customers while also providing feedback you can clearly interpret. For example, if the local market, where you can get clearer feedback, resembles the target market, start there; if not, start with the target market. The right testing ground generates learning signals — not just early traction.

Digital technologies, including AI, are not automatic solutions to geographic disadvantage. Without strategic clarity, they can just as easily widen the gap as close it. Grab, GoJek, Grammarly, and Spotify were able to achieve scale because they understood and exploited what made them different. With strategic clarity, location becomes a choice rather than a constraint, and technology becomes what it was always meant to be: not a substitute for strategy but a powerful amplifier of it.

To understand their customers and markets, a growing number of customer-oriented companies are using generative AI tools, alongside the language and reasoning capabilities of popular large language models (LLMs), to access and analyze their own internal content. These hybrid knowledge approaches, which typically employ a technique called retrieval-augmented generation (RAG), allow the integration of a company’s own customer insights with the general knowledge base on which an LLM was trained. Companies taking this approach to what was previously called “knowledge management” reap several benefits, such as enabling employees to access and summarize content in natural language. That capability is particularly important in large organizations — where employees searching for insights often have no idea where those insights originated or how they might be found.

Organizations gather and attend to insights about what their customers want, how they want to be sold to, and what products and services they have interest in. Customer insights typically originate from market research departments or external market research agencies, but they can also be found in sales interactions, customer letters and emails, website behaviors, social media interactions, customer service tickets, purchase patterns, focus groups, and other channels. It can add up to a lot of structured and unstructured information, so tools to help summarize, categorize, store, and access it certainly make sense.

However, companies that focus exclusively on storing and accessing knowledge are making the same error that many organizations made in the earlier generation of knowledge management: That focus is too narrow. Instead, companies should also address knowledge flows — how customer and market insights are created, analyzed, stored, and accessed. New technologies — using generative AI to at least some degree — can assist with all of those steps.

While earlier tools for knowledge management (such as Lotus Notes and Microsoft SharePoint) allowed broad access to customer and market insight content, many organizations did not experience a revolution in insights access and usage. The technology provided more access, but cultural challenges — difficulty in organizing and retrieving the knowledge, indifference to the content, organizational silos and overlaps, lack of collaboration with external agencies — often prevailed. Today, organizations still face those same cultural obstacles when tackling knowledge management.

To learn how generative AI can and can’t help leaders beat knowledge management obstacles, we spoke with customer and market insight specialists or leaders at eight different consumer-oriented companies. Some of these leaders had responsibility for creating and overseeing the generative AI system; others had broader responsibility for market research or insights-oriented technology. We also spoke with several vendors of GenAI-based technology for customer and market insights, and one market research agency that makes extensive use of GenAI-based qualitative research technology.

How Generative AI Tools Help

Companies can combine GenAI tools with customer and market insight content on their own (though it may be difficult), use external software vendors to simplify the work, or use a combination of both approaches. Procter & Gamble, for example, uses vendor-supplied software for knowledge storage and access but has created its own system for GenAI-based analysis and categorization of the content. As Kirti Singh, P&G’s chief analytics, insights and media officer, noted, this way they get “sharp, pointed answers from GenAI” rather than just links to documents.

Among the tool vendors, focus differs. Some primarily focus on the storage of and access to insights, but the tools’ value goes beyond providing simple automated virtual filing cabinets. Their functions include automated curation of documents, integration of diverse content types, on-demand analysis done in response to queries, and synthesized answers to prompts. This type of tool, however, assumes that data analysis has already been done and that insights are waiting to be found. Analysis of quantitative data is done by analytical software, and the capacity to analyze qualitative data is limited.

Other vendors concentrate on the analysis of qualitative customer data and documents for customer insights. Still other vendors emphasize rapid testing of consumer responses to advertising. Today, there is overlap among these categories, and most vendors are attempting to address the broad process of identifying or creating customer insights, curating and categorizing them, and making them available for later access. We expect that at some point, broad customer insights platforms will emerge, employing generative AI and other capabilities to address the entire process.

If the vendor’s primary function is insight storage and access, the approach typically involves adding the customer’s custom and proprietary content on top of large language models. The content stored can include structured data (quantitative market research results, spreadsheets, or customer satisfaction ratings, for example) and unstructured data (such as transcripts of interviews, social media comments, or focus group results) from both internal and external sources.

In most cases, organizations pursuing this route centralize as much customer and market content as possible in one system. Curation is required to reduce content overlaps, eliminate obsolete/irrelevant documents, and generally maintain quality. As one customer insights leader told us, “AI is only as useful as the data it learns from.” To make sense of insights, the GenAI tools typically must tackle categorization, summarization, and content tagging. Tagging the content makes it more likely to be retrieved later. Some companies use manual tagging, while some systems employ GenAI-based tagging using a predefined taxonomy.

Novartis offers an example of a company successfully revamping insight storage and access using GenAI tools. Working with an external vendor, the company developed a customer and market insights system, called Sherlock, for its consumer business. After users pose questions, the system gives answers by pointing to a specific line of text or a time stamp in a video. Sherlock also incorporates expert-curated microsites, known as Knowledge Zones, on particular topics, such as packaging. Users who add content to the system must adhere to strict governance guidelines about document formats and quality. Novartis’s research vendors can upload project deliverables directly into Sherlock.

The system helps Novartis avoid spending on redundant insights services across its business and helps employees find relevant insights quickly, without overgeneralization. (For example, it could flag results that were based on patient data from Europe only, using a feature called WatchOut.) The results have added up: Novartis saved more than $29 million in primary market research costs in just one year. Such use of GenAI to enhance insight storage and access can facilitate the democratization of information by helping employees find both knowledge and knowledgeable people.

Qualitative Data Analysis: A Special Problem

For a long time, the ability to do qualitative data analysis — a messy business — has not been included in off-the-shelf analytical tools. Specialized software for this purpose has mostly been used in academic qualitative research; historically, most market researchers have conducted semi-manual analyses, using spreadsheets. GenAI tools offer an alternative to this extremely time-consuming qualitative analysis work.

A recent academic article argued that GenAI is unsuited to qualitative analysis, but our analysis suggests that this is true only of generic AI chatbots. More specialized tools have other capabilities that make them suited to effective qualitative data analysis.

Tracy Tuten, who leads qualitative research at market research agency Illuminas (now part of Radius Insights), became an early adopter of a vendor’s generative AI software in order to mine customer insights. Tuten, who has taught market research at several universities, refers to this approach as “conversational qualitative data analysis.”

Via GenAI-based software, Tuten uses natural language prompts to analyze qualitative data from interviews and focus groups. The system lets her upload audio and video files for automatic transcription, generate summaries, surface themes, and compare them across audience segments. A large-scale qualitative project such as a global study with 30-plus interviews might have taken six weeks to analyze in the past but can now be synthesized in a day, Tuten said. The tool also lets her surface secondary insights that she might have missed in the unstructured data. Tuten often uses the software collaboratively with clients in workshops, enabling faster, more participatory insight discovery.

Given that many qualitative researchers previously relied on spreadsheets and manual cut-and-paste coding to analyze data, this AI-based approach represents a major advance in efficiency and rigor. However, uncritical use of generative AI or other forms of AI may have significant shortcomings. Conversational qualitative data analysis does not replace the researchers; it only augments their performance.

PepsiCo makes extensive use of software for creating customer and market knowledge, including both structured and unstructured data. The company has particularly focused on determining how customers respond to specific advertising campaign and brand messages. But that isn’t the only application the company has employed. In an interview with us and in The Consumer Insights Revolution, a book describing a transformation of customer insights at PepsiCo, Stephan Gans, senior vice president and chief customer insights and analytics officer, described the company’s “platform” for marketing research, called Ask Ada. It includes:

• The ability to test new creative content on real or synthetic customers.
• A data repository on the results from advertising, influencer, and other types of campaigns.
• Social listening capabilities.
• Predictive modeling.
• Knowledge management of customer insight lessons, present and past, including meta-learning.
• A conversational interface to all Ask Ada content.

Gans also credited Ask Ada with reducing PepsiCo’s dependence on external agencies and consultants.

Why AI Isn’t Enough: Four Challenging Factors

Despite these new capabilities, a GenAI tool cannot replace a marketing leader for strategy work. As Gans noted, “Raising the bar on marketing and innovation effectiveness to fuel commercial excellence will become increasingly automated. Leading the understanding of consumer demand is much more strategic and still requires humans.”

As we discovered in our research, several important issues inhibit the ability of AI technology to transform customer and market insights. These issues predate knowledge management and generative AI and will cause problems if not addressed. Let’s examine four of these factors, along with examples of organizations that have encountered and overcome them.

1. Geographical and business units lack common approaches.

One global consumer goods company where we conducted interviews had acquired a GenAI-based customer and market knowledge tool from a vendor but thus far had made little progress improving access to global knowledge. The problem was that the company does business in over 100 countries, and country-based units have a large degree of autonomy. There’s no companywide consensus on names for brands, categories, and distribution approaches. “We have pockets of knowledge, but they are very incoherent,” the head of knowledge management for insights told us. “Different people are investing in different things. We have contradicting numbers and outputs — they are all contextualized differently.” No senior executive has attempted to create greater commonality of information and knowledge formats across units; it would be viewed as counter to the company culture. As a result, the generative AI tool is used by only a few geographical units, and the company’s marketers and product developers are unable to learn from each other. The company is attempting to develop a strategy to create a more centralized, global approach.

At PepsiCo, the story is different. When Gans was first named chief consumer insights and analytics officer in 2017, the company had a diverse set of approaches to customer and market insights. But Gans wanted to create “one nation” of market research so that the company’s marketers could learn from one another and share relevant insights. With strong support from the CMO, Gans created the Global Insights Council, which comprised 15 insights leaders representing all regions and central/global capabilities. Today, customer insights are tightly integrated into PepsiCo’s innovation work.

2. Customer and market insights aren’t part of strategy and culture.

Even the best technologies won’t succeed if the organization’s decision makers aren’t ardent consumers of that type of knowledge. Without those passionate consumers of information, a company’s market research efforts will fall flat, our research showed. So culture and change management work will often be required of leaders.

Passion for data runs high at P&G, which is known for its long-term focus on being customer- and market-driven. Indeed, P&G recently celebrated its 100th year of market research; in 1924, the then-CEO asked a researcher to determine why customers were buying Ivory soap. P&G’s Singh told us, “At the heart of everything we do is the consumer. … Our strategy is to provide a superior product experience to our consumers. We employ experimental science, human and behavioral science, data science, and technology platform knowledge to understand our consumers.”

3. Agency relationships introduce data ownership complexity.

Many companies use external advertising and marketing agencies for consumer research. The client/agency relationship may lead to uncertainties and dysfunction involving analysis strategies, interpretations of analyses, and ongoing ownership of the data and results. If agencies end up owning all or most customer and market insights, a company’s employees will be unable to meet customer needs without external help. PepsiCo’s Gans argued strongly that the client company has to own all research results and insights created by agencies on the client’s behalf. He added that it’s not a good idea to own the data but then outsource the learning from it, because employees should apply lessons learned from market research in future campaigns.

However, for consumer-oriented companies that continue to work with agencies, some vendor software can facilitate collaboration between clients and agencies. Both parties can view, edit, and query customer research and produce a variety of outputs.

4. Analytics professionals may be seen as low-status “order takers.”

If that is the case in your organization, that reputation needs to change. At one of the consumer products companies where we conducted interviews, the insights and analytics function always had a library-like focus. Previously, internal customers who were interested in insights had to consult with a researcher or “librarian.” With the advent of a vendor-supplied GenAI tool, the function has been democratized.

However, users of the company’s system still treat it as a library; they don’t contribute much to the stock of insights. Enabling internal customers to serve themselves hasn’t substantially increased demand for the content and analysis. Users also don’t always supply high-quality prompts; they might ask, “What do we know about back to school?” not realizing that the company has decades of market research on the topic. As at Novartis, experts created micro-sites of curated content within the platform, to address particular information-access issues for certain areas. Still, budgets and head counts in the insights and analytics function have been cut in recent years. Similar functions outside the U.S. don’t want to pay for the GenAI-enabled tool, so they take different approaches to customer and market knowledge.

PepsiCo previously had something of an “order-taking” mentality for market research, and researchers were rarely asked to collaborate with the internal customers or help to shape the requests for insight. Research team members had little respect for their roles, and the function was asked to cut its budget several times. When Gans arrived in the leadership role for the function, he and the CMO concluded that PepsiCo was spending hundreds of millions of dollars per year on consumer insights and that it made little sense to do that unless the company were to become more customer-centric. He made a series of changes — including the implementation of a new AI and insights software system and the Ask Ada platform — that eventually made the customer insights and analytics organization well respected and well funded.

Overall, any AI tool should not be considered a replacement for what the organization is already doing well. As P&G’s Singh told us, “We brought together our tradition of being focused on understanding the customer with the latest AI solutions — not replacing, for example, customer home visits, but augmenting them with AI.”

Our study also suggests that the AI-enabled software for managing customer and market insights is evolving rapidly. Leaders are often interested in different features and functions that fit their company’s specific situation. But leaders should be aware that today’s software has limits. In poorly integrated global organizations, humans have created different names for customers, brands, and marketing approaches across geographies; even AI can’t pull together a unified set of customer and market insights in that case. Companies need humans to integrate and standardize that data to analyze it and act on it effectively.

Finally, if a company’s workforce isn’t actually interested in gathering and acting on customer and market insights, no software is likely to change that situation. These are problems that need to be addressed by humans, not AI.

What does artificial intelligence do to creativity? Are generative AI tools making us more creative, or less? Given that creativity is often the engine behind the most successful ideas and ventures, and that 83% of senior executives rank innovation among their top three priorities, understanding how using AI affects human creativity is critical for businesses.1 On the one hand, generative AI can act as a valuable brainstorming partner, enabling inventors and designers to rapidly prototype ideas and concepts. On the other hand, it risks inadvertently constraining creativity by narrowing the search space too early and encouraging users to anchor on AI-generated suggestions that seem “good enough.”

Across four recent studies, our research reveals that the truth lies beyond this simple binary. We have found that although AI can enhance individual creativity, it reduces collective creativity. To explain why this occurs, we should first clarify what we mean by creativity.

From Individual Creativity to Societal Innovation

Scholars typically define creativity as the intersection of novelty and usefulness.2 Novelty is the degree to which an idea or artifact is original or rare, and usefulness is the degree to which it is valuable or effective in achieving its purpose. An idea that is novel but useless or useful but unoriginal is not creative.

While these dimensions capture the creativity of a single idea, the dimension that best captures the creativity of a group of ideas is its diversity. Any collection of ideas may contain a few that are novel to some but obvious to others, or novel yet not useful. But a highly diverse set is more likely to contain a few highly original outliers that are both genuinely original and potentially valuable. This breadth provides teams with more raw material to recombine, compare, and refine over time, yielding products that better match the full range of customer preferences. After all, ideas that initially appear to be impractical can turn out to be breakthroughs once they have been refined or recombined: for example, the “failed” adhesive that became the Post-it Note or the abandoned video game whose internal communication tool became Slack. In other words, creativity requires more than just quantity and quality of output. It also requires diversity of output, where different ideas can spark new lines of inquiry, new speculation, and new seeds that breed new innovations.

By casting a wider net, organizations can guard against premature convergence on safe, conventional options, increasing the odds of surprising, high-impact breakthroughs. This, however, is where our research reveals an interesting paradox. Individually, AI often enhances creativity, particularly by enabling less experienced or less inherently creative individuals to generate more novel and useful ideas. But collectively, AI often “compresses” the idea space. Because many people anchor on similar AI-generated suggestions, outputs converge. A typical output produced with AI assistance is more creative, but the variance of the full set of outputs decreases. In short, even if an AI-inspired idea looks good, it may turn out to be similar to everyone else’s AI-inspired ideas.

For managers, the implication is profound. The challenge is to harness AI’s productivity and quality benefits while preserving the diversity of ideas that fuels long-term innovation.

Impact of AI on Idea Diversity

To understand how AI affects creative diversity, we analyzed four recent studies we worked on that span different creative domains: short-story writing, circular-economy solutions, humor caption contests, and collaborative storytelling. Despite the varied contexts, a consistent pattern emerged: AI assistance improved individual output quality while narrowing collective diversity.

In the first of these studies, two of us (Anil and Oliver) examined how access to AI influences the creative process and the diversity of collective output.3 In this experiment, participants were asked to write short, eight-sentence stories. Some people wrote entirely on their own, while others were given up to five three-sentence story seeds generated by an AI model. Independent evaluators rated the individual creativity of each participant’s story. We also used AI-based text analysis to measure the degree of semantic similarity among the stories, comparing those written with versus without AI assistance.

The results revealed the core tension. We found that AI assistance improved story novelty, especially for writers with a lower baseline level of creativity (as measured beforehand with an existing paradigm, the Divergent Association Task). Yet at the collective level, diversity declined. Stories from the AI-assisted groups converged on more similar beats or structures, showing less variance than those written without AI. (See the story-writing graphics.) This suggests a social dilemma: While individuals gain from AI, especially those who struggle most with creative tasks, widespread reliance risks narrowing the collective pool of ideas, leaving us with higher average quality but fewer distinctive outliers.

In a second study one of us worked on (Léonard, with four collaborators), this pattern held in a very different domain.4 We asked participants to propose circular-economy solutions to address sustainability challenges, such as repurposing waste materials. A human-only crowd produced a broad range of ideas, from conventional recycling proposals to unique, unconventional ones such as innovative bricks made from foundry dust and waste plastic with a Lego-like interlocking design to reduce construction-related air pollution. In contrast, a single human working with AI often surpassed the crowd in independent evaluators’ ratings of overall quality, strategic viability, and financial and environmental value. But the human crowd scored higher on novelty, and the unusual ideas that might spark breakthroughs emerged mostly from the human-only group. Once again, AI raised the floor of performance but narrowed the variance in outputs. (See the circular-economy graphic.)

A third study pinpointed where in the creative process this narrowing occurs.5 One of us (Kartik, with a co-researcher) conducted a randomized experiment modeled on the New Yorker cartoon caption contest to test whether AI reduced the diversity of creative outputs during the idea-generation or idea-evaluation stage. We tested four collaboration designs: human-only, AI use for idea generation alone, human idea generation with AI use for idea selection alone, and AI support during both phases. The study found that AI boosted both the quantity and average quality of ideas, with the greatest gains when it supported both the generation and evaluation stages. Importantly, the diversity effects diverged depending on when AI was used: AI in idea generation consistently reduced diversity, whereas AI in idea selection preserved variety at levels comparable to those of human-only work. (See the humor caption contest graphic). This finding suggests a potential way forward: The stage at which AI enters the workflow may matter as much as whether it is used at all.

A fourth study that one of us worked on (Kartik and a co-researcher) tested this insight directly.6 It examined how different human-AI collaboration models affect both the quality and the diversity of story writing, as well as the impact of those collaboration models on self-reported writer satisfaction. Four designs were tested: human-only, human-led ideation with AI drafting, AI-led creation with human approval, and continuous human-AI collaboration throughout (the “copilot” scenario). The results confirmed the pattern observed in the previous study: Ceding creative control to AI produced the most homogeneous outputs; having humans and AI work together as copilots mitigated the effect to some extent; and keeping humans in charge of early creative tasks preserved significantly more diversity — approaching the variety seen in fully human work. (See the Hosanagar and Ahn story-writing graphic.) This indicated a clear design principle: whether diversity survives depends on where humans are introduced into the workflow.

The graphs above visualize these findings across all four studies. To measure the diversity effect, we used AI techniques to calculate the similarity between the various outputs within each group and then averaged those scores. Think of it as a clustering metric: A higher similarity score means that ideas bunched together; a lower score means that they were spread out across a wider creative space. In each panel, the horizontal axis represents the average similarity score, and the distribution curve shows the frequency of that score among participants. When AI is involved, the curves consistently shift to the right — toward higher similarity — signaling that the outputs become more alike.

How to Use AI in Creative Workflows (Without Sacrificing Diversity)

The evidence across studies points to one conclusion: How you use AI in creative work matters as much as whether you use it at all. Leaders who seek the efficiency gains of AI while preserving or enhancing originality must intentionally design their workflows. Here are some practical strategies you can use.

1. Keep humans in the driver’s seat for ideation. The fourth study described earlier, which tested different modes of human-AI collaboration in story writing, offers direct guidance here. Participants who retained responsibility for ideation produced stories that were rated higher by independent evaluators in terms of interestingness and overall quality, and they reported greater satisfaction. The diversity effects were equally important: Ceding creative control to AI produced the most homogeneous outputs, while keeping humans in charge of early creative tasks resulted in significantly more diversity, approaching the variety seen in fully human work. Even the copilot model, which involved AI throughout, narrowed the diversity of output compared with human-led ideation.

This has immediate practical implications for managers: Let humans take the lead in any creative and innovative workflow to capture more unique ideas. Start by having a team sketch ideas or draft early outlines before integrating AI into the process. This sequencing preserves variety while still capturing efficiency — and ensures that AI complements, rather than substitutes for, the uniquely human capacity to make messy, surprising leaps.

Video game producer Ubisoft’s in-house AI tool, Ghostwriter, offers a concrete illustration of this sequencing in action. Designed to assist scriptwriters working on large open-world games, Ghostwriter takes on one of the most repetitive narrative tasks: generating first drafts of short lines of dialogue spoken by nonplayer characters. Crucially, the tool does not replace the writer’s role in shaping character or story; scriptwriters define the character and context first and then select and edit from among the AI’s generated variations. Human judgment remains in the driver’s seat throughout the process. The result is a workflow that frees writers to invest their creative energy where it matters most while AI absorbs the volume work downstream.

One other insight emerged consistently across the four studies: If what you are after is the greatest diversity of ideas possible, then humans are hard to beat. But diversity alone is not enough. In the circular-economy study, for instance, a single human iteratively working with AI produced solutions that scored higher in overall quality because targeted prompting enables rapid refinement toward practical value, even though a human crowd produces more diverse and novel ideas. Leaders should choose wisely when deciding how to employ human-only groups in their organization’s workflow.

2. Diversify AI inputs. Homogenization often stems from everyone using the same AI tool in the same way. Managers can push back against this by deliberately introducing variety: Rotate prompts, experiment with role-playing instructions (such as “Argue against this idea”), run parallel AI models, or integrate novel data sources.

Research supports the use of those tactics to increase idea diversity. For instance, chain-of-thought prompting, which involves asking the model to reason step by step before generating outputs, produces substantially greater dispersion in idea sets than plain-vanilla prompts and, in some cases, approaches the variance achieved by human groups.7

Complementing this, the circular-economy study demonstrated that when humans iteratively instruct a large language model to generate solutions distinct from previous iterations, they significantly enhance novelty without sacrificing value. That field study found that this human-guided differentiation approach, which explicitly prompted the model to “tackle a different problem than the previous ones and propose a different solution” after each output, produced solutions with novelty ratings comparable to those of human crowds while maintaining superior strategic viability and overall quality.

3. Deploy multi-agent and multimodel approaches. Just as diverse human teams surface a broader range of perspectives than homogeneous ones, diversifying AI “voices” can counteract convergence. Incorporating agentic workflows, where one AI generates ideas while another critiques them or multiple specialized agents tackle different aspects of a problem, can significantly broaden the search space.

Organizations can design systems where different models or agents address the same challenge from distinct perspectives. At the research frontier, multi-agent AI systems are beginning to assist with scientific discovery itself, generating hypotheses, critiquing them, and refining them in self-improving cycles. Some early use cases are already producing experimentally validated results.8 Colgate-Palmolive offers a practical illustration of this architecture in action. Rather than routing innovation work through a single AI interface, the company uses different AI systems in conjunction with one another: One mines consumer data to surface unmet needs, a proprietary AI generates product concepts, and a third uses “digital consumer twins” to simulate consumer reactions — with humans guiding each handoff.

The key insight is architectural: Rather than channeling all creative work through a single AI interface, organizations should build workflows that create productive tension across multiple AI perspectives. This approach takes advantage of AI’s efficiency while maintaining the divergent thinking that drives breakthrough innovation.

4. Build guardrails and mindful friction to protect human comparative advantage. The temptation will be to let AI act as a copilot everywhere. But if employees outsource their core creative tasks, they risk losing the very skills that make them distinctive. We recommend introducing some friction to AI use — small design choices that prevent people from becoming passive consumers of AI output. For instance, teams could be required to submit human-generated options before consulting AI, or justify why an AI-suggested idea should be selected.

There’s more at stake than just another good idea that might benefit the organization: These practices keep people’s creative muscles active while still harnessing AI’s efficiencies. Without such guardrails, efficiency gains will quickly become commoditized, leaving little basis for competitive differentiation and depriving the workforce of its ability to drive new ideas forward in an age when everyone will have access to AI. Research backs this up. A 2025 study found that students with unrestricted AI access performed significantly worse once that access was removed — but carefully designed guardrails eliminated this penalty.9 Similarly, in a different study, consultants who blindly adopted AI recommendations underperformed compared with those who maintained critical oversight.10

Ultimately, what will define organizations’ competitive edge in the years to come is their ability to cultivate a diverse set of creative ideas through human ingenuity, complemented by an efficient, research-backed workflow that uses AI’s capabilities at the right time to achieve superior quality and feasibility.

AI can support creativity, but only if humans engage actively and early in shaping the process. The organizations that stand out will not be those that use AI the most but those that use it most intentionally, designing AI use in ways that allow human originality and machine efficiency to amplify rather than cancel each other out.

When leaders talk about generative AI tools, one promise comes up repeatedly: These tools will save time.

Fewer emails. Fewer meetings. Less administrative drag.

That expectation shapes how many organizations decide whether GenAI is “working” — and why they’re often disappointed when people’s calendars don’t suddenly open up.

But for our organization, time savings turned out to be the wrong place to look.

What we saw inside a large public higher-education institution, the Community College of Philadelphia, wasn’t less work, but work that changed form. When generative AI entered our organization’s everyday workflows in 2026, coordination didn’t vanish. It shifted away from meetings and toward writing, away from clarification and toward clearer first passes, away from back-and-forth deliberation and toward faster closure on decisions.

To understand what really changed, we looked at how three of the professional roles within one administrative unit — executive leaders, operational leaders, and student-facing professionals — worked during the same six-week period across four different years. What emerged wasn’t a story about automation replacing people. It was a story about how work gets shaped, completed, and passed along.

That distinction matters. Organizations that judge AI only by hours saved risk missing the real gains and feeling underwhelmed by AI, even when it’s quietly doing what it’s supposed to do.

Three Groups’ GenAI Gains

GenAI tools showed up in day-to-day work at our college in 2026. To understand the impact GenAI had on the three groups of professionals, we examined the same six-week window each year (February 1 through March 15), comparing work in 2026 with patterns from the previous three years.

We didn’t ask, “How fast did people work?”

We asked, “What kind of work were they producing?”

Because staffing levels and work hours remained essentially the same across all four years, any differences we observed reflected changes in how work was done, not changes in capacity.

Throughout this article, we use “baseline” to refer to the same six-week period in 2023-2025 — the three years when work was performed under comparable, but pre-AI, conditions. Here’s a breakdown of the results.

Executive Leadership: More Decisiveness

For executive leadership, generative AI usage brought a clear shift toward more decision-focused communication.

Outbound email volume increased in 2026 compared with the previous year. At the same time, the share of messages that were decision- or execution-grade rose sharply, from roughly 60% at baseline to about 80% in 2026.

This was not an anomaly or simply noise. It was evidence of more direction, clarity, and closure. Emails regarding decisions were sent once rather than negotiated repeatedly.

The productivity gain didn’t come from people writing emails more quickly. It came from finishing the thinking before hitting “send,” which reduced the need for downstream clarification and prevented issues from bouncing back up the chain.

Operational Leadership: Faster Work

Operational leadership’s pattern was different from that of executive leaders. Instead of increasing, overall email volume remained relatively stable across the four years. What changed was the quality of that communication.

The share of decision- and execution-grade messages increased substantially, from about 65% at baseline to roughly 85% in 2026. That translated to less drafting, redrafting, and reclarifying of decisions. As a result, productivity gains appeared as organizational speed rather than a simple reduction in email volume: Time was freed up for direct engagement with faculty, staff members, and students.

Given that staffing levels and work hours were unchanged, these gains reflected faster turnaround and lower effort per decision (as opposed to a shift in communication channels).

Student-Facing Professionals: Resolution Efficiency

This group had experienced a pronounced spike in email volume in 2024, but communication had declined sharply by 2026. The share of decision- and execution-grade messages increased modestly in 2026, from an already high baseline of around 80% to about 85%.

This lighter communication pattern did not signal disengagement but resolution efficiency. Clearer guidance upstream, combined with a procedural shift that routed certain interactions through a centralized portal rather than email, reduced the number of clarification cycles required to resolve student queries.

Generative AI played a supporting role by reducing the time needed to draft and redraft responses, which enabled staff members to answer student questions with fewer steps. As a result, the staff now had more time to interact face-to-face with students when the need arose.

What We Gained

Across all three roles, meetings did not go away but many escalations did. Standing meetings remained. External meetings continued. One-on-ones didn’t vanish.

But issues that once triggered quick “let’s talk this through” meetings were increasingly resolved in writing. Decisions were made and communicated with enough context to stand on their own. Clarification moved out of synchronous time and into first-pass clarity.

AI didn’t eliminate meetings. It reduced unnecessary escalations — a far more meaningful gain.

What about economic benefits? The productivity gains we observed did not come from reducing head count or extending work hours. Staffing levels remained stable. Yet more decision-grade work was completed, and more time was available for direct engagement with students.

For leaders, the implication is not immediate cost cutting thanks to GenAI tools but avoided friction. When decisions display clarity and enough context to stand on their own, faculty and staff members spend less time seeking clarification, revisiting prior guidance, or navigating uncertainty. At our college, that reclaimed time is redirected toward student support, instruction, and problem-solving rather than internal coordination.

Over time, this type of shift will have economic consequences, and not just at postsecondary institutions. Clearer coordination allows organizations to absorb more work without adding layers, roles, or meetings. It reduces the hidden costs of delay — repeated emails, follow-up meetings, and stalled actions — that quietly consume employee capacity and often feed burnout.

In this sense, the economic value of generative AI may show up less as line-item savings and more as structural resilience. In our case, this means having the ability to keep organizational focus on student success while slowing the growth of administrative overhead.

Takeaways for Leaders

Although our analysis draws on a higher-education setting, the coordination patterns observed — decision escalation, clarification cycles, and role-specific workflows — are common to many people-intensive organizations.

Three lessons stand out:

1. Don’t judge generative AI only by time saved. Look at how work changes shape.
2. Expect communication to evolve, not disappear. Clarity has organizational value.
3. Design for specific job roles, not averages. The same tool produces different gains depending on how work is organized.

Across roles, generative AI did not create a single productivity effect. It amplified what mattered most in each role: decisiveness for executives, speed for operational leaders, and resolution efficiency for student-facing professionals.

For our organization, AI did not produce empty calendars or fewer emails. We gained better first drafts, faster closure, and more time to deal with people directly. Those are valuable gains.

Avoid chasing the wrong success metrics: Consider your organizational dynamics and where workflow gains are most needed.

In 1951, philosopher Martin Heidegger told a small audience, “The most thought-provoking thing in our thought-provoking time is that we are still not thinking.” Few understood him then. Seventy-five years later, the observation has become unavoidable because AI has forced every leader to confront a question about the nature of intelligence and thinking itself. If thinking is nothing but what machines can do, only faster, we have no case: We outsource it to machines. But if thinking is something else — an embodied, attentive activity through which reality reveals itself — then leadership in the age of AI is the task of cultivating a generative capacity no machine can replicate.

Consider the doctor who treats screens instead of patients, the teacher constrained by standardized testing, or the World Cup referee whose real-time decisions are repeatedly overturned by a video assistant referee. Everywhere, situation-sensitive judgment is being replaced by what Hartmut Rosa calls execution logic: prestructured parameters that turn decision makers into mere executors.1 As spheres of discretion disappear, the creativity of human agency drains away. Beneath these surface symptoms sits the deeper question now beginning to surface in boardrooms: What is irreplaceable about us, and which intelligence will be the foundation of durable advantage once everything codifiable has been automated?

Every leader I work with — in business, government, international institutions, and nongovernmental organizations — reports the same thing. The machine is spinning faster than they can process and think. The acceleration extends far beyond AI: the inbox, the KPIs, escalating disruptions, tools meant to save time that consume more. Overwhelm has become a shared planetary experience. It is also an early warning signal that something essential is being eroded, precisely when we most need it.

This erosion has a name, a diagnosis, and a response. The name is intelligence monoculture: the assumption that AI is the only intelligence worth investing in. The diagnosis is that monocultures, sooner or later, collapse. Our response should be to create a second infrastructure, running in parallel to the agentic AI-enabled IT stack: a deep-sensing leadership infrastructure that cultivates the collective capacities to co-sense and cocreate at the level of the whole system. With it, AI becomes survivable and useful. Without it, the first infrastructure depletes the very soil it is rooted in — heading toward erosion and, eventually, collapse.

This is the blind spot. Leaders have a strong grasp of what they do (the actions they take, the strategies they execute) and how they do it (the processes, the systems, the tools). What remains hidden is the inner place from which they operate: the source of attention, intention, and creativity that no machine can replicate.

The age of AI forces us to clarify our assumptions about intelligence. Can thinking be reduced to computation and pattern recognition? Or is human thinking qualitatively different? And underlying this looms a deeper question: Who are we as human beings? Are we mere extensions of increasingly powerful algorithms — or genuine sources of awareness, intention, and agency?

Three Intelligences for the Age of AI

Intelligence is not one thing. At a minimum, three forms must be differentiated and integrated.

AI in the form of large language models (LLMs) is a pattern-prediction machine, matching and meshing existing human knowledge at a superhuman speed. Trained on existing data, AI deals extraordinarily well with dynamic complexity. It’s powerful — but structurally backward-looking, even when it appears to look forward.

Organic intelligence (OI) is the intelligence of structurally coupled living systems in ecologies of relationships. It senses multiple perspectives and orients to see with rather than just look at.2 This is where empathic listening lives. OI handles social complexity — the texture of multiple worldviews, cultures, and interests.

Source intelligence (SI) is the intelligence of the whole social field — the social soil from which all perspectives emerge. It is sourced not only from what is but from what is emerging. SI also stands for soil intelligence: the intelligence of the social mycelium running through that soil that connects what looks separate aboveground. Examples are entrepreneurs and leaders who sense and create a future that does not yet exist.

SI is grounded in what Eva Pomeroy and I have called fourth-person knowing: the source from which collective action arises.3 It handles emerging complexity: Where the solution is unknown, the problem keeps changing, and it is unclear who needs to be at the table.

The three intelligences are highly interwoven and nested, with source intelligence at the core and organic and artificial intelligences in the surrounding spheres. An intelligence monoculture — almost entirely dominated by AI — would look like an empty shell. There would still be some hardware. But the living, breathing inner core would be gone, turning the shell into a superhardened iron cage for those trapped inside.

The standard fear about AI runs one way: Machines are becoming more like humans. The real danger, though, may run in the opposite direction. We are becoming more like machines — not physically, but epistemically: We see thinking as computation, learning as data processing, creativity as recombination, decision-making as optimization, and the human self as algorithm. That epistemic conversion is what makes LLMs so seductive: They do not need to actually understand. They only need us to have already redefined understanding as what they do.

The Cave and the Sun

At the heart of the leadership challenge in the age of AI lies the question of where human attention, creativity, and agency originate. The late Bill O’Brien, a former CEO of Hanover Insurance, named it in a single sentence: The success of an intervention depends on the interior condition of the intervener.

In our work with teams across sectors, we have identified four structures of attention that organize how we listen, think, and act:4

1.0: Downloading. I listen to what I already know. Attention originates from inside the system; the interior condition is enclosed and reactive (ego-centric).

2.0: Factual listening. I lean into new facts with curiosity. Attention originates from the boundary of the system; the interior condition is transactional (object-centric).

3.0: Empathic listening. I see the world through the perspective of another. Attention originates from the field of relationships (relation-centric).

4.0: Generative listening. I listen to what is emerging from the edges, leaning into its best future potential. Attention originates from the surrounding sphere of potential; the interior condition becomes permeable to what wants to emerge (eco- or cosmo-centric).

The blind spot operates differently at each level. The arc from 1.0 to 4.0 is a shift in the structure of attention. What Plato names allegorically, leadership in the age of AI must name operationally. Prisoners chained in a cave see only shadows cast by a fire behind them; at levels 1.0 and 2.0, they take the shadows for reality. Much of today’s management lives among shadows — AI-generated projections, KPIs, dashboards, pattern matches mistaken for understanding. At Level 3.0, we turn around: We see the fire that casts the shadows. At this level, systems begin to see themselves. At 4.0, we step outside the cave into sunlight, into the realm of the source, which illuminates all things but cannot be seen by looking directly at it.

AI produces ever-more-convincing shadows. It simulates all four levels with astonishing mastery — text patterns that sound ego-centric, object-centric, empathic, even field-aware. But the simulation comes from patterns without interior condition — no witnessing awareness, no deep thinking. No one is there.

Perhaps the ultimate gift of AI is this: It holds up a mirror that forces us to see ourselves and ask, “Who are we? And who do we want to become?”

Four Levels of Collective Action and Strategic Innovation

Resilient organizations operate and innovate across four levels of collective action. Each level involves a distinct structure of attention and, in the age of AI, a distinct set of core leadership skills for the respective human-AI interface.

Level 1.0: Pattern-Executing — Automating. The first level is pattern executing and replicating: This operates with the logic of downloading, as in the levels of attention above. Agentic AI is an unprecedented driver of this level. The human-AI mode is delegation: AI or machine intelligence takes over well-defined cognitive tasks. Think about a fully automated production line. The core leadership skills here center around judgment, or how to recognize plausible but false AI outputs and results. The focus on automation can liberate human attention for higher-level work. This is where most investment flows today — and where Rosa’s execution logic operates in its purest form.

Level 2.0: Pattern-Adapting — Augmenting. The second level is pattern adapting and adjusting to the context of the environment. Human attention engages in object-centric ways — noticing disconfirming data, exceptions, and anomalies — but intention and agency remain within existing frames. The human-AI mode is navigating — Kasparov’s centaur: human strategist on top, AI as the powerful body underneath, with the human steering. This is what Nobel laureates Daron Acemoglu and Simon Johnson call “machine usefulness”: AI complements rather than replaces human labor.5 An MIT Media Lab study on cognitive debt found that LLM-assisted writers’ showed neural connectivity up to 55% lower than that of those who wrote without AI — and that sequence matters: Those who worked brain-first and then engaged AI showed significantly stronger metacognitive engagement than those who started with AI.6 At Level 2, the core leadership skills involve intention setting, sensemaking, and good judgment.

Level 3.0: Pattern-Shaping — Co-Sensing. The third level is pattern sensing and pattern shaping. Conversations shift from debate to reflective dialogue, that is, to thinking together. The move from sensing to shaping defines this level. Here, all three intelligences interact. OI tunes into the multiple perspectives at play. SI leans into emergence. AI surfaces patterns across large-scale data that no individual could perceive — and, used well, holds up a mirror in reflective dialogue that helps humans become more aware of their own assumptions and agency. The human-AI mode is shaped by a partnership with machines, revolving around orchestration and mirroring. This mode requires holding spaces for multiple intelligences to interplay, which in turn requires the core leadership skills of holding space for co-sensing, discernment, intention setting, and co-shaping to happen.

Level 4.0: Pattern-Originating — Deep Sensing and Cocreating. The fourth level is pattern-originating: deep sensing and cocreating. Here, SI moves to the core. Sensing what is shifts to sensing what emerges — the highest future potential. Reflective conversation shifts into generative dialogue: collective creativity and flow. The human-AI mode is holding the space: Origination emerges from human attention that becomes permeable to the field (eco- or cosmo-centric). AI moves from the center to the periphery, if it appears at all (a transcript, a reflective surface to return to later), and is not part of the originating act. The core leadership skills at this level center around holding space for deep sensing, moral discernment,7 shared intention, and cocreating.

In other words, the core leadership skills of the lower levels are included and recontextualized in the higher levels of collective action. One of the most critical leadership capacities today is the metacapacity to balance all four of these levels appropriately. Without that rebalancing, the gravity of AI pulls everything toward Level 1.0 and 2.0 monocultures.

From Machine to Living System

Industrial-era companies were designed like machines: standardized, process-driven, hierarchical, and replaceable. AI-era organizations, as my colleague Lili Xu has observed, increasingly resemble living ecosystems: dynamically collaborative, decentralized, adaptive, and responsive in real time. The most powerful companies of the future may not be the largest but the ones that learn and sense into emerging opportunities the fastest.

As AI dramatically reduces the cost of replicating expertise, what was once the source of competitive advantage — proprietary methods, scale, 10 years of training — collapses. What is truly irreplaceable about a company in the age of AI? Not algorithms; those are commodifying. The real source is the capacity to build organizations where technological intelligence and human field intelligence can evolve together.

The hidden infrastructure for this resilience, says Xu, is the people who sense tensions before they become crises, who hold trust across stakeholder groups, who perceive what customers cannot articulate. These forms of intelligence rarely appear in KPIs, yet they are often the source of an organization’s deepest competitive durability. This constitutes the paradox of the AI era: The more that intelligence becomes abundant, the more the relational and field-based intelligence becomes scarce — and therefore valuable.

What organizations now need to do is invest in deep-sensing infrastructure with the same seriousness they invest in AI. This is the other half of the infrastructure that is missing today in most organizations and economies.

For leadership teams ready to assess where they currently stand, the first question to ask is “How much leadership attention is currently going to the first and second levels of action, and how much to the third and fourth?”

Four mini diagnostics can help to clarify that picture:

• How much time in meetings is spent downloading and debating (levels 1.0 and 2.0) versus engaging in reflective and generative dialogue (levels 3.0 and 4.0)?
• Where is the center of gravity of how your organization currently operates: pattern-executing, pattern-adapting, pattern-shaping, or pattern-originating?
• To meet the demands of our age, which of those levels needs strengthening and more leadership attention now?
• What support structures — tools, practices, places — have you created that help your teams and organization to develop deep sensing and innovation capacities around levels 3-4?

Beyond the Blind Spot

Inside the cave, we take shadows for reality. AI-generated projections are mistaken for understanding. What is missing is the Level 3.0 capacity to turn around and see the structure that generates the projections — and the Level 4.0 capacity to step outside into sunlight, to originate new patterns from source.

AI produces ever-more-convincing shadows — depth, empathy, even wisdom — simulated from patterns without an interior condition: without the awareness that notices its own awareness. The current cave that we are operating in is our blind spot. Turning around and stepping outside requires what no AI can do for us: the cultivation of an interior condition from which we can see more deeply, more clearly, and more collectively.

Max Weber warned of modernity’s iron cage a century ago. Today, the cage has a new name: the 1.0-2.0 machine, supercharged by a trillion-dollar industry, the logic of inevitability, and the daily downloading that floods our calendars and shapes our attention. Each of us faces a choice: Get absorbed into the machine, or turn around and step outside. Choose what story of the future you want to be part of — and give AI the role it deserves: tool, partner, mirror or master. That move, if performed collectively, requires a new minimal enabling infrastructure: deep-sensing spaces that enable organizations to upgrade their operating systems and their capacities to levels 3.0 and 4.0.

Every day, leaders have two critical allocations to make: the allocation of attention, and the allocation of budget. What percentage of each is going into automation? Into navigation? Into orchestration? Into deep sensing and pattern origination? If your ratio is vastly out of whack, you already know what the next move should be.

The cave is comfortable. The shadows are mesmerizing. The logic of inevitability whispers that there is no alternative.

There is.

Leaders at literally every Fortune 500 company will tell you that they are governing their AI — every single one of them. Now ask those same leaders who’s responsible for shutting down an AI model that’s causing harm. Most people can’t answer that question.

That silence is the most important story in enterprise technology right now, and it’s rarely, if ever, addressed.

During the past several years, a governance industry has quietly grown up around artificial intelligence. Companies have built registries to catalog their AI models. They have implemented classification systems to label their data. They have stood up dashboards to monitor model behavior, and risk councils to review new deployments. They have written policies, hired compliance officers, and presented slides to their boards. The infrastructure of governance has proliferated.

What’s missing is the governor.

Who Can Shut Down an AI Model

I run AI and data governance at Adobe. My job is to build exactly the kind of program that every company now claims to have. And what I have learned from the inside, while getting my hands dirty, is that the hard part is never the technology. The hard part is a question that sounds almost insultingly simple: When an AI model does something it shouldn’t, who has the authority to stop it?

The question is not who gets notified. It’s not who writes the incident report. It’s who has the authority, the organizational standing, and, frankly, the job security, to walk into a meeting and say, “We are shutting this down.”

In most companies, that person either doesn’t exist or is a paper tiger set apart from development teams organizationally. Roles like chief AI ethics officers and groups like data governance councils and responsible AI teams are completely necessary but merely advisory. They can flag. They can recommend. They can escalate. What they generally cannot do is hit the stop button. The actual decision authority sits somewhere else. Inside most Fortune 500 companies, it generally sits with someone whose primary job is shipping products and hitting revenue targets and who has every incentive to treat the governance flag as an afterthought rather than a mandate.

This is not a criticism of individuals; it is a description of a structural problem that the governance industry has largely chosen to ignore because the governance industry is selling tools, not accountability.

These tools are genuinely useful and vital to regulatory compliance and effective governance. A model registry tells you what AI systems exist and what they’re doing. A risk classification framework tells you which ones deserve the most scrutiny. A data lineage system tells you where the inputs came from and whether they were clean. All of this is real and important infrastructure. But it is infrastructure for visibility, not infrastructure for action.

You can have perfect visibility into a problem and no mechanism for solving it. You have to see something to do something, but you also have to pick up the hose to put out a fire.

Think of it this way: A fire alarm is not a fire department. You can wire every room in your house, dutifully change the battery of every smoke detector annually, and route every alert to a beautiful dashboard — but still have your house burn down because nobody picked up the hose. Yes, it’s critical to know that the house is burning — otherwise, you wouldn’t know that you need a hose. But you need somebody to tell the fire department to take action.

At Adobe, we addressed this by creating a federated governance model with named owners for every AI system and a centralized steering committee, with escalation authority, reporting into the trust and security organization, not the product team. That’s the key design choice we made: to give governance a reporting line independent of the teams shipping AI products. This is essential so that the person who can say “no” to an AI decision doesn’t report to the person who benefits from saying “yes” to shipping products.

Why Urgency Is Required

The stakes here are rising fast. The European Union’s AI Act is now in force, and it does not ask companies to demonstrate that they have dashboards. It asks them to demonstrate that they have meaningful governance. It requires documented decision-making, clear lines of accountability, and the ability to show, after the fact, who made a consequential choice about an AI system and why. When regulators come asking those questions, a policy document and a risk registry aren’t going to be sufficient answers. Regulators want a name.

The urgent need for governance is compounded by the speed of AI deployment. Most large enterprises are now running hundreds of AI systems across their organizations, in places leaders may not even be aware of. You will find AI tools being used in customer service, hiring, content moderation, pricing, fraud detection, and anyplace well-intentioned employees are just trying to make their lives easier. Many of these systems were deployed quickly, under pressure, with governance treated as something to be sorted by future-them. The time for future-them has arrived.

The answer is not to slow down AI adoption. It is to take the organizational design question as seriously as the technical one. Every AI governance program should be able to answer three questions: Who has the authority to stop a model? Do they know it’s their job? And do they have the standing to exercise that authority when it conflicts with someone else’s road map?

If your company cannot answer those questions, you do not have a governance program. You have paperwork.

The companies that will navigate the next five years of AI regulation and public scrutiny are not necessarily the ones with the most sophisticated tooling. They are the enterprises that did the harder, less glamorous work of building a human accountability structure to sit underneath the technology.

These organizations are the ones that appointed an AI governor, gave them real authority, and made clear that the job was not to make AI deployment easier but to make it defensible. These organizations have a federated team deputized to identify, remediate, and escalate, and they know that escalation requires a destination. That means a governance function with a direct line to senior leadership, independent of the product teams shipping AI, with explicit authority to stop an AI deployment.

Every company says it governs its AI. The real question separating governance from theater is simpler than any framework. Ask yourself: Who in my organization can say “no” and have the authority to mean it?

What would you do with artificial intelligence if you were confident that it would transform your industry? What actions would you take if you felt that you were at an inflection point in that transformation? Would you try to be an early proponent of AI-first in your industry, or a fast follower?

Those are some of the questions faced by the leaders of Franklin Templeton — officially Franklin Resources Inc. — a large investment and asset management firm with about $1.7 trillion in assets under management that was founded in 1947.

Over its 79-year history, Franklin Templeton has grown through strategic acquisitions that have enhanced its capabilities and global reach and expanded its competencies across asset classes, geographies, and investment philosophies.

Today, however, AI is an important driver of future growth and profitability. Consultants, academics, and industry associations agree that the technology is already powering research, compliance, and client relationships in the investment field and that it will transform them further in the future.

Jenny Johnson, Franklin Templeton’s CEO and a third-generation leader of the firm, has long combined investment leadership with deep technology fluency, having managed technology organizations earlier in her career. Before AI became a board-level mandate, she had already been focusing on AI for years. She personally experiments with generative AI, building AI agents and using techniques like vibe coding (using generative AI prompts to write code) to create computer programs.

But even Johnson has been amazed by the rapid advancement of AI in the industry. “This is faster than even I thought it was coming,” she said in a November 2025 video interview. “Every big financial institution spends a lot of money on reconciliation between systems and reconciling data. AI can help with that.” She noted that AI could also review company research reports and sell-side reports, analyzing, for instance, how tariffs would affect U.S. pharmaceutical companies versus those in Europe. “I don’t think everyone will have the same models,” she said. “Training the model is all going to be about your own data.” The future, she said, will be having the company’s entire talent force using AI as a tool.

AI Capabilities Today at Franklin Templeton

Franklin Templeton is moving rapidly toward that future, with a huge variety of internal AI capabilities and transformative platforms at both production and pilot status. The company has product teams that work with business units such as distribution, operations, and investments. Each product team operates under an AI-first model that combines product management, engineering, and data science into one unit. There is a common AI platform team and a research team. There is also an adoption and solutions team that drives employee implementation of AI and helps align business benefits with the products.

Deep Ratna Srivastav, the company’s chief AI officer, is responsible for AI product management, engineering, research, and adoption. He was involved in the conceptualization and launch of Franklin Templeton’s Goals Optimization Engine, one of the company’s early portfolio selection and optimization offerings. He told us that the engine integrates with global fintech ecosystems — including those with recordkeepers, managed account providers, custodians, and digital wealth platforms — to deliver personalized investment strategies aligned to investors’ financial objectives. It currently generates recommendations for over 40,000 investors, primarily focusing on retirement goals. The application has been embraced by several of the company’s strategic partners and is part of the firm’s forward-looking AI road map. The next phase, Srivastav said, will apply reinforcement learning to advance portfolio optimization.

Franklin Templeton offers its sales and distribution team its Intelligence Hub, which brings together AI and digital capabilities designed to enhance insights, facilitate territory management, and strengthen client engagement in meetings with financial advisers. The hub centralizes previously fragmented data sources, research, and over 15 workflow tools into a single interface, reducing manual search time and accelerating access to important content for sales meetings. AI-powered workflows automate list generation, meeting preparation, and dynamic prioritization. A Franklin Templeton salesperson can get a recommendation on which independent financial advisers to highlight, what to focus on in a client conversation, how best to get visibility with the adviser, and the most appropriate clients to meet with based on geographical proximity.

Following a yearlong pilot, Intelligence Hub was made broadly available to the company’s sales professionals in early 2026. Srivastav said it has delivered measurable efficiency improvements, including reduced daily preparation time before client meetings. It has also led to a significant increase in value-added client interactions.

The company has also applied AI to end-to-end processes in the middle and back offices of the organization. There are AI-enabled platforms in production for automated reconciliation of trades and for creating scalable communications with custodians, counterparties, and core trade operations.

Investment analysis is also increasingly supported by AI. The goal is not to automate investment advice but to support it with better information, faster iteration, and insights that humans alone couldn’t arrive at. “Copilot, not autopilot” is the overall objective.

To that end, a system called MosaiQ combines portfolio construction, manager research, and analysis into a single platform. An AI assistant named Pixel guides users through MosaiQ using natural language to explain complex investing concepts and, increasingly, to perform end-to-end tasks on users’ behalf. There is a new portfolio manager “copilot” assistant in place that can provide early warnings of market shocks, identify behavioral biases in training, and provide insights on portfolio creation. Franklin Templeton has also built an agentic investment analyst called Gromit that can independently analyze nuanced topics (for example, the impact of higher oil prices on U.S. labor trends), fact-check humans, and offer contrarian viewpoints by analyzing a breadth of proprietary and third-party data sources. Those systems are primarily powered by generative AI.

Looking Forward

To position the company for evolving client demands, Franklin Templeton’s $103 billion multi-asset group, Franklin Templeton Investment Solutions, tasked Max Gokhman, formerly its deputy chief investment officer, to lead the new AI & Digital Asset Solutions team. It will focus on three areas: further developing AI-driven investment capabilities, launching strategies incorporating digital assets and tokenized products, and advising clients on the effective use of these technologies in their own portfolios and organizations. Gokhman’s experience as an AI company founder, digital asset investor, institutional asset allocator, multi-asset portfolio manager, and chief investment officer made him uniquely suited to lead this effort.

“I’ve seen our industry change multiple times over my career, but never at a pace this rapid,” Gokhman said. “Tenacious focus and a willingness to pivot are requisite for any asset manager that wants to be relevant five years from now.”

Chief AI officer Srivastav and his colleagues are working across many other end-to-end processes. One involves voice intelligence for the U.S. retail business to transform customer engagement. “Portfolio commentary” AI, which will deliver timely insights to strengthen the client experience, is in the planning stage. Utilizing the multi-agent orchestration portfolio management copilot for the investment team is another step in the end-to-end redesign of the investment process. Marketing is streamlining its content creation process, enabling it to produce more personalized, timely, and high-quality content. Other corporate functions — including legal, compliance, HR, and finance — will be similarly reengineered with AI.

Neither Srivastav nor CEO Johnson is terribly concerned about whether Franklin Templeton’s employees will go along with the AI transformation. While the opportunities for AI education have been only somewhat popular, there has nonetheless been rapid adoption of virtually every AI tool made available to employees, Srivastav said. In many cases, these tools have been visible to clients and partners, which is helpful in persuading employees to use them. Srivastav noted that noncompliance with the company’s extensive AI governance policy and procedures has not been a concern thus far.

The leadership team of Franklin Templeton isn’t sure whether its AI capabilities will result in a “big bank” transformation or whether they’ll power a slower evolution toward increased efficiency and effectiveness. They do know, however, that they want to be ready in advance of customer and market demand and that they need to be among the industry’s leaders.

“Professionalism” encompasses the broad set of shared beliefs and expectations about how people within an industry or workplace should interact with one another: Think communication style, punctuality, or meeting etiquette. But opinions differ: Cameras on? Cameras off? Do meetings start precisely on the hour? Is arriving a few minutes late acceptable or offensive?

Our conversations about professionalism tend to proceed like a garden that has been allowed to grow without controlling for weeds or pests and is then subject to endless debate over whether the result is “good” or “bad.” But that has never been the right conversation, because context matters: Are your organization’s professional norms good or bad for your particular workplace?

While some norms are common to many workplaces — such as following through on commitments, treating colleagues with respect, and communicating appropriately — professionalism has no single definition. It varies across regions, cultures, sectors, and industries. But as a set of norms for differentiating wanted (“professional”) from unwanted (“unprofessional”) behaviors, professionalism is inherently about excluding some for the benefit of the whole. When defined well and fairly, professional standards can effectively guard against harmful behavior while creating a shared sense of identity among people from a range of backgrounds, compounding their individual efforts into collective impact. But, defined poorly, professionalism can divide and distract teams, systematize active discrimination, and discount — or even incentivize — detrimental behavior.

As an organizational consultant, a leadership adviser, and an analyst of workplace systems, I’ve learned that the key to establishing a professionalism that works is to actively define norms and standards for your particular organization. Far too many leaders ignore their own agency to shape what professionalism means, defaulting to “how we’ve always done it” rather than questioning which norms would, in fact, serve their people best. As a result, workplace professionalism is often a mixed bag: norms that signal competence and skills alongside outdated norms that can unintentionally disadvantage some team members. For example, norms that discourage discussion of caretaking at work can exclude caretakers and parents; expectations of “normal” appearance and body language can hinder neurodivergent or LGBTQ+ people’s self-expression; and dress codes defining “acceptable” hairstyles can stigmatize people with natural, Afro-textured hair.

Contextually Defined Norms

Every leader has the responsibility to create a version of professionalism designed for their unique workplace context. By incentivizing helpful behaviors that bring the best out of every person and disincentivizing harmful behaviors that impede performance, leaders can design a bespoke code of professionalism that serves people rather than functioning as an obstacle. Here’s how to lead a collaborative process of rethinking their workplace’s approach to professionalism, regardless of geographic region, sector, or industry.

1. Define success for your unique context. Take a step back to see the bigger picture. Ask your workers and key partners to share with you what they believe success looks like for your workplace. More products sold? Satisfied customers? Highly engaged workers? Trusting relationships with key community leaders? A succession plan for solid leadership over the next decade? Defining the outcomes that matter most to your organization grounds everything you do in a “why” that goes deeper than “because a leader said so.”

2. Identify deal-breaker behaviors. Imagine an employee who is highly effective at delivering results — but the way they do it is egregious enough that it compromises their own, or possibly their entire team’s, success.

Clear deal-breakers are physical violence, harassment or intimidation, verbal abuse, or discrimination — even on the part of your top performer. Defining more subtle offenses is trickier. What if their workstation is messy? Not ideal, but perhaps excusable. What if their lack of personal hygiene causes their colleagues to avoid them? More troubling. What if they cause important clients to feel disrespected or belittled after meetings? That might be a deal-breaker.

But deal-breaker behaviors aren’t universal and may vary across cultures or industries. The practice of identifying your organization’s particular deal-breakers is powerful precisely because it can reveal cultural norms or shared beliefs so deeply held that they’re practically invisible. Discuss this as a group to identify where your key partners might agree or disagree about what behaviors constitute deal-breakers.

3. Identify the minimal expectations required for success. This is the most uncomfortable step. If professionalism is up to us to define, we might want to define it aspirationally, as the highest expectations we can set to be the best version of ourselves. Always saying please and thank-you, always following every cultural norm to the letter, embodying perfection in all workplace interactions — that’s the ideal. But no person is perfect in any setting, to say nothing of the workplace. As a pragmatic tool, professionalism is best used to define the minimum standards of behavior that we expect from our colleagues, one step above our deal-breakers.

For example, it may not be feasible to expect our colleagues to wear a uniform, but we might define success in our workplace as having a strong sense of shared group identity and attention to detail. Those criteria may be reflected in a dress code that sets the expectations that clothing will not have visible dirt or stains but will include an accessory with the company logo.

Ideally, everyone in the workplace would be gracious and warm in every interaction, but human nature makes that infeasible. However, we can define success in our workplace as requiring effective communication and good teamwork. A respectful conduct policy might set the expectation that the way we communicate will make our colleagues feel safe and respected, and that if we miss the mark, we will swiftly make amends.

Similarly, it may not be feasible to expect our colleagues to always have their video on during virtual calls. But we might define success during important discussions as requiring deep human connection — and so our leadership team might set the expectation that webcams will be on during retreats, culture-building events, and teamwide discussions.

4. Understand the gap between expectations and reality. Ask your key partners what behaviors are really rewarded or punished in practice. You may find that aspirational norms have unintended consequences. Leaders may, for example, officially encourage workers to respond to emails within 24 hours — but in practice, managers may penalize workers who don’t respond quickly, even outside of traditional working hours. Leaders may communicate that deliverables and results matter more than busywork — but in practice, they may still extend promotions to workers who seem to always be working rather than to their more efficient colleagues, simply because the busier workers seem “more committed.”

Each of these gaps has a real cost, not just to people but in terms of your ability to align your actions with how you defined success in Step 1. If these gaps represent behavioral shortcomings of your starting point of “passive professionalism,” closing them will help you establish a far more functional and beneficial definition of professionalism, tailor-made for your context and directly linked to your organization’s success.

5. Incentivize what you want, and discourage what you don’t. Professional norms are not rigid policy but a means to an end. Your particular definition of professionalism can help ensure that everyone in your workplace is rowing in the same direction, is protected from abusive and harmful behaviors, and can expect the same standard of mutual respect throughout the workplace.

If old norms are no longer contributing to success, or new norms are needed to reach success — or both — it’s not enough to simply declare a policy change in an email or during a team meeting. Leadership has to align their behaviors — particularly their informal rewards and rebukes — with the professional norms they’ve defined. To support a norm of punctuality, for example, managers can praise and acknowledge those who best embody that norm while confronting any deal-breaking behavior. (For example, an employee who routinely joins meetings halfway through should be addressed directly to correct the behavior.)

Be on the lookout for any existing behaviors that contradict the norms you’re trying to build. For example, the new norm of punctuality might clash with an unspoken norm that seniority grants flexibility, with certain employees held to a far looser standard than others. To truly ensure that timeliness becomes prioritized across the workplace, you may need to clarify that senior leaders must now show up on time as well, with no exceptions, even if they have been excused for not doing so in the past. Focusing on changing one high-impact behavior or practice at a time, and clarifying what is and is not expected, can make this shift feel more tangible.

Professionalism will always be a potential source of debate as times change and work evolves. Critiques of professionalism — that it may not meaningfully align with success, that it may be biased in its application, or that it may result in harm — reflect the real possibility that the norms you have today may not be the norms that your organization needs. Especially during contentious times, be open to revisiting what you consider professional behavior and asking yourself whether your norms are most effectively serving their purpose: empowering your people. When in doubt, return to these steps to design a strategically aligned set of professional norms that enables everyone to bring their best.

After several years of AI experiments and pilot initiatives, a crucial question remains open for most companies: How much of a return — and what kinds of returns — are we getting from all of this AI investment? To many executives, AI ROI still often feels more like art than science: elusive, imprecise, and industry-dependent.

Surveys and benchmarks paint a confusing picture about current returns. Much of the guidance also remains focused on measuring inputs — encouraging organizations to invest, experiment, and build capabilities (“You should invest in …”) — rather than on outputs and how to assess impact (“Here’s how to measure results”). Today, few companies apply the same financial discipline to artificial intelligence as they would to a new factory or piece of machinery.

Our interviews with more than 30 CEOs and senior leaders across various industries confirm that measuring AI ROI is anything but standard practice: Two companies making nearly identical investments may define success in entirely different ways. Yet companies that fail to identify an explicit approach to AI ROI — or that simply roll out generic AI tools and hope for productivity gains — rarely realize credible, lasting returns.

ROI measurement differs by the type of AI technology being used. Analytical AI projects, which are typically based on established machine learning techniques like prediction and optimization, often produce more directly attributable financial returns but tend to be applied to targeted, well-defined use cases. Generative AI, in contrast, is broadly applicable, given its ability to perform a range of knowledge work tasks previously done by humans. A GenAI tool often creates improvements in speed, quality, or volume of work, requiring deliberate translation into financial impact. And some companies combine both analytical and generative AI solutions in a customized manner.

AI ROI also depends heavily on industry context. In the consumer goods sector, companies streamline their supply chains by using analytical AI, enhancing demand responsiveness. A B2B marketing agency using generative AI may focus instead on creative throughput and ideation, proposal win rates, or lead conversions — a different definition of “return.”

Three Pathways to Tangible AI ROI

Based on our interviews with executives, we identified three practical approaches to measure and manage AI ROI. These approaches reflect a range of AI maturity levels among companies, and varying strategic intents.

By comparing your organization’s current approach against this framework, you can identify where you are and what it will take to move forward. The overarching goal for leaders: to ensure the translation of AI activity into verifiable business results.

1. Function-focused approach

Who it serves: Companies trying to build credible proof points before scaling.

With this approach, you select one or a small number of business functions, such as customer service, marketing, production, or HR, as the starting point for focused AI tool deployment. In each function’s case, you build or acquire tailored AI solutions and equip people with rigorous, function-specific performance metrics. This means tracking outcomes such as shorter response times, fewer errors, improved quality, or reduced unit costs. For leaders, the logic is “If we can demonstrate credible ROI here, we can justify broader deployment elsewhere.”

Function-focused AI initiatives often deliver some of the most tangible ROI, especially when paired with deliberate workflow redesign. In customer service, organizations that deploy GenAI-driven agents and decision-support tools have reduced handling times and call volumes — often automating a high percentage of routine customer requests — and translated those gains into lower service costs and improved customer satisfaction.

For instance, Unilever redesigned its early-stage recruitment process around AI-based candidate assessment, reducing HR’s reliance on external recruiters while shortening time to hire and lowering recruitment costs. In other companies, finance units have experienced similar dynamics, where AI-based forecasting, pricing, or fraud detection systems embedded into core decision workflows have improved accuracy, reduced losses, and delivered measurable cost benefits.

The function-focused approach to AI ROI is particularly effective for building organizational confidence in AI investments. The plus side: By limiting scope and maintaining clear ownership, organizations can create credible proof points that are easier to measure, explain, and defend. The negative side: Because specific needs and contextual factors shape function-specific ROI, different success stories might be difficult to compare or aggregate as AI adoption expands.

Your next move: If you’ve already done several function-specific AI initiatives, it’s time to begin laying the groundwork for the next stage: coordination. As function-level proof points accumulate, leaders can gradually move toward a shared AI ROI playbook with consistent definitions, financial logic, and data instrumentation standards. Start by standardizing metrics that can be transferred across functions and aligning financial assumptions across key use cases. As one CEO said, “We’re iterating toward a more structured model, linking AI impact to planning, budgeting, and playbook development; it’s an ongoing loop of learning.”

2. Coordinated approach

Who it serves: Companies trying to make AI ROI comparable across functions or units.

With this approach, you’re managing a growing set of AI initiatives across the organization. Concurrently with function-specific deployments, or even earlier, you’re also rolling out some general-purpose AI tools and shared AI capabilities that touch multiple teams and workflows. The defining challenge here is coordination: maintaining broad visibility into AI activity while selectively focusing on the metrics that have the most significant business and economic impact. Ideally, this approach facilitates shared learning, reuse of proven metrics and assumptions, and faster replication of successful AI use cases.

Organizations taking a coordinated approach often use shared AI platforms and capabilities to manage initiatives spanning multiple teams. At JPMorgan Chase, an internal AI platform called LLM Suite has been deployed to more than 200,000 employees across legal, research, client services, operations, and other functions. This gives people broad access to generative and analytical AI tools while requiring coordination mechanisms to ensure consistent value creation. At Amazon, the evolution of internal AI systems resembles an AI flywheel, whereby innovations — such as recommendations or robotics — that begin in isolated teams spread and are reused across the organization through shared machine-learning platforms and practices.

In both cases, value comes from coordinating how results are interpreted, compared, and scaled across the organization. At this stage, generative AI tools are often used both inside and across business functions, heightening the need for coordination. Analytical AI tools deliver results that are often easier to compare, via clearer links to operational and financial outcomes.

The logic and business motivation for coordination are straightforward: “We’ve invested in many AI initiatives, and we need a way to stay on top of them all.”

However, especially in larger organizations, coordination without clear standards can result in a patchwork of ROI methods, making it difficult to align priorities, compare outcomes, and decide what to scale.

Your next move: During this phase, it’s important to continue prioritizing and standardizing. Identify where deeper ROI instrumentation is warranted, and apply consistent financial logic across the full set of AI initiatives, regardless of whether they involve broad tools and capabilities or targeted deployments. Standardizing how results are translated into financial terms enables meaningful comparison and scaling across initiatives. As one CEO put it, emphasizing the need for a common baseline, “If an AI initiative claims to replace the work of four employees, I want to know who they are; otherwise, it’s not real savings.”

3. Enterprise portfolio approach

Who it serves: Companies that are ready to govern AI ROI at scale.

This stage represents the highest level of ROI maturity and is where you’re applying rigorous financial logic across the entire portfolio of AI initiatives. An AI initiative is treated like any other significant investment: It is governed through forums similar to those for capital projects and is evaluated with business cases, financial models, and portfolio metrics such as net present value and internal rate of return. This approach emphasizes funding projects that create measurable value as quickly as possible.

At Morgan Stanley, for example, AI initiatives are assessed through a structured evaluation framework that tests each use case against real-world criteria before deployment. This approach enables disciplined enterprise-level oversight and scaling of AI tools. In comparison, one equipment manufacturing company we studied applied strict financial discipline: Both analytical and generative AI initiatives were allowed to run for a limited trial period and were routinely terminated if they failed to demonstrate measurable value within six months. This ensured rigor but risked premature rejection of promising efforts.

A professional services firm pursued another option: It separated two kinds of AI initiatives — those that built mandatory foundations for generative AI adoption, where ROI was not enforced upfront; and targeted, tailored applications, where clear financial returns were required. Effective enterprise AI ROI management depends on deliberate and company-specific choices about timing, risk tolerance, and evaluation rigor.

At the portfolio level, both analytical and generative AI are evaluated as part of the investment mix, but often under different expectations. Analytical AI work fits naturally into traditional financial models, whereas generative AI initiatives may require staged evaluation and adapted governance. For example, milestone-based funding or phased ROI thresholds may be needed to save worthwhile initiatives from premature rejection when those projects have indirect benefits, delayed adoption, or value creation driven through learning.

The enterprise portfolio approach to AI ROI offers clear benefits. You can compare AI initiatives side by side, compare them with other technology investments, track portfolio-level value creation, and make more confident decisions. As AI initiatives begin to reshape the operating model, however, initiative-level ROI comparisons become less informative; leaders should then rely more heavily on enterprise-level performance indicators to assess systemwide impact.

Your next move: If you choose to take an enterprise portfolio approach, it’s important to decide how strict you want to be. Fully enforced ROI can kill breakthrough AI bets too early if you overlook the value of new capabilities, learnings, and spillover benefits. The goal is to balance financial discipline with strategic patience: Apply lighter ROI tracking to early-stage AI experiments and introduce more rigorous scrutiny as projects scale. Consider creating a separate unit or governance track for more radical AI initiatives. As one executive told us, “You don’t need to measure everything from day one; start with clear KPIs for each area, then layer in more rigor as solutions mature.”

Getting AI ROI Right: Three Takeaways

Many organizations will move through all three approaches to AI ROI over time. Here are three parting takeaways from the executives we interviewed:

• Prioritize high-value, scalable AI use cases. ROI becomes most visible and meaningful when AI is applied to high-volume, high-leverage work. Whether through enterprisewide deployment or targeted use cases, even small productivity gains in large-scale activities can compound into significant value.
• Lead decisively. AI ROI doesn’t materialize by accident. The benefits come only when you provide direction, follow through, and rethink how work gets done.
• Remind yourself that your company and AI technology will keep evolving. To navigate ongoing changes, avoid both overengineering and under-measuring.

As your organization accumulates AI maturity, use the three approaches to track your progress and see your ROI grow.

Large companies seeking access to new technologies — as well as the high returns promised by early investments in successful startups — have been establishing corporate venture capital (CVC) units for many years. But returns on those investments can be erratic, and new technologies can be difficult for the parent company to take advantage of. Why do many companies struggle to derive adequate benefits from their CVC efforts? We think that at the heart of the issue is a persistent confusion over objectives that ultimately makes CVCs difficult to sustain.

Dueling objectives have long been a problem: According to a 2015 survey of CVC investors, 79% aimed to support the parent company’s strategic aims, while 76% of respondents from the same sample claimed to prioritize financial returns.1 A 2021 study found that most CVCs still rely on ad hoc structures and governance processes that confuse parent companies and result in weak executive support and frequent shutdowns.2 Our research indicates that many CVCs continue to pursue both strategic and financial benefits, only to discover that these two goals are very difficult to mix in practice. There are no easy solutions to this problem, but our data and interviews have led us to some specific recommendations.

Our primary argument is that, once the parent company and the CVC unit agree on what they seek to gain from investments, that decision needs to drive everything else the CVC does: investment guidelines, team composition, the decision-making process, and the extent of its integration with its parent. Failure to align CVC objectives with parent expectations and then with organizational implementation is likely to be fatal.3

The Spectrum of Investment Models

Strategic-priority CVCs benefit the parent company by investing in startups that provide insight into and access to new technologies, products, services, and business ideas that the parent can take advantage of. Realizing these benefits requires close integration with the parent company’s business divisions. Financial-priority CVCs invest in startups primarily to generate a monetary return. Hybrid CVCs try to give equal weight to strategic benefits and financial returns. (See “CVC Investment Models.”) While financial returns are easily calculated by comparing sums invested to the current market value of a portfolio, evaluating strategic returns is much more difficult, especially when CVCs mix strategic and financial goals.

As of January 2025, our sample of 59 CVCs had adopted those investment models in relatively similar numbers. We classified 21 (36%) as financial-priority leaning, 20 (34%) as strategic-priority leaning, and 18 (30%) as hybrid. We included only CVCs that had made CB Insights’ annual top 10 list in terms of active investments between 2017 and 2024 and were still active in 2025. On average, these CVCs were 19 years old with a recent estimated fund size or investment budget of $749 million.

It’s important to note that most CVCs fall along a spectrum, not at the extremes (that is, wholly devoted to one or the other objective). We do not recommend a strategy on the extremes or squarely in the middle. These positions are difficult to sustain, either because they fail to provide any strategic value or financial returns or because they are mediocre at both. Instead, we suggest that CVCs prioritize strategic benefits or financial returns but aim to gain some benefits in the lower-priority category. In the majority of cases, it makes the most sense for CVCs to focus on strategic investments that yield some financial benefits, since this investment strategy is most likely to identify viable startups that can benefit the parent company. (See “The CVC Spectrum.”) All CVCs, theoretically, have a lower cost of capital than independent VCs, to the extent that they receive money from their parent companies and don’t have to compete for outside investors. Since most CVCs have some financial criteria, their main differentiation occurs in how high those financial bars are and to what extent CVCs access their parents for help with investment decisions.

Pros and Cons of Strategic, Financial, and Hybrid Approaches

Some practitioners argue that a CVC should emphasize strategic objectives as an investment in the parent company’s future. Les Vadasz, the founder of Intel Capital, one of the oldest, largest, and most successful CVCs, strongly holds this view, saying, “If you don’t have a strategic reason to invest, then I don’t think the CVC has a reason to be in business.” Vadasz expected his investments to help build demand for Intel’s semiconductor products and to provide some insight into future trends.4 He looked for a relatively quick impact on demand for the microprocessor business — for example, by investing in software companies whose applications ran on the Intel x86 chip architecture.

Siemens’s experience illustrates how difficult it is to maintain a strategic focus if that means passing up potentially good financial investments. Frank Andrasco, a veteran of Siemens Ventures and its successor, Next47, and now a senior investment director at Aramco Ventures, agreed that strategic benefits should be the main focus of a CVC. However, he found a pure strategic portfolio to be difficult to sustain. Siemens Venture Capital had been strategically oriented, but, because it had declined to invest in many deals that it later realized would have offered good financial returns, Siemens’s top management made its successor CVC unit, Next47, financially oriented. Andrasco moved on, frustrated with this decision. CVCs “are always going to be beaten to the best deals. … They are competing with Andreessen Horowitz and Sequoia. Why are they going to be better than those guys?” he told us.

Missing out on good financial investments is only part of the frustration for strategically oriented CVCs. Bailing out failing startups is also not sustainable, as Vadasz explained. “We invested money for strategic reasons,” he said. “Now, a little caveat here: You have to invest with financial discipline because companies that don’t succeed do not help you.” Also, strategic CVCs tend to become less strategic over time, according to Andrasco. “You can’t get in on the best deals because your strategic constraints create adverse selection,” he said. “So the only solution is to remove the strategic constraints.”

Another school of thought — the one to which Siemens pivoted — is that CVCs should focus on making money because startup investing offers the potential of extraordinary financial returns. Tim Chiang, a veteran of GE Ventures and Xerox Ventures, strongly holds this view, arguing that financial-priority CVCs can make quicker decisions than strategic-priority units because they don’t need to coordinate due diligence and priorities with a parent company.

In our sample, most CVCs owned by financial services firms based in Asia (such as Mitsubishi UFJ, SBI Securities, Daiwa, Mitsui Sumitomo Insurance, Fosun Capital, and CreditEase) were in the financial-priority category. They often treated a new venture fund as one of several investment vehicles for their clients. However, we’re seeing many other CVCs in this space as well, owned by Google, Panasonic, Baidu, Legend/Lenovo, and Next47 on the tech/industrial side, and SR One (GSK), Roche Venture Fund, Novartis Venture Fund, and Novo Ventures on the pharma/biotech side.

Some financial-priority CVCs in our sample occasionally capitalized on expertise in their parent companies for due diligence, startup mentoring, and business development, suggesting a different strategy than pure financial motivation. We call this model financial-priority/hybrid. Financial returns remain the primary goal, and there are no strategic investment criteria, but there is some help from the parent, such as making investment decisions or providing startup mentoring. (See “CVC Investment Models.”)

Many prominent global corporations try to give equal weight to strategic and financial objectives and create hybrid CVCs, but these give rise to the most difficult implementation challenges. There is no overarching goal to guide decision-making, and such initiatives can fall short on both strategic and financial expectations.

“The challenge is that hybrid CVCs are trying to do something that is ... inherently serving two masters. And they don’t know which one will try to kill them,” Chiang told us. Investment teams also struggle to combine different goals: “It’s hard to force an embedded VC group to change colors on the spot,” he said.

Even CVCs that successfully balance financial and strategic objectives may be shut down when the parent company runs into trouble or shifts direction. GE Ventures illustrates this point. The unit’s founder, Sue Siegel, told us that she felt compelled from the outset to balance financial and strategic criteria. “With no financial discipline, you don’t have anything. … It’s all about the healthy exit,” she said. The GE Ventures portfolio did well, but in 2024, General Electric’s board closed the CVC and divided the conglomerate into three separate companies.

Execution Challenges for CVCs

While each of the investment approaches described above comes with particular execution challenges, the most noteworthy that we saw in our research involved deciding how to measure and realize strategic benefits, maintain financial discipline, and recruit and compensate a top-notch investment team. We’ll review each in turn.

1. Measuring and realizing strategic benefits. Getting an accurate and consistent picture of strategic benefits afforded by their investments seems to be a huge hurdle for strategic and hybrid CVCs. Managers we interviewed used both qualitative and quantitative metrics. Intel Capital analyzed investment success based on the money, time, and effort the company put in, and any strategic benefits and financial returns achieved.5 Vadasz focused on two types of strategic benefits while also trying not to lose money. One benefit was access to startups that had technology Intel wanted to use, such as advanced chip production equipment. The other, as described earlier, was relationships that would increase demand for Intel’s core microprocessor products.

GE Ventures tracked the number and type of partnerships that a portfolio company had with a GE business unit, such as for distribution or commercial product development. It recorded how much money GE Ventures put into the investments and how many employees were involved in supporting partnerships. GE Ventures and GE executives reviewed the portfolio at quarterly meetings.

Based on his experience at Siemens, Andrasco developed a model at Aramco Ventures to estimate what potential value a startup investment might create for the parent company. This model also gave the CVC a basis to compare actual strategic returns — losses avoided or revenues and profits gained.

Realizing strategic benefits requires that a CVC be tightly connected to the parent company. Intel Capital did this through a matrix structure when Vadasz managed the CVC. At that time, 15 to 20 people (of about 100 total employees) were attached to one of Intel’s functional and geographic divisions but worked primarily for him. These employees attended Intel Capital staff meetings, helped with due diligence, and worked closely with portfolio companies to develop their businesses. They were assigned to work with the CVC unit for a minimum of two years and often did so for longer.

Andrasco also relies on a matrix at Aramco Ventures, with about 15 of the 40 CVC employees based in the Saudi Aramco home office doing business development and recruitment for startups. Andrasco considers this structure to be “lightweight strategic,” which he defined as being open to the “possibility of the company and the startup working together … although it may not actually happen.”

As a hybrid, GE Ventures operated more like an independent VC, but with its parent company represented on the investment committee. Siegel invited GE executives to join the committee when the CVC was considering a startup in their business area. The GE executive got one vote but did not have veto power. The three GE business units that engaged most closely with startups assigned their employees to work with them while paying their salaries. In other GE business units, the CTO or chief strategy officer sat on the investment committee for a particular review. If the investment went forward, that executive became responsible for assigning people to serve as “shepherds” and develop a partnership between the GE business unit and the startup.

Our interviews suggest that for strategic-priority CVCs, a realistic target for close relationships or partnerships with the parent company might be one-fourth to one-third of the portfolio investments. But building and maintaining these relationships requires both the CVC and the parent company to make serious commitments in terms of people and time. Acquisitions were another way to realize strategic benefits, but the CVC managers we interviewed saw M&A as a separate corporate or divisional activity.

2. Maintaining financial discipline. Financial criteria are straightforward to implement. The CVC needs to pay attention to cash burn rates and possibly set a threshold floor for “exit value” — the minimal level of desired return should the startup be sold or go public. Determining the exit value requires estimating what comparable startups have sold for or noting what their IPO values have been, or who potential acquirers might be. Establishing value requires the investment team to estimate how far from a commercial product or service a startup actually is, what the competition looks like, and who the likely customers and acquirers might be.

Financial discipline also means spreading out your bets. For example, GE Ventures adopted what Siegel called a layered investment strategy. In the first layer, early-stage investments (Series A and some seed funding) were limited to 20% of the portfolio, given that they might take 10 to 15 years to pay off, while 80% of investments were later-stage — more likely to have an earlier payoff but less likely to have a supersized return. The second-layer investments were in strategic domains, such as health care, advanced manufacturing, or energy startups. The third layer of the strategy was to target syndicate members that might become investment partners. GE Ventures wanted to invest with the top 25% of VCs, such as Sequoia and Kleiner Perkins, based on their returns over the past 15 to 20 years.

Another aspect of financial discipline is to understand what leads to a healthy portfolio. Andrasco looks for a 12% annual appreciation in the value of Aramco Venture’s investments. Similar to Vadasz and Siegel, he has established a modest financial floor because of his experience that “CVCs that lose money don’t stay in business.” Andrasco also insists that CVCs should not negotiate special deals for their portfolio companies and create situations where the parent is the startup’s least-profitable customer.

3. Recruiting and compensating the investment team. These challenges are intertwined, because choices on how to compensate the investment team affect recruitment. We found that CVCs generally struggled to compete with independent VCs on this front. Independent VCs raise outside funds and charge a management fee (usually 2%). They compensate partners with a share of any equity gains (usually 20%), called carry or carried interest. In the U.S., tax authorities treat this type of income as long-term capital gains and impose taxes at a lower rate than for ordinary income. As a result, carry often leads to huge paydays. In contrast, most CVCs compensate managers and teams at a level similar to that for new business development. One alternative is for a CVC to offer large bonuses, sometimes called “phantom” or “shadow” carry, that are indirectly tied to investment returns. Another option is to create a separate CVC fund and compensate with carry, like an independent VC.

Next47 and Siemens Ventures, as well as Intel Capital and GE Ventures, did not compensate with carry, because senior management and board directors would not permit it. In contrast, Aramco Ventures gives out bonuses that incorporate financial returns based on phantom carry and estimates of strategic value achieved.

Intel Capital looked for people from Intel business units who were interested in a temporary assignment in business development compensated via bonuses. GE Ventures looked for talented early-career VCs who had not yet made partner in independent firms. Siegel offered the equivalent of a general partnership, heavy on cash and with long-term GE stock options.

The Bottom Line

We started this research believing that most CVCs should prioritize strategic returns because parent companies have a responsibility to invest in the future and startups can help them do that. We still think that strategic investments are the most valuable bets, especially since CVC financial returns are likely to be small for multi-billion-dollar parent companies. Nonetheless, if a parent company believes that it can make more money from venture capital than from other investments, and it wants to directly influence those investments, then a financial-priority CVC makes sense. In that case, financial CVCs should at least try to take advantage of their parent companies’ domain expertise, because this is their main advantage over independent VCs.

CVCs also need to realize that the objectives and situations of their parents will change over time, which in turn, will impact their missions and evaluations. During our research, for example, Time Warner, General Electric, and Xerox all closed their CVC units, even though we’d been told that the portfolios were performing well. Several companies (including NTT, Samsung, and Siemens) also launched multiple CVCs and funds to achieve different objectives. In early 2023, Microsoft’s M12 venture fund, which started out prioritizing financial returns, announced it was adapting its investment approach to incorporate more strategic considerations.6

There will no doubt always be some tension and change in priorities for CVCs that don’t have clear objectives and performance metrics. Perhaps the biggest challenge for CVCs is to build close relationships with their parent companies, for either strategic or financial investments, while still maintaining enough independence to avoid potentially bad investments.

In this episode of Leaders at All Levels, hosts Katherine W. Isaacs and Michele Zanini speak with Bidyut Dumra, group head of innovation and future of work at DBS, to learn how he’s helped turn innovation from a top-down mandate to one that actually grows from the bottom up. DBS organized its entire operating model around cross-functional “customer journey” teams, led by what Dumra calls “mini CEOs” who have the authority and funding to make real decisions to meet customers’ needs.

Dumra also explains how one customer’s credit card loss sparked an insight that changed everything: Customers don’t think in terms of processes; they think in terms of intents. That single moment, he said, launched the journey-based model that now drives the bank’s organization and growth.

The DBS Playbook: Borrow These Ideas

• DBS benchmarks itself against tech leaders Google, Amazon, Netflix, Apple, LinkedIn, and Facebook instead of other banks, with the goal of being “digital to the core.”
• DBS’s transformation team has a playbook and training at the ready to help teams achieve their KPIs. “The skilling is available to every employee,” Dumra said.
• Some of the bank’s products launched without a business case, with the case built retrospectively, one year after launch. “[If] I know exactly what’s going to happen, I’m not really pushing the needle,” Dumra said.

Hosts Issacs and Zanini dig into how DBS makes distributed leadership work at scale — and what you can borrow from their playbook.

Video Credits

Bidyut Dumra is the group head of innovation and future of work at DBS Bank.

Kate W. Isaacs is a senior lecturer at the MIT Sloan School of Management.

Michele Zanini is coauthor of the Wall Street Journal bestseller Humanocracy (Harvard Business Review Press, 2020).

M. Shawn Read is the multimedia editor at MIT Sloan Management Review.

Today’s episode of the Me, Myself, and AI podcast, the final one of Season 13, explores how Bank of America is preparing a massive global workforce for an AI future through upskilling and reskilling. Bernard Hampton, head of the financial institution’s Academy, explains how the learning and development organization focuses on workforce agility and a building combination of technical and soft skills.

Bernard outlines a three-level approach to adopting artificial intelligence and shares situations in which he feels humans need to stay in the loop.

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

Transcript

Allison Ryder: What learning and development lessons can we take away from an organization upskilling hundreds of thousands of employees on the use of AI? Find out on today’s episode.

Bernard Hampton: I am Bernard Hampton from Bank of America, 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 12 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.

Welcome back to Me, Myself, and AI. Today we’re joined by Bernard Hampton, head of The Academy at Bank of America. The Academy is one of the largest learning and onboarding organizations in corporate America, supporting more than 200,000 employees worldwide. Bernard has a central role in the bank’s effort to upskill, reskill, and prepare talent for the use of AI. Bernard, welcome to the show.

Bernard Hampton: Hey, Sam, thanks so much. [It’s] great to meet you.

Sam Ransbotham: I’m guessing most listeners are pretty familiar with Bank of America. It’s pretty huge. It’s one of the world’s largest financial institutions. I looked [this] up: 70 million clients, 35 countries. It’s huge, but I’m guessing most people may not be familiar with The Academy, which you lead. So can you tell us a little bit about The Academy and how that relates to Bank of America?

Bernard Hampton: Yes, certainly. The Academy [has] existed since 2017. It replaced our legacy learning organization, and it’s Bank of America’s award-winning onboarding education and professional development organization that’s really dedicated to the growth and success of teammates across the enterprise.

At The Academy, we’re laser-focused on workforce agility. By that, I mean it’s about building the right skills in the right roles faster, and we continuously process, improve, and look for opportunities for operational excellence or to bring in new technology or modalities, to be able to hit that mark. That’s really about the mobility, upskilling, and readiness of an AI enabled-workforce.

Sam Ransbotham: You know, we’re kind of the same. I teach a couple hundred students a year, and you’ve got 200,000. That’s about the same, right?

Bernard Hampton: Close.

Sam Ransbotham: The scale seems kind of staggering — the scale combined with the speed of change of everything going on. How do you manage those two things at the same time?

Bernard Hampton: Our academy pathways are really central to technical skills, data and AI literacy, client-facing excellence, leadership capabilities that scale. So at the end of the day, when we think about those shifting priorities across the organization for specific populations, we do a couple of things. Number one, we have an internal, traditional learning skills organization, but at the same time, we match that with subject matter expertise from the business. So within my organization over the last few years, some 750 people have moved from the line of business into The Academy and became a full-fledged Academy teammate, contributing that real-world intelligence to the organization.

Sam Ransbotham: That sounds good, and I like the idea, but it just seems really hard. I think about a year ago, everybody [felt they needed] to learn prompt engineering. And then RAG [retrieval-augmented generation] was the latest thing. Then it just feels like these topics are coming along so quickly. And actually, I could pick the topic of today, but we’re recording about a month before this broadcasts, so it’ll probably be old hat by then. How do you keep up with that? How do you design a process that can respond to that level of agility?

Bernard Hampton: AI certainly has created quite a bit of runway and opportunity for us. It shifted the learning priorities toward faster proficiency in core roles; better critical thinking and decision-making, as you can imagine; stronger communication and relationship skills; and then practical fluency in AI tied to daily work.

When we use AI-based learning modules, it’s not about saying, “Oh, we’re putting an AI tool in front of someone to help aid learning.” It’s thinking in real, practical ways about ultimately who do we serve, what are we trying to accomplish, and then work backward and determine the best solution that allows us at scale to be able to be practical, fact-based, help somebody focus on and develop core skills in a way that is psychologically safe but also engaging.

Sam Ransbotham: You mentioned things like communication skills at the same time you also mentioned AI technical skills. If you think about the spectrum from supersoft skills versus the more technical skills, where are your challenges? What are you having more trouble with? Or how do the challenges differ for each of those types of learning experiences?

Bernard Hampton: Ultimately, it’s been incredibly important that we keep both of them top of mind. I mean, it is easy today, and AI dominates most news cycles. It dominates what you read online. It’s the fun thing to talk about, when the reality is it is not like a toy — companies [that] treat that really seriously are going to start top-down in leadership and developing skill in the space so that it flows through the organization. Those that may be not so serious are going to treat it like a toy that you play with for a while, and then you put it away.

At the same time, what’s operating in the background is this concept that we believe that humans should always take the lead with AI, and so our ability to talk about both simultaneously says that the importance of human skills continues to be really important and a critical differentiator when you start to think about things like empathy, listening, judgment, and decision-making. [They] continue to become incredibly important, while at the same time technical fluency in AI becomes incredibly important.

The other two things that I’d say are a backdrop across both of those is AI will continue to change the way that we work at a faster and faster pace, as we all can imagine, if we’re embracing the technology for what it can ultimately do for us, while at the same time, we have to continue to consider what career mobility looks like and where the workforce is in their skills journey.

As work changes, you may need fewer people to do certain things, but at the end of the day, we are a client business. And we want not only more people to face off with clients but [to] think about, what could more people bring to clients if they were spending less time administratively or on task-type functions, [to] really go support and understand what the needs, the goals, the objectives of the clients are, wherever they are in the spectrum?

Sam Ransbotham: I’m going to get this stat wrong, and so you can correct me, but I think I read somewhere you fill something like 40% of your roles internally. That requires a lot of upskilling and reskilling, I would guess. Is that where the challenge is? Or is [the] goal to use more internal or more external, or how are you thinking about that mix?

Bernard Hampton: You [are] really close. We hired 20,000 people last year, and that includes 2,000 of our student campus hires — 45% of open roles last year almost were filled internally, which further leans into why skilling and upskilling are so important across the organization.

Sam Ransbotham: I was reading somewhere you’re talking about the desire to redeploy talent versus reduce head count. I think there’s certainly a headline out there right now. It seems like every time I look at the news, there’s “Company X has reduced head count by thousands of people, all because of AI.” One, I’m suspicious in the first place that that’s actually due to AI. But I think you’re on the record of trying a different approach versus that reduction. What’s your thinking there?

Bernard Hampton: Our CEO’s been quite clear, and really this is about our clients, it’s about our teammates, and it’s about communities. So when you think about an employer of our size and scale, the knowledge of the organization and our client connectivity becomes really important. For our teammates, yes, we’ve said that over time you may need less people to do certain functions in the organization, but at the same time, there [are] opportunities for reinvestment.

So our opportunity is the recognition that our employees bring a lot of value to the organization. They’ve had a commitment and a level of loyalty, and those [who] want to continue to learn or [who] are curious, [who] are agile, we continue to provide them tools to be able to have a career full of as much mobility as they would like over the course of their careers. At the same time, they carry with them a level of acumen and experience that’s beneficial for our clients and the organization, whether it’s working in risk or if it’s working in a client-facing role. For instance, we want them to be able to continue to bring their best and enjoy doing so. And our employee engagement results bear that out as well.

Sam Ransbotham: You’ve got a massive variety of people within your organization, from super technical to super nontechnical. How do you figure out who needs to know what? That stymies me.

Bernard Hampton: In short, everyone needs to know something. I think in its simplest form, we think about AI in three different levels. At level one, that is about every role and function. That’s about your personal use of tools. It’s about personal productivity. So everyone has access to some version of AI today, to be able to enhance what they do today and think about things like where [they] need to write or analyze information, maybe prepare something, the task-oriented or administrative-type functions. We want them to feel confident and capable, to be able to use AI in creative ways to make their workload simpler.

Now, on one side of that you can certainly say, “Oh, I save a bunch of time. I can take a deep breath and kick back,” but the reality is the best measure of that is what do you turn that increased capability into? And the way that we think about it is, how do we measure the transition of people doing everything from upskilling themselves to expending that time in more accretive activities that are beneficial to the client, that are beneficial to the productivity of the organization, or supporting someone else who takes care of a client?

And then there’s a secondary level of AI that is really about functions. We’ll take unique systems. Maybe there’s one group that needs to use one system most often, and we’ll curate using agents to be able to decipher, pull together, aggregate information that simplifies this one function across a particular group.

And then there’s level three, where we think about large workflows, multiple data sources, multiple agents involved, and that’s usually [at a] large scale and horizontal across the organization.

Well, each one of those has pretty big wins for the organization at the end of the day that builds a picture of productivity. That allows us over time to begin to, as that productivity ramps up, decide where are opportunities for redeployment or where are opportunities that maybe you don’t replace a role, but it doesn’t mean you need to go into a situation as in some companies that generate large-scale layoffs at the end of the day.

Sam Ransbotham: Those levels are interesting, and I’m glad you mentioned measurement, particularly in the first one. I’m sure Bank of America, like everywhere else, has a whole bunch of KPIs that measure what’s going on and measure productivity and efficiency and those things. As I think about it, I worry that the prevalence of those sorts of measures is going to lead us toward really focusing on what you call that level one, which is much easier to measure. It’s going to fit well with the existing KPIs, versus that level three, which seems more cross-cutting. It has the chance of changing balance within organizations.

How do you keep from just making everything a level one type — “Hey, let’s get more efficient and more productive?”

Bernard Hampton: From our perspective, it means that (1) you do them all simultaneously, and (2) on the other side of that, a big part of the work that we do in The Academy is not just aiding in the development of tools and resources to help bring AI fluency across the organization and readiness to be able to use those tools, but also our skills library helps us continually provide opportunities for teammates to invest in themselves. So there’s a balance of what you measure. Sometimes that measurement may be about legacy systems that you want to be able to sunset in terms of newer systems that are more AI-forward or technology that allows you to better communicate with clients.

The other part of that is measuring what’s the [amount of] time that we spend on high-value work? And that’s not necessarily a function of only measuring productivity. You’ve got a couple of different triggers. You’ve got the one that says people will move to do things on their own. And the other is you reduce the number of people who do that work to what’s appropriate for the volume of what’s left over.

Sam Ransbotham: Are there things that you’ve looked at on paper that might be a good place to use AI, but you’ve decided that the risk didn’t pay out? Or how are you instilling some of those guidelines, and where should we be using tools rather than where could we be using tools?

Bernard Hampton: Think about what AI is really good at. AI is really good at research. It’s really good at writing, administrative functions. It’s good at tasks. AI is not good at judgment that requires a human in the loop.

When we think about the what and the how in our training process, we deploy AI to make learning (1) more practical, (2) more relevant, and (3) scalable. So that includes AI-enabled learning experiences such as simulations, guided practice. The Academy leverages AI conversation simulators to help teammates build and strengthen soft skills through interactive role-play and coaching, strengthening along the way. [It’s] AI guided by the way. And then it’s designed to accelerate readiness for teammates across various roles, support career mobility, and ensure human oversight remains central to the learning experience.

In doing so, we begin to somewhat be able to say, “Hey, here’s what AI is really capable of doing very well.” We want to put people in a situation where they experience and improve their skill. The one I’m talking about in particular is an interactive platform that enables teammates to practice real-world scenarios. That’s a great use of AI in thinking about, how do I immerse somebody in a situation in a safe, simulated environment? Ultimately, it builds pride, proficiency, and professionalism.

Sam Ransbotham: Ooh, I really love that. We did some research a couple years ago where we framed it as self-determination. If you felt like you had more authority, if you felt more confident, if you felt like you had better relationships with people, if you felt good about what you’re doing, you’re more likely to use these tools, even though you might think that they may be tools that “replace” us as humans, and that’s not at all the perspective. And I love that simulation aspect.

Do you watch The Good Place, the TV show? I recommend it. I think it’s hilarious. But one of the scenarios in The Good Place is they have someone go through a simulation of breaking up with his girlfriend. You just don’t get that many chances to break up with your girlfriend, and you want to do it right. For the lovers out there, I’ll say that he finds there’s no good way to do it; it’s going to be painful no matter what.

But what you’re talking about there is having people practice things that are hard in safe places, things we don’t get to practice very much. What kinds of things are you putting through this interactive simulation environment? I’m curious about the actual things that people can practice.

Bernard Hampton: These are mostly used in our high-volume, high-paced environments. Think about our contact centers. Think about the 3,500 financial centers around the country and the peaks that happen at different time periods. At every one of them, whether it’s a slow-paced time or a fast-paced time, we need people to exercise great judgment, have a client feel that they’re listened to by somebody who’s empathetic and is working to be able to help them, and at the end of the day is focused on what they want to accomplish.

So it could be anything from cashing a check to performing a complex transaction. We put people in real-life situations that allow them to respond to an avatar that looks like a live walking, talking, breathing client. You get to engage in various scenarios and get feedback [in] real time on your handling of it, on your use of tools and resources in the process, and how you exercise judgment. So that’s one.

Two, when I think about human in the lead, one of the key questions behind developing, training, and using technology correctly is in situations where, let’s say, you process a high-risk transaction five times in a row. If you were using [an] AI of sorts and it said, “yes, yes, yes.” The key question has to be, “Well, what happens that sixth time?” Does Sam look at that and say, “Well, it’s said yes five times in a row. This is probably the same”? The ability to recognize human in the lead is to use scenarios that prepare people to say, “What is it that I should be using?” as the human to validate the accuracy of the recommended action that’s taking place in that moment. So we have to always have those considerations in mind, to both protect our clients, protect the organization, and have the client have a great experience.

Sam Ransbotham: You’ve got a huge organization. You’ve got a lot of resources and scale that you can put into that. What’s the advice to people [who] may not have those resources for developing that level of infrastructure? Do any of these things work well in small chunks, or do they need a big scale to work?

Bernard Hampton: You know what? They absolutely do. In fact, there [are] a few other routines that we have. Yes, there [are] some additional things that we measure, like systems that we want to sunset for another. We measure how many prompts an organization is writing by line of business. So what is the kind of culture of AI adoption by [an] individual group around the organization? Some of those things most would have access to depending on the AI tools that they’ve chosen and what their infrastructure looks like.

But some of the best advice that we get actually happens at multiple levels. We recently did this at a senior-level group around the organization. [We] met with different parts of the organization other than our own, cross-functional groups at multiple levels, and did hundreds of these listening sessions where we brought together 20, 30 people at a time, and began to engage them about their thoughts about AI. What are they finding helpful? What are they still curious about? Where do they need help? All those ideas and feedback generate everything from feedback for my group, as we’re building and developing training; feedback for our technology group and council as we think about what’s next; or the filtration of what are the prioritized major projects and initiatives for the company to invest in next to be able to support our teammates?

So anybody can just simply talk and listen to people when you deploy a tool, and out of that you get the opportunity to prioritize, and that’s a value regardless of the size of the organization.

Sam Ransbotham: I think that’s a great way to think about that. ... Things that don’t require a lot of resources to implement, it seems entirely within the realm of most organizations.

Bernard Hampton: I talk to companies of all sizes. On this journey, being curious has been important to me. It’s been important to my leadership team as well. We meet with some of the largest companies around the world, but we also meet with several midsize and small organizations because out of that I’m thinking about how our clients are potentially thinking about it and what they need. How am I thinking about groups that may be of different size and scale than another? What might they be missing? What might we be missing? That general curiosity and learning from missteps and learning from successes of other companies is just an important place to engage in dialogue and being thoughtful about what do you do first and next so that you’re not just experimenting, but you’re being really intentional about what do you adopt? What’s the reason why that gives people confidence on the other end of why am I experiencing X, Y, or Z next?

Sam Ransbotham: I like [that] you mentioned missteps. I think we’re always too hesitant to admit that we’ve ever done anything wrong. You know, most people other than me have done things wrong in the past. But the idea of getting feedback is really huge, and you mentioned that in the simulation part.

I find students don’t actually mind tests as much as you think they do because they see the things that they don’t know well and where they can improve. We very much like to improve. I think that’s been a theme that’s come through what you’re talking about; let people know what areas to improve and how to improve, and people generally like that.

I mentioned my students. You mentioned 40%, 45% of your people are internal. If I’m doing the math right, that means that 55% or 60% come from external. What kind of advice can you give to people who are entering the workforce now? What kinds of skills should they be thinking about to be an active part of a workforce now?

Bernard Hampton: One, I think you said the right operative word when you mentioned skills, because one thing that’s become clear is that technical skills, or the half-life of technical skills, have become shorter at any time probably in my lifetime as an adult, and to recognize things that we continually talk about in our company, which is clarity, learning agility, and intellectual curiosity. Continuing to keep those things at the forefront [is an] incredibly important attribute.

We talk about them not only quite a bit here at Bank of America, but, specifically, curiosity keeps you relevant, and that’s including about new technologies. When I think about AI today, it should not be a fear of the unknown but the opportunity to embrace something that will be in today’s and tomorrow’s environment just as important as using the telephone or email to be able to do business.

I would say to anybody thinking about their professional life ahead is to be intentional about challenging yourself. Pick stretch work when you have an opportunity, that builds a skill that you can reuse, and then be a great teammate by learning from and sharing with other people. Collaboration is such an important trait in this work environment. Across companies, typically, the days of working in a silo, particularly if you work in a client business, your need for others and thinking about the power of the organization with the client at the center could not be more important. And then, finally, I’d say just continuing to develop human skills and recognize that strength of ethics and judgment and decision-making continue to be ultra-important.

Sam Ransbotham: I like [that] you’ve thought about a lot of these things, maybe in a lot more depth than I have. One of the things we sometimes do in the show, and I think it would be fun for you, is to just ask you a bunch of rapid-fire questions.

What do you think people are getting wrong about artificial intelligence right now? You see a lot of people learning about this technology — what are they getting wrong?

Bernard Hampton: Probably two things come to mind. One is it will go away, that it’s a fad, and then number two, to think that it within itself means everybody’s job’s going to go away.

Sam Ransbotham: What’s moving faster or slower about AI than you thought?

Bernard Hampton: Probably what’s moving faster is adoption, and I think some of that may deal more with the approach that we’ve taken as an organization. I think what’s moving slower — I say slower, but it’s also at an appropriate pace — we’re a highly regulated industry, so we’re always going to be thoughtful as we move.

It’s hard to believe that, just over a decade ago, we didn’t have a client AI solution, but today we have one that clients use more than 169 million times a quarter and growing, quarter after quarter. So I say some things are moving slower, but it’s appropriately measured with the right risk mindset.

Sam Ransbotham: How do you personally get the most value out of an AI tool, just in your daily life? What are you getting the most out of?

Bernard Hampton: I certainly use it to write. I use it to analyze information, and use AI to curate information. I’m always thinking about how we are incorporating and evolving our training solution in a scalable way that fits what they need. Sometimes it’s about individual productivity tools, and sometimes it’s about a vendor or a tool that we might build that is scalable, that will align to how we build the level of proficiency faster than we might be by other means, or maybe differently than what we currently do today.

Sam Ransbotham: Building proficiency faster — that seems like a good way to wrap this up. I think that’s the core of what you’re trying to do. I think it’s a staggering challenge at the scale that you’re trying to do it in. Thanks for sharing your thoughts on it today. Thanks for joining us.

Bernard Hampton: My pleasure. Great to be with you.

Sam Ransbotham: Thanks for joining us for another season of Me, Myself, and AI. We’ve had some really interesting conversations about learning and AI development, and our discussions on the implications of AI on the workforce feel particularly important. We’ve talked with Taylor Stockton at the U.S. Department of Labor, Andrew Palmer at The Economist, and today’s discussion with Bernard. It is hard to pick a favorite! We’ll be back this summer with bonus episodes with a more academic research angle. We encourage you to continue to review our podcast and send us any comments or requests for topics you’d like us to cover. Thanks for helping us make Me, Myself, and AI so successful.

Some of the most spectacular stories of corporate growth revolve around big bets — long-term investments, bold pivots, and major acquisitions. Think of ASML, which pursued next-generation semiconductor manufacturing technologies for more than 30 years; Adobe, which abandoned perpetual licenses in favor of cloud subscriptions; or Disney, which acquired Pixar, Marvel, and Lucasfilm in quick succession.

The companies and leaders that pull off such moves are celebrated as heroes.

But not every company is comfortable making big bets — particularly in volatile times. Our recent research showed that when faced with high-uncertainty events, 90% of companies pulled back rather than doubling down. So, what about a growth strategy not for the heroes but for the rest of us? How can businesses reignite or sustain growth without betting big? It’s a particularly pressing question at a time when economic tailwinds that aid corporate growth are slowing.

To find answers, we evaluated more than 1,200 companies operating in industries structurally challenged on growth, taking a close look at players that grew without relying on high-risk moves. We found that de-risking growth does not rely on making smaller bets, or on making bold moves less frequently. Rather, it requires a different approach at every stage of the growth cycle — from identifying opportunities, to executing on them, to managing risk across a portfolio of initiatives.

Learning From Low-Risk Growth Strategies

To study growth in the absence of economic tailwinds, we focused on industries in which aggregate revenues grew less than global GDP over the past 10 years. As expected, we found that in these challenged sectors, achieving high growth rates (more than 8% annually over our period of investigation) is hard: Out of the more than 1,200 companies we evaluated, fewer than 120 achieved that level of growth. (See “Significant Growth Without Big Bets.”)

Around half of those companies achieved their extraordinary growth by making big bets — major pivots in their business models or industry footprints, or acquisitions exceeding 20% of their market cap. They were rewarded with a median annual total shareholder return (TSR) of 5.5% over the 10-year period we studied, with the top quintile among them even generating a remarkable 13% annual TSR.

However, there was another, similarly sized group of companies that achieved comparable revenue growth rates without making such major moves. These low-risk growers recorded a median annual TSR of 4.2% — clearly outperforming the other 90% of the companies operating in these low-growth sectors, which achieved a median annual TSR of -1.2%. With a top-quintile TSR of 8%, the low-risk growers did not achieve nearly the same upside potential as their higher-risk peers. However, they also limited their downside risk, with only 17% recording negative TSR over the course of the decade — compared with 30% of the big bettors.

Overall, our empirical analysis shows that even in the absence of economic tailwinds, companies can achieve significant growth without taking major risks.

Low-Risk Growth: Four Key Elements

So, what did these successful companies do differently? Across them, we observed four recurring patterns that we think of as components of an operating system for lower-risk growth.

1. Commercializing internal capabilities

When chasing growth, organizations often start from a market perspective — looking for higher-growth sectors that they are not currently serving, and developing products or services tailored to them. However, 33% of the lower-risk growers in our sample instead focused on monetizing internally used assets or capabilities in new ways by offering them as products or services to external clients.

Stride, an education technology company, is one player in our sample that pursued such a strategy. It began as an operator of virtual public schools, using a platform and curricula it had developed. Over time, the company recognized that the learning management system it had built for virtual K-12 schools had value beyond that use. Stride began licensing its technology to school districts, government agencies, the military, and private companies — offering education software as a service for institutions looking to run their own learning programs. This has driven significant growth for Stride.

Under that approach, existing assets that have already been built and internally battle-tested are turned into engines for new growth — with less upfront capital and time investments required compared with greenfield innovation.

Applying that approach, a consumer packaged goods company with strong marketing capabilities might begin selling marketing services; a retailer with advanced last-mile optimization capabilities might offer route planning and carrier selection as a service to e-commerce brands; or a wholesaler with advanced demand-forecasting capabilities might repackage its models into an analytics subscription for manufacturers.

To get this approach right, companies first need to choose the right capability to sell. For one thing, it needs to be valuable — which means that it must be demonstrably superior to existing offerings or capabilities built by others. Often, these are support or back-office functions that others may underinvest in. Yet, this capability should not be a company’s only or most crucial source of differentiation — otherwise, providing it to peers would risk commoditizing competitive advantage.

Walmart’s GoLocal offers a compelling illustration. Having built a vast last-mile delivery network to serve its own customers, Walmart launched GoLocal in 2021 to offer delivery as a service to other retailers. The move works precisely because last-mile logistics, while valuable, is not what sets Walmart apart: The company’s competitive edge rests on pricing, assortment, and the density of its physical footprint.

Once the right asset or capability has been identified, it needs to be turned into a marketable offering. Companies must build a new sales and marketing engine around this product, targeting a distinct set of clients. Pursuing this strategy is particularly sensible for builder-operators — vertically integrated businesses that have developed proprietary systems, tools, or functions to run their own complex machines but remain standardizable enough to be useful to peers in their industry and beyond.

2. Acquiring growth catalysts

Another common strategy companies use when searching for growth is buying market share (by acquiring direct competitors) or buying growth (by acquiring existing businesses in higher-growth industries). But targets with sizable revenues or strong growth trajectories tend to be expensive and demand that buyers pay a high acquisition premium — making acquisitions costly and risky. Moreover, the path to turning this new growth into value is not straightforward: Assuming that the deal is fairly priced, the acquirer will have to realize cost savings or enhance the target’s growth potential beyond what the market had expected.

In our sample, 16% of the lower-risk growers took a different approach to M&As, selecting targets not for the direct revenue uplift they would deliver but for the capabilities they would bring. These players acquired specific technologies, expertise, or access to channels that opened up new opportunities for the existing business.

Like a catalyst that is needed to trigger a chemical reaction, the acquired target adds the missing piece that allows the buyer to use existing assets or capabilities to create new revenue streams. For example, a traditional publishing house might acquire a digital marketing firm to expand its online offering; or a retailer might take over a loyalty-data platform with a strong algorithm to level up its consumer insights.

A case in point from our sample is China Literature, a major online reading platform hosting millions of web novels. It acquired a TV and film studio to turn its most popular intellectual property into dramas and movies. The move combined China Literature’s audience insights with new production capabilities, enabling it to deliver multiple hits that contributed to its 31% annual revenue growth during the past decade.

In our sample, lower-risk growers that applied that approach were able to unlock growth with significantly lower spending on deals: They acquired companies priced at an average of 2% of their own market cap — while higher-risk players, which often acquired growth outright, spent more than 20% of their market cap.

To succeed with this strategy, a company must identify capability gaps that are preventing it from entering new growth verticals. Starting out, it should formulate an explicit thesis: “There is an attractive revenue pool X that we could unlock via pathway Y if we added the missing capability Z.” These pathways should also be mapped before the acquisition to identify where catalysts plug in: products that can be upgraded, customers that can be cross-sold, or channels that can be activated. Then, appropriate targets need to be identified that could bring these capabilities.

These deals are rarely self-contained. Rather, they are treated as a starting point: All of the buyers in our sample increased their R&D investments post-acquisition, working to refine the acquired assets and turn them into marketable products. Often, this effort was led by the acquired company’s team, ensuring that expertise stayed onboard.

Companies pursuing this strategy usually already have a strong core to amplify. When the catalyst can be applied to a sizable base (such as customers, data, or distribution network), its impact is greater.

Moreover, these companies are usually disciplined integrators, possessing the managerial experience and bandwidth needed to embed the catalyst in existing processes and build on it.

3. Jumping on a partner’s bandwagon

As noted above, when a company tries to simply buy market share by acquiring a major player in its sector, the outcome is often mixed. Thus, in our sample, 19% of companies found a lower-risk alternative: They partnered with innovative, growing companies and brought their own legacy strengths, such as large distribution networks, existing customer relationships, or hard-to-replicate physical assets. Such assets can help a new partner overcome challenges in scaling up.

One such case from our sample is home security-monitoring company ADT, which partnered with Google to act as a sales, distribution, and professional-installation channel for Google’s Nest smart home devices. The partnership delivered deep product integration: Customers can link their Nest and ADT accounts and manage them through the ADT app. Since it first partnered with Google, ADT has reported record levels of recurring revenue and over 1 million Nest-related subscribers.

Getting this strategy right involves first bringing a truly scarce asset to the table: Assets like service networks, customer access, or regulatory licenses can be hard for disruptors to replicate. Moreover, the partners need to align incentives.

4. Building a growth portfolio with multiple options

When pursuing growth through higher-risk bets, companies often prioritize the most attractive option and go all in on it. Meanwhile, successful lower-risk growers in our sample pursued an optionality strategy, running a portfolio of bets in parallel. A notable 33% of our sample used this approach. On average, those players launched three new growth initiatives per year. By running many smaller-scale experiments, companies can limit the potential downside of each single option — and reduce the risk of one failed big bet negatively impacting the company’s future.

However, managing a portfolio of initiatives adds complexity for leaders. Companies in our sample used a variety of approaches to pull off this work successfully. Many set up dedicated organizational vehicles for growth. They could take the form of internal “new bets” teams or external structures, such as corporate venture capital arms, innovation hubs, or moonshot factories, with the goal of identifying and incubating new initiatives before spinning them out as full-scale business units.

To ensure that incentives are aligned and that the growth vehicle does not require heavy central oversight, many companies use performance-based contracts for the leaders of such efforts. When these growth units hit their performance marks, they receive strong support from the corporate center — for example, on finance, talent management, or technology. Finally, at the business level, clear “kill rules” must be established so that projects that do not show early signs of success are sunsetted (in order to limit downsides and complexity). On average, companies in our sample either scaled up or abandoned their bets within two years of launch.

Wireless infrastructure provider Sunwave Communications offers an example of this strategy in action. To grow internationally, Sunwave built a dedicated unit that entered North America, Latin America, Europe, and the Middle East through small test-and-learn pilots that scaled only when evidence of initial success emerged. Additionally, the company developed a suite of products tailored to emerging use cases, with a few (such as smart cities, transportation, and manufacturing) demonstrating significant success and contributing to the company’s impressive growth of 26% annually over the past decade.

Individually, each of these approaches reduces risk at a different stage of the growth cycle: in opportunity identification (by capitalizing on what you already have and/or limiting deal size), in execution (by sharing exposure with a partner), and in risk management (by diversifying across bets). By combining them, companies can form a powerful operating system for lower-risk growth.

This course of action requires a very different mindset than a high-risk strategy that revolves around big bets. While big bets in uncertain times can pay off, the reality is that many leaders shy away from this path. Our research reveals a complementary truth for them: Patient, disciplined growth — rooted in existing capabilities, small acquisitions, smart partnerships, and diversified bets — delivers returns well above those realized by peers stuck in the low-growth status quo. Growing in a challenging economic environment, it turns out, is not just for the high-risk gamblers.

What came back wasn’t a technology story but a management story.

Thomas H. Davenport, a professor at Babson College and a fellow at the MIT Initiative on the Digital Economy, struck a cautionary note. He warned that human-in-the-loop oversight of AI tools is becoming performative: “People are being pestered to approve things rapidly, so they don’t really have a chance to engage.” He worries that most humans simply won’t want to serve as auditors of what AI is doing and asserts that no amount of policy will easily fix that.

George Westerman, a principal research scientist and senior lecturer at the MIT Sloan School of Management, said that agents “are not really ready for prime time in most organizations.” He noted that the word agent is being slapped on things that aren’t that sophisticated yet — inflating expectations without delivering value. His advice: Automate where it makes sense, not where it’s easy, and rebuild processes around the desired outcomes.

Watch the video for more lessons from the leaders in the room, including:

• Micro-agents and the trust fabric. Find out how one organization evolved from placing humans at every step to placing them at the right steps.
• In the loop versus on the loop. Acknowledge the fundamental split between agents that execute tasks and agents that clarify what you actually want.
• Building trust gradually. Move from small experiments to full deployment, just as a new driver moves from local roads to the highway.

Video Credits

Abbie Lundberg is the editor in chief at MIT Sloan Management Review.

M. Shawn Read is the multimedia editor at MIT Sloan Management Review.

At the 2026 MIT Sloan CIO Symposium, we asked technology and business leaders to answer this question: What is one thing you do to keep critical thinking sharp as AI takes over more work? Their responses revealed a shared concern and a growing set of practical strategies for fighting back against what some people refer to as “AI atrophy.”

Michael Schrage, a research fellow at the MIT Initiative on the Digital Economy, offered a memorable reframe for AI users: “Don’t view these [AI] outputs as answers — view them as hypotheses that you should test and stress-test.” His approach is to enjoy a compelling AI output for a moment, then immediately challenge the tool by asking for the strongest counterarguments before accepting anything.

George Westerman, principal research scientist and senior lecturer at the MIT Sloan School of Management, suggested that teams consider whether the AI tool was even built for the task at hand before deciding to use it.

Watch the video for more strategies from the leaders in the room, including:

• Protecting time for unstructured thinking. Form your own answer before consulting AI.
• Building in checkpoints. Ensure that engineers, product managers, and architects each verify AI output in their domain before it ships.
• Showing your work. Require teams to demonstrate the prompts they used, the edits they made, and the citations they checked.

The message from these leaders is clear: AI should accelerate thinking, not replace it. Watch the video to hear more about strategies you can apply.

Video Credits

Abbie Lundberg is the editor in chief at MIT Sloan Management Review.

M. Shawn Read is the multimedia editor at MIT Sloan Management Review.

The consulting manager took a call at 7:30 p.m., while volunteering at her son’s soccer practice, from an employee who felt “on the verge of quitting.” Later that same week, she responded to texts sent at 2 a.m. from team members who could not sleep amid corporate restructuring and AI uncertainty. On Sunday, she sent notes of encouragement before the workweek resumed.

This is the reality of a climate in which expectations for leaders to show humanity, compassion, and empathy have intensified. Across industries, employees are feeling stressed, worried about economic headwinds, and unsure how AI will reshape their jobs. Organizational fear has always existed, but it’s becoming more visible as the pace of change accelerates.

Leaders are expected to steady anxious teams, absorb emotional fallout, and respond to employees’ increasing mental health needs. These expectations are redefining leadership roles. Yet the burden is being shared unequally: Women are carrying a disproportionate amount of caring tasks at work, often at the expense of their own well-being.

When we polled more than 350 professional women in managerial roles as part of our research, 81.6% told us they spend at least 30% of their workweek on caring tasks, such as listening to colleagues’ anxieties, offering encouragement, or monitoring how people around them are feeling. That’s more than a business day’s worth of work in a five-day week. Increasingly, such work is no longer incidental. It’s becoming part of how organizations function. This level of emotional labor is equivalent to a part-time job layered on top of a person’s existing formal responsibilities. These findings mirror what we’ve consistently heard in one-on-one interviews and group sessions.

We call this the empathy tax, or care tax: the invisible emotional toll women leaders pay when they shoulder most of an organization’s caring labor. This labor causes care fatigue — exhaustion that stems from constantly absorbing people’s stress, frustration, and anxiety. Care fatigue is rarely discussed in leadership circles, yet many managers recognize it immediately when it’s named. It’s the slow accumulation of small stabilizing acts: calming a worried employee, translating a confusing strategy shift, reassuring a team after another round of change.

To be clear, compassion is a valuable component of leadership; when employees feel seen and supported, that’s a good thing. Compassion has positive organizational impacts, including increasing trust, engagement, and resilience. But when women are expected to shoulder an outsize share of caring work, it undermines their well-being and feeds burnout, exposing companies to higher risks of attrition and disengagement among women in managerial roles.

Care creep — the expansion of emotional and support work that becomes expected but not formally recognized — also tangibly hits organizations as women spend more time on caring work. That’s time that would otherwise be spent on core responsibilities and advancing organizational goals.

An Increasing Burden

The caring burden is clearly growing, according to our research. When we asked women how their time spent on caring tasks had changed since the previous year, 20.1% of respondents said they were spending “much more” time on caring tasks, and 38.8% said “somewhat more.” In other words, nearly 59% of women reported an increase in emotional labor at work. Our findings suggest that, at a time when workplace stress and uncertainty continue to rise, it’s women who are increasingly being called on to absorb the emotional energy of their teams.

People may experience additional or different expectations related to race, ethnicity, and cultural and organizational contexts; this article focuses on the gendered pattern that first prompted our research.  

What about the men? In our early conversations with professionals of all genders, men did not describe feeling the same pressure to provide emotional support. In many cases, men didn’t even observe such work happening around them — whereas women described it as commonplace.

That dynamic shows that emotional labor often goes unnoticed. So to surface its scope and impact, we asked women about the extent to which they were performing emotional care at work. We heard many stories like the one from the consulting manager at the beginning of this article.

Why are the empathy tax and care fatigue hitting women so hard? A large body of research in social psychology and management has found that women are expected to demonstrate warmth and caring in the workplace and are viewed negatively when they fail to do so. Gender norms that associate women with caring, compassion, and warmth are deeply ingrained. A notable 76% of respondents reported that emotional and caring work in their organizations is performed mostly by women, while only 10.6% said it is shared equally and just 1.7% said it falls primarily on men. These findings underscore how deeply gendered expectations shape the distribution of emotional labor, amplifying the pressures on women leaders.

This dynamic isn’t just statistical; it plays out in everyday life. Researcher and author Brené Brown described being stopped by strangers eager to share their most painful and traumatic stories, a dynamic her fellow academic, Adam Grant, said he hasn’t experienced. Despite having similar platforms, they’re expected to demonstrate empathy in very different ways. When one of us shared this example on LinkedIn, dozens of women responded with similar experiences.

Research shows that in occupations where emotional labor is high, women in senior roles report feeling more overwhelmed than their male peers. This dynamic isn’t new, but as the load increases, the labor is spreading. Caring work has long been expected in functions with a high percentage of women, such as human resources and communications. But as societal stress and mental health challenges rise, especially among Generation Z and younger workers, empathy has become a broader organizational imperative and companies are leaning on a larger group of women.

Three Ways the Empathy Tax Shows Up

Here are some of the invisible ways women leaders shoulder emotional labor at work.

1. Absorbing others’ emotions. Gender norms that cast women as naturally warm and attuned to others’ feelings create an expectation, conscious or not, that women will provide support and compassion when colleagues raise concerns or share their challenges. Research has shown that women in managerial roles are acutely aware of these gendered expectations and work to meet them.

These expectations result in female leaders spending significantly more energy listening to others and soothing and managing their emotions, such as stress, worry, and frustration, than their male counterparts do. Some women reported to us that they were expected to have an endless well of emotional availability and the capacity to constantly absorb others’ stories and stresses. The overloaded consulting leader we mentioned earlier said she spent hours holding space for employees after layoff announcements, but her male counterparts weren’t asked to do the same.

This work doesn’t just take time. Whether it’s absorbing stories about difficult or painful experiences, sitting with a crying or angry employee, or being present for someone processing hard news, these types of moments evoke emotions in the listener. It takes a toll on their energy. This work doesn’t end when the conversation does. Leaders carry the emotional residue of these interactions — such as sadness, frustration, and anxiety — with them. As one nonprofit leader put it, “I wasn’t trained in trauma or therapy. I leave these conversations emotionally exhausted and unsure how to set the boundary and not absorb it all.”

2. Getting graded on compassion. Gendered expectations that encourage employees to look to women leaders for emotional support also limit how women can respond and how much space they have to deal with their own feelings. In our respective books, we each interviewed multiple women who spoke about having little room to process their own sadness, stress, fear, or other difficult emotions during times of organizational or societal turmoil.

CEOs may make the formal statement or claim that “the buck stops here” when an unpopular choice is made. But, in practice, they are often insulated from front-line reactions and are rarely expected to engage deeply with employees’ emotions. Managers — particularly women, as our data shows — take on the organizational work of demonstrating empathy, allaying fears, and reassuring employees that they are being heard.

Also, female managers face backlash when they’re seen as insufficiently warm. As a result, women’s performance of caring becomes central to how they are perceived and valued as leaders. They are, in effect, graded on how much and how well they show compassion.

3. Sacrificing time. The different behaviors that make up empathic labor at work, from listening to offering pragmatic support, take not only effort but also time. When women are expected to take on primary responsibility for expressing care within an organization, they must dedicate a meaningful portion of their work hours to meet this demand, our data shows. One problem is that the time women spend filling this role is time taken away from core job responsibilities.

Additionally, many of the women we surveyed said they were often “volunteered” by others on their team to take on caring responsibilities. A finance vice president shared that she was late to a client meeting because an employee became very upset in her office and her colleagues thought it best that she stay until HR arrived.

Like “office housework” or secondary roles such as leading employee resource groups, organizing team morale programs, and mentoring, the emotional caretaker role isn’t formally recognized or rewarded with concrete benefits like higher pay or high-profile assignments. Yet this work plays a critical role in group functioning and supports the common good.

Balancing these caretaker demands with the typical leader’s roster of meetings, emails, and core tasks can quickly lead to overwork. More than a third of our respondents (35.8%) said that the caring work they do in the workplace increases their likelihood of leaving their current role. That is a tangible consequence of the empathy tax for organizations. Moreover, caring tasks limit women’s bandwidth for other leadership work, such as advancing organizational goals and priorities.

The combination of overwork and emotional strain can lead to underperformance, burnout, and, ultimately, attrition — reinforcing the very pressures organizations hope to avoid by calling on women to provide emotional support.

How Women and Organizations Can Address the Empathy Tax

Emotional labor rarely appears in strategy documents or performance metrics, yet it is often the quiet infrastructure that allows organizations to function at all.

For women, the first step is containment. Before reducing your care burden, you need to recognize and reject the harmful narrative that you have to prove your worthiness for leadership by sacrificing your own well-being to meet others’ emotional needs.

Persistent gender norms that expect women to hold endless emotional capacity are the backdrop to this myth, which means that pushing back can be uncomfortable. Remind yourself that your needs — including time to process your own emotions, rest, and pursue your goals — are as important as those of people asking you for care.

This reframe doesn’t mean devaluing care. Indeed, many women we’ve heard from have noted that emotional IQ and warmth are useful leadership traits, even “superpowers” at times. It means deploying your compassion in ways that don’t deplete you.

Next, define and set boundaries that will enable you to feel centered and focused, not drained and scattered in too many directions. These boundaries are going to look different for everyone, depending on role, team dynamics, and personal circumstances. The important part is reflecting on what boundaries will support your success at work and then practicing them consistently.

Remember that your “no” can be a “not now” that respects your time by shifting a conversation to a day or time when you’re not on a deadline. Or your “no” can direct someone to a resource that meets their needs so you can step away. Your boundary might be a clear limit on how long you can talk through a team member’s feelings about a new initiative.

It may help to talk to your peers about their level of care fatigue and collectively reinforce the value of protecting your time and energy. In an organizational culture where many women are taking on a high caring load, one person shifting their approach is swimming against the tide, but a group can create real momentum toward change.

However, the change can’t come just from women: Organizations have their own work to do in taking action to prevent care fatigue from spiraling out of control. First, they should actively assess the extent to which care fatigue is affecting women in leadership roles, whether broadly or in specific pockets of the company. Every manager should be curious about how much time the women on their team — especially women managing others — are spending on caring work. This means both asking directly and observing interactions as they occur.

Managers should also visibly support the boundaries women set in attending to others’ emotional needs — for example, by redirecting any pushback regarding warmth and ensuring that women aren’t positioned as the default emotional resources.

Organizations can also disrupt a culture that leads to the empathy tax and care fatigue in the first place. One approach is rewarding men who step up in a caring capacity. Gender norms often mean that men are criticized for emotional expression or may be seen as weak for exhibiting caring behaviors that are valued in women. By prioritizing empathy and compassion as core leadership qualities for everyone, organizations can reap the benefits of building multifaceted leaders, without placing the burden on women alone. Organizations can help everyone develop a broader leadership toolkit.

Care fatigue is real, and it can derail hard-won career progress for women and for the organizations that want to retain them. The challenge is not whether empathy belongs at work. It is whether organizations are willing to recognize and share the labor required to sustain it. There are ways to address the empathy tax:

• Name it. Acknowledge that the empathy tax and care fatigue exist in your organization.
• Normalize limits. Remove the stigma around a person expressing limits to caring work or seeking support. Leaders should normalize that emotional labor is real and requires intentional management and that caring, empathic leadership is a strength, regardless of someone’s gender.
• Redistribute workload. Design team practices so that all team members share the workload of providing empathy and support. This helps women perform at their best and organizations realize everyone’s potential.

Jean-Christophe Jaunin became CEO of Nespresso North America, the Nestlé unit that sells coffee brewing machines and capsules, on Jan. 1, 2026, after having served as global chief customer and technology officer. At the NYU Stern Center for Sustainable Business’s annual practice forum in March, MIT Sloan Management Review spoke with Jaunin about Nespresso’s commitment to sustainability. This interview has been edited for clarity and length.

How do you make sure sustainability targets don’t get sidelined as the pressure to deliver financial growth intensifies?

Jean-Christophe Jaunin: It’s the foundation of the quality we promise. Every time you’re drinking an espresso from Costa Rica, it will taste like Costa Rica. Yet the inputs — the soil in which the coffee tree grows, the environment — are changing rapidly. Sustainability here means going deeper into taking care of the soil, the climate, the environment in which the coffee grows, so that we can proactively manage change and future-proof our business.

More than 20 years ago, we started to identify the risk that conventional agriculture posed to coffee quality. Traditional farming practices were aimed at maximizing productivity. When mass production of coffee began, the thinking was to get rid of all other plants and just put in coffee trees. What happened is that the soil got poorer and poorer. Poor soil means drier beans, and the whole taste profile suffers. So we started putting back trees to see how a mix of different plants would stabilize the soil. Birds, insects, and other plants come back. This creates a new kind of compost that nourishes the soil, and by enriching the soil, the coffee quality gets better.

How do you convey the value of these changes to farmers who may be used to doing things the traditional way?

Jaunin: We need to create loyalty with them. The more than 150,000 families who are part of our Sustainable Quality Program are independent business owners who joined voluntarily. We have trained more than 600 agronomists to provide farmers with technical assistance and cultivate a direct relationship rather than going through brokers and intermediaries.

With traditional agriculture, if the coffee market was bad, there was nothing else. Now, with biodiversity, they have bananas, they have avocados. A couple of years ago, we partnered with expert beekeepers in Colombia and helped farmers put back beehives. The bees pollinate the coffee [plants], but they also create additional revenue for the farmers through honey. By giving farmers the chance to diversify their revenue, we create a more resilient economic model for them. And that resilience ultimately protects our supply.

Nespresso has taken on significant costs to manage the end of life of its aluminum coffee capsules. How do you justify that?

Jaunin: It is costly, but it’s core to our business model. We made the choice to use aluminum because it lets us vacuum-seal the coffee’s freshness for a very long time. In addition, aluminum can be recycled indefinitely. But because we made the choice to use this material, we need to take care of it.

There are more than 30,000 municipalities in the U.S., so we need to work with local authorities, regional authorities, businesses, recyclers, and composters. There’s the mail-back program, where we prepay the return for customers. In New York, we’ve invested in equipment at a waste management facility in Brooklyn that separates the aluminum from the coffee grounds so customers can simply drop capsules in the regular recycling bin. In Texas, we’re currently testing a pick-up-at-home model: The postal delivery person delivers your coffee and goes back with your empty capsules. It takes time and investment, but we are committed to ensuring 100% of our capsules can be recycled.

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Top Takeaways: Companies that establish a new kind of internal AI organization that researchers have dubbed the “AI spine” are better positioned to expand the scope of use cases, continually improve them, and identify the ones that will improve processes and create real value for the business. The spine model facilitates greater sharing of knowledge and innovative ideas across business units by connecting resources — including users and cross-functional experts — to a flexible technical core. Disciplined project governance keeps resources focused on the areas where generative AI is most likely to have a positive impact.

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