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.

Create Generative AI Value at Scale

Kevin Schmitt, Gregory Vial, and Ivo Blohm

Key Insight: Organizations are expanding their GenAI use by implementing coordinated cross-functional structures that draw on domain expertise and user innovation.

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.

Read the article

Scaling AI With Adaptive Governance

Gianvito Lanzolla, Margherita Pagani, and Christopher L. Tucci

Key Insight: Organizations must implement a new approach to AI governance across a system’s life cycle to manage risks at scale.

Top Takeaways: As organizations adopt AI systems across business functions, they need to manage increasingly complex risks not only during the development process but also after deployment. Leaders should start by identifying the risks their organization faces and the controls needed to manage them. Then, by adopting adaptive AI governance practices, they can continually realign AI with organizational needs as those systems scale. Organizations that embed risk controls into operations, overcome cross-domain barriers, and institutionalize continuous learning and improvement will have an advantage over those that don’t.

Read the article

Why AI Isn’t Transforming Finance Yet

Stijn Viaene, Kristof Stouthuysen, and Bjorn Cumps

Key Insight: CFOs must adapt their leadership approach to balance finance’s traditional role with the use of AI to help shape organizational strategy.

Top Takeaways: Finance offices have been slow to meaningfully adopt artificial intelligence, often due to a narrow perception of the function’s role as a steward of discipline and consistency. When finance leaders and their teams realize how AI can help them stay alert to changes in the business environment, experiment in the course of their work, think differently about the future, and embed new practices in their everyday processes, they will begin to see opportunities for using AI as a tool that supports broader organizational change.

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Why Businesses Should Experiment With Quantum Computing Now

Avi Goldfarb and Florenta Teodoridis

Key Insight: Quantum’s benefits won’t materialize overnight. Companies that start experimenting today can gain a competitive edge.

Top Takeaways: Companies shouldn’t wait until quantum computing technologies have reached maturity to invest in them. As an enabling technology, quantum requires hands-on experimentation, feedback loops that support incremental learning, and co-invention cycles between producers and users — over time — to identify practical use cases. Investments in quantum today may see near-term payoffs, but the focus should be on active learning and the potential for breakthrough innovations over the longer term.

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Level Up Your Crisis Management Skills

Rick Aalbers, Killian McCarthy, and Arjan Groen

Key Insight: Leaders can become more adept at responding to crises by developing stronger skills in seven critical practice areas.

Top Takeaways: People who have successfully managed crises in governments and large organizations aren’t innately better at it. They’ve learned to apply critical crisis management practices. Interviews with high-level leaders in a variety of industries found that organizations with strong crisis management capabilities have invested time and effort to develop maturity in seven key areas researchers have dubbed the “7C’s”: contingency planning, cross-functional coordination, transparent communication, compassion, confrontation of hard truths, control, and continuity.

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Data Transformation Is the CEO’s Business

Barbara Wixom, Ogi Redzic, Brandon Hootman, Joaquin Rodriguez, Gabriele Piccoli, and Cynthia Beath

Key Insight: Caterpillar’s data overhaul shows the essential transformation work that CEOs and senior leaders must commit to for AI readiness.

Top Takeaways: A multiyear data transformation project at Caterpillar that made the heavy-equipment manufacturer AI-ready provides an exemplary case for what leadership commitment to such a technology project involves. CEOs must go beyond communicating abstract intentions by setting a tangible, strategic business goal that the transformation will support; giving teams realistic time horizons and adequate resources; and assigning meaningful, instrumental roles to members of the leadership team.

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What It Takes to Scale Value-Based Industrial Solutions

Johan Frishammar and Vinit Parida

Key Insight: Manufacturers can successfully build upon value-based sales pilots by using a framework centered on six core capabilities.

Top Takeaways: Industrial equipment manufacturers moving to a value-based sales model often find that delivering initial solutions on a one-off basis is relatively straightforward. The real challenge lies in scaling those solutions to more customers, which requires structured, repeatable processes and strong, entrenched capabilities. New research points to two important phases of capability building — scaling prerequisites and scaling execution — and identifies the organizational skills, processes, and relationships that successful companies assemble.

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Gain Consumer Insight With Generative AI

Neeraj Arora, Ishita Chakraborty, and Yohei Nishimura

Key Insight: Large language models can transform marketing research by enabling faster concept testing, qualitative research, and data analysis at scale.

Top Takeaways: Typical marketing research efforts can cost tens of thousands of dollars and take months to complete. LLMs are starting to change the industry by compressing timelines from months to days. How? By enabling the development of synthetic consumer “digital twins” for rapid concept testing, the use of AI-moderated interviews for qualitative research at scale, and the ability to conduct powerful analyses of unstructured data. These LLM-based AI tools allow smaller research teams to conduct larger studies while maintaining quality, thus enabling more frequent testing and experimentation.

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How Leaders Can Move Past Personal Obstacles

Katherine W. Isaacs and Richard C. Schwartz

Key Insight: Leaders can overcome conflicting motivators that hinder their effectiveness by applying psychotherapeutic tools while managing others.

Top Takeaways: Professional growth involves acknowledging and releasing beliefs and behavioral patterns that have been interfering with good decision-making or strong working relationships. A leadership development expert and psychologist explain how simple techniques drawn from the Internal Family Systems psychotherapy approach can help leaders shift persistent attitudes and behaviors through greater self-awareness and cultivate greater compassion, curiosity, clarity, creativity, calmness, confidence, courage, and connectedness.

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Resolve the Conflict Between Efficiency and Resilience

Vishal Ahuja, Yasin Alan, and Mazhar Arıkan

Key Insight: Fine-tuned buffers and adjustments to performance metrics can strengthen operational resilience without sacrificing efficiency.

Top Takeaways: Studies of the airline industry show that achieving resilience doesn’t have to come at the cost of efficiency. Managers in a variety of industries can meet both objectives by ensuring that operational performance metrics reflect true customer priorities; using predictive analytics and data-driven insights to allocate system buffers where they generate the most meaningful resilience benefits; and shaping the options offered to customers to improve the organization’s resilience to disruptions.

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It’s safe to say that most people who rise to the top of their companies like to win. A healthy competitive streak is energizing and motivates individuals and teams to do their best — to find their edge and sharpen it. But sustained, long-term success and industry leadership often rely on the ability to look beyond your self-interests and see where, in the bigger picture, your contributions to building up your sector can make your own company stronger.

That theme emerges from several articles in this issue, although in different contexts. We see the benefits of early-stage engagement with emerging technology, the buffering effect of business ecosystem connections in a crisis, and how one company models sharing lessons learned with a community of practitioners.

On the emerging-technology front, Avi Goldfarb and Florenta Teodoridis issue a call to managers to get off the sidelines of quantum computing and contribute to developing practice. The authors remind us that with all enabling technologies — as was the case with the internet and electricity — value is cocreated by early users of the technology. Users’ experiments contribute to feedback loops that identify promising application areas and clarify how the complementary ecosystem around the enabling technology needs to evolve.

Goldfarb and Teodoridis emphasize that in quantum computing, this is a collective effort among organizations that apply the technology and those that aim to supply it. Practitioner involvement now, even though it’s early days, is crucial to the development of the ecosystem that must grow up around this technology to make it broadly usable by businesses. Companies that get involved in and contribute to this development will certainly be helping others, maybe even their competitors, but they will also be demonstrating that they are leaders in their sectors.

Cultivating the ecosystem around your company can also improve your resilience in a crisis. Rick Aalbers, Killian McCarthy, and Arjan Groen interviewed senior leaders across government, multinational companies, and the military to understand what made them successful at crisis management. From their research, they distilled not a set of heroic personal traits but rather a framework of organizational capabilities that those leaders had put in place well before a crisis arose. Those leaders were able to weather storms because they could rely on well-prepared people and well-tested processes when disruption occurred. One subtle but significant element that isn’t part of the framework but does show up in crisis stories: Having good working relationships with other players in the broader business ecosystem clearly gives leaders more options when the chips are down.

Finally, one of the most significant ways to contribute to the health of your business ecosystem is to share your own hard-won lessons. That’s not the subject of our article about how CEOs need to engage deeply with data transformation initiatives. But the article itself is a result of Caterpillar’s willingness to allow researchers (led by Barbara Wixom, principal research scientist at the MIT Center for Information Systems Research) to follow its massive, multiyear transformation in great depth. The company permitted executives involved in the initiative to join the researchers in writing about their experience for MIT SMR in order to share what they learned with the wider practitioner community. That kind of generosity contributes to collective learning via an ongoing conversation among practitioners and scholars that, over time, improves management practice to everyone’s benefit.

Artificial intelligence was supposed to dramatically change the corporate finance function. Forecasts would become more accurate and more frequent. Closing cycles would shorten. Risks would be identified earlier. Scenario planning would evolve from an occasional exercise into a continuous capability. On the basis of those optimistic predictions, many finance leaders have invested heavily in the technology.

However, when CFOs speak in private, a different story emerges. There are proofs of concept that never leave their sandboxes. Models that looked promising in pilot sit unused when the pressure of the quarter hits. Dashboards are produced and refreshed but rarely shape the decisions that matter most. Finance is undeniably busier and more automated but not obviously more forward-leaning in how it helps the organization adapt.

It is tempting to blame the shortfall on technology issues: The data quality is not there yet; the tools are not sufficiently integrated; the models are not trusted; the vendors overpromised. All of those factors matter, but they do not explain why similar AI technologies, introduced under broadly comparable conditions, lead to very different outcomes in finance than in other corporate functions.

After several years of working closely with CFOs and their teams as they tried to apply AI in practice, another explanation became hard to avoid: In many organizations, the technology is moving faster than the way leadership actually works inside the finance function.

When new tools arrive, people tend to talk, decide, and behave much as they did before. Attention gravitates toward getting the close done, explaining variances, defending a single forecast number, and treating deviations as errors to be corrected rather than signals to be explored. AI is introduced into that environment and expected to transform it. Most of the time, it does not.

To understand why, and what might be done differently, it helps to look less at technology adoption and more at leadership practice. (See “The Research.”) In studying the question of AI adoption in finance, we took a simple but demanding view of leadership: Leadership is not a job title or an individual trait; it is the work through which people help their organization adapt under uncertain and changing conditions. In finance, that work shows up every time someone reframes a question, tries a different way of seeing the numbers, surfaces an uncomfortable signal, or helps colleagues adopt a better routine.

Viewed this way, leadership does not sit only with the CFO or a small circle of direct reports. It can be exercised by an analyst who notices something unusual and asks, “What might this mean?”; by a controller who proposes a trial of a different forecasting driver; or by a planning manager who brings several futures into the conversation instead of converging on one.

Leadership becomes visible in the way practices are introduced, tested, and shared. It also becomes visible in who feels empowered to initiate and sustain that work. This view aligns with broader work on digital transformation that frames leadership less as top-down control and more as the orchestration of attention, accountability, and learning across the organization.1

This way of looking at leadership has a sobering implication for AI in finance. If leadership is understood primarily as the CFO’s personal competence or as the formal hierarchy’s right to decide, then the function’s capacity to experiment, learn, and embed new ways of working will always be limited. If leadership is instead understood as shared work in practice, then AI becomes an opportunity to reshape how that work is done. However, the very nature of finance work itself can raise challenges for AI adoption.

When Finance Is Pulled Into a Paradox It Did Not Choose

Finance has always lived with a tension between control and change. Its core mandate is to ensure reliability: accurate reporting, regulatory compliance, and disciplined stewardship of capital. Over time, the function has built processes, controls, and habits designed to reduce surprise. A great deal of finance’s professional ethos is shaped by the imperative of not being caught out.

AI introduces a different dynamic into this environment. It allows finance teams to see more, and earlier. It makes it possible to scan wider sets of signals, test alternative assumptions at low cost, and explore uncertainty in ways that were previously impractical.

The result is that finance is pulled more deeply into a paradox it did not choose. It remains responsible for being the organization’s safe pair of hands while at the same time being asked to become more curious, experimental, imaginative, and adaptive. Finance must protect what is working, even as it helps reinvent what may soon no longer work.

Some finance functions have learned to live with this paradox. They develop ways of working that keep discipline and exploration in constructive tension. Others fall to one side or the other: They either protect the familiar and treat AI as an efficiency add-on, or they embrace every new tool and struggle to make anything stick.

What makes the difference is not simply the tools they buy but the pattern of leadership work that emerges inside the function.

Across many engagements, we saw four recurring activities that particularly mattered for finance teams learning to work with AI: staying alert to what is changing, experimenting in practice, thinking differently about the future, and embedding what proves useful. These are not stages in a process. They are different ways that leadership shows up in everyday finance work. Here, we will present four vignettes, drawn from our research, that show how leadership work around AI takes shape in everyday finance practice. Details have been anonymized and, where necessary, combined to protect confidentiality, but each vignette reflects patterns we observed repeatedly across multiple organizations, rather than isolated or exceptional cases.

When Vigilance Becomes Shared Work

At a European manufacturing company, the central finance team had invested in a sophisticated data platform that provided access to a wide range of external market and supply chain indicators. Over time, the volume of available information increased, but much of it remained in the background. The data was technically accessible, yet it was rarely featured in the conversations that shaped plans or decisions.

That began to change when a financial planning and analysis manager proposed a small adjustment to how the team worked. Each Monday morning, two analysts were asked to bring one external signal they considered potentially important to a short discussion with colleagues. The conversation always started from the same question: “If this were the first sign of something bigger, what might it be?”

AI made it possible for the team to scan a much broader range of signals than before and to narrow that field to a manageable set for discussion. The more significant shift, however, was behavioral. Analysts and controllers began to see paying attention to early signals as part of their everyday responsibilities rather than as a specialist task. Over time, the team’s discussions started to influence budget assumptions and the way scenarios were framed for business partners.

No one on the team described this as leadership — yet it was. What changed was not the technology but the shared responsibility for noticing and interpreting what might be changing around the business. We observed similar practices across multiple finance teams. Where this shared vigilance took hold, AI was experienced as practical support embedded in everyday work rather than as an abstract promise. Where it did not, AI-driven signals tended to remain peripheral: available in dashboards, discussed in isolation, or quietly ignored when core planning routines took over.

When Experimentation Becomes Routine Rather Than the Exception

At an international consumer services company, the finance director had grown skeptical of large transformation projects that promised to reinvent planning with AI and delivered little beyond stress. Instead of launching another program, she encouraged her teams to think in terms of small, bounded trials that would generate insight without putting core processes at risk.

During one quarter, a business unit controller proposed running an explainable forecasting model alongside the standard statistical one. Using machine learning, the model suggested different revenue drivers for a specific product line. The trial was deliberately constrained: It would run for two sprints, it would not influence formal guidance, and success would be assessed not only by accuracy but by what the team learned.

The experiment did not outperform the existing model. What it did produce was a clearer understanding of which data the team actually trusted, where the conventional approach was more robust than expected, and where there might be room to rethink drivers in the next cycle. Crucially, no one was criticized for having wasted time. In the review meeting, the CFO asked a different question: “What did we learn that we could not have learned otherwise?”

That question quietly reset expectations around experimentation. Over time, teams began to treat trials not as projects to justify but as part of how finance learned. This mattered even more when generative AI tools became available.

In many finance functions we observed, the arrival of generative AI initially led to highly individual and largely invisible experimentation. People tried out tools on their own, unsure of what was acceptable, wary of failure, and sometimes concerned about what the technology might imply for their own role. Learning remained fragmented, insights stayed personal, and little of that experimentation translated into changes in shared routines.

In this team, the dynamic unfolded differently. Because experimentation had already been legitimized as collective work, generative tools were folded into the same discipline. Teams openly tested where AI-generated explanations or variance narratives genuinely improved shared understanding and where they merely added fluent but unhelpful noise. The tools were kept in a supporting role, helping teams reflect on results rather than replacing human judgment.

More broadly, we found that where AI remains peripheral, experimentation is treated as a temporary deviation from “real work.” Where AI begins to change practice, experimentation becomes part of how finance operates: disciplined, bounded trials carried out by people close to the business, using AI to learn what works, what does not, and where judgment must remain firmly human.

When the Future Becomes a Subject for Conversation, Not Prediction

At a regional utilities provider, the leadership team had always expected finance to deliver a single forecast that captured where the business was heading. As volatility increased in energy markets, the numbers they generated became harder to defend. After a series of painful forecast misses, the CFO tried a different approach.

Using AI-enabled scenario tools, the planning team constructed a handful of plausible futures for the next three years, each built around different combinations of input prices, regulatory decisions, and customer responses. Instead of producing one projection, finance brought several stories to the executive table, each with its own numbers and early-warning indicators.

The discussion shifted. Rather than debating which forecast was “right,” executives began asking, “What should we do if this scenario starts to materialize?” Finance’s role changed accordingly. It was no longer expected to predict the future with precision but to help the organization think through alternative futures and make deliberate strategic choices in the face of uncertainty.

AI made it easier to construct and analyze scenarios in greater depth. But the leadership move was to make it legitimate for finance to acknowledge uncertainty and to connect that uncertainty to concrete choices.

We observed a very different dynamic in finance teams that remained focused on prediction alone. There, AI was primarily used to refine a single forecast, optimize existing assumptions, and reduce apparent error. Paradoxically, this often increased people’s defensiveness. When models were challenged, teams responded by tightening assumptions rather than widening the conversation. Uncertainty was compressed into confidence intervals, and alternative futures were treated as distractions rather than inputs to decision-making. AI made the forecast more sophisticated but not more useful.

When Good Ideas Are Helped to Spread

At a large retail group, a finance team in one country had developed an AI-assisted routine for identifying unusual patterns in store-level expenses. It helped the team distinguish more quickly between genuine issues and benign anomalies. For some time, this remained a local success, known mainly to the people directly involved.

The practice began to travel when the organization created space for teams to share how they worked, not just what they delivered. During an internal learning session, a finance manager from another country heard about the routine and asked for the code and the checklist the team was using. Within weeks, a slightly adapted version was running in two more countries.

Crucially, this early diffusion was noticed and reinforced. Several months later, someone in the head office suggested integrating the approach into the retail group’s standard monthly review, with a small number of governance guardrails agreed to jointly with internal audit. That move did not mandate adoption, but it signaled that the practice was legitimate, safe to reuse, and worth building on.

Nothing in this process required a major program. The AI component itself was neither complex nor revolutionary. What mattered was that people saw it as acceptable to borrow and adapt one another’s ways of working, and that senior leaders took an active interest in how a good idea could become normal practice rather than remain a local innovation.

The Quiet Power of the CFO

Across all of these patterns, including shared vigilance, routine experimentation, strategic direction setting, and the spread of local practices, the CFO’s influence is both indirect and decisive.

Some of it is visible: The CFO decides where to invest, which initiatives to sponsor, which skills to hire for, which projects to stop. These choices shape what is possible. But another part of the role is less obvious and, in the context of AI, just as important: the tone they set about what counts as “real work” in finance.

When a CFO consistently asks only about accuracy and speed, people learn that the safest way to succeed is to avoid anything that might introduce uncertainty. When a CFO shows interest in what teams are learning from experiments, or in which weak signals might matter, people learn that thinking and trying are also part of their job. When a CFO insists that every pilot demonstrate a clear return on investment before it starts, experimentation dies in the planning stage. When a CFO is prepared to back a modest trial to see what happens, even if the payoff is not guaranteed, experimentation becomes possible.

Leadership in this sense is not dramatic. It is expressed in questions, in the allocation of a little time here and there, and in the willingness to protect a practice that is still fragile. Over time, those small acts accumulate. They determine whether AI finds a place in the real routines of the finance function or remains stuck in presentations and proofs of concept.

What the Numbers Can Reveal, if Read Differently

Our earlier work on digital finance maturity examined which organizational conditions tend to be present when finance teams make sustained progress with AI, such as ongoing experimentation and clear accountability for decisions supported by models.2 Our follow-up work showed why many teams nevertheless stall as AI expands: Those conditions often fade in day-to-day routines, even when the technology itself continues to improve.3

The evidence in this article adds another layer. It shows how those same conditions are not abstract capabilities but rather the result of everyday leadership work inside the finance function. Whether experimentation, accountability, or learning persists depends less on formal design choices than on how people are encouraged, protected, and listened to in practice — and on how those same people, in their everyday work, help shape and carry new practices forward. The constraint, in other words, is how leadership work takes shape.

Finance leaders are used to interpreting diagnostic surveys on digital maturity or AI readiness at face value. Read that way, such data shows where functions have invested, which tools they have adopted, and how they assess their own progress.

There is another way to read the same data: as indirect evidence of where leadership work is, and is not, taking place.

When external data rarely influences planning discussions, the issue may not be data availability but whether shared vigilance is part of everyday work. When many organizations report running pilots but few of them see changes in core forecasting or planning routines, the problem may lie less in experimentation than in the ability to turn local learning into shared practice. And when finance professionals say they have access to advanced tools but hesitate to surface the uncertainties those tools reveal, the constraint is often one of leadership tone and permission rather than technical capability.

It is important not to overstate what such numbers can prove. They do not establish causal relationships between specific leadership behaviors and AI outcomes — but they do reveal patterns. Read carefully, they make it plausible that differences in how leadership shows up in everyday practice help explain why similar technologies produce very different results across finance functions.

Changing How Leadership Works Inside Finance

AI will continue to advance. Tools will become more accessible. Vendors will refine their offers. Regulatory expectations will grow. None of that guarantees that finance will transform. What will matter is whether finance functions change how leadership works.

If leadership remains concentrated in a few places and focused primarily on protecting existing routines, AI will mostly be used to continue old ways of working, only faster. If leadership is understood as shared work on practice, such as watching the horizon, trying things in small ways, shaping strategy under uncertainty, and helping good practices spread, then AI can become a powerful ally in reshaping what finance does and how it contributes.

CFOs do not need to brand any of this as a new leadership model. They do not need to talk about frameworks at all. They do need to ask themselves some plain questions:

• Who in our function feels responsible for noticing and discussing early signals?
• Where do people feel safe experimenting with new ways of forecasting or analyzing risk?
• How do we encourage thinking about alternative futures rather than defending a single number?
• What do we do to ensure that local innovations become shared practice when they prove their worth?
• What am I, as CFO, doing day to day to make those forms of leadership more likely, or less?

The latter question is perhaps the most important. But until the answers to all of those questions begin to change, AI will keep pressing against the glass of the finance function without fully entering the room. When they do change, the technology that once felt like an external pressure will start to feel more like an instrument that teams can pick up and use as they learn together how to work differently.

Leaders with even a cursory understanding of artificial intelligence know that while the technology can help them improve productivity and capture new opportunities, it can also expose their organization to many risks. Those with a bit more knowledge are aware that surfacing and mitigating those risks requires adopting responsible AI practices. And leaders who are scaling an AI implementation within their organization will quickly realize that ad hoc attention to those practices is inadequate and that they need to develop the capacity to systematically govern AI at scale.

But building that capacity is proving far harder than most executives expect. They know what they need to accomplish; frameworks from governments and regulators define important guardrails and principles, such as transparency, fairness, and accountability.1 But to implement controls and principles into day-to-day workflows and decision-making, organizations must rethink AI governance. They must frame that task not as a compliance obligation but as a strategic, adaptive capability that evolves as AI systems scale, use cases expand, and risks shift over time.

In this article, we will share how leading organizations are doing exactly that. We will also introduce an approach to adaptive AI governance built on two principles: matching governance controls to the type of AI system and risk involved, and embedding those controls directly into workflows, decision rights, and accountability structures.

The Fundamentals of AI Risk

To design effective AI governance, leaders must first understand the multiple ways in which AI can fail and the corresponding risks. The nature and severity of these risks depend on the type of system, its level of autonomy, and the scope of domains affected by its decisions. The central challenge, therefore, is to design controls that anticipate how risks will emerge and that evolve as AI systems operate. Even as conditions, inputs, and expectations change, AI must remain reliable, safe, and aligned with an organization’s values and goals.

In practice, most AI risks emerge at two moments that require very different governance responses: during development and after deployment. Development risks include using biased or incomplete training data, failing to adequately align the model to the task requirements, and following inadequate validation processes. For example, an early credit-limit-increase model at a bank we studied demonstrated that small input changes could lead to unexpected decision shifts.

Deployment risks arise when models interact with dynamic environments and human operators: Sustaining legitimacy, judgment, and accountability once AI systems are operating at scale in real time is a central challenge. Over time, model quality may degrade as the statistical properties of input data change over time, a phenomenon termed data drift. A model may generate plausible but false outputs or be overly trusted by users who lack the means to detect errors. At Nasdaq, AI-driven market-surveillance systems monitor trading activity for suspicious patterns, generating hundreds of alerts per second. Those systems may fail to accurately flag activity, however, because the boundary between abnormal and illicit behavior is often hard to spot; illegitimate behavior may be deliberately designed to pass as compliant by exploiting model learning patterns.

Fit-for-Purpose Controls

The kinds of controls employed depend not only on when risks arise in the AI life cycle but also on what kind of AI system is involved and how widely its decisions propagate. Artificial intelligence systems can be broadly divided into two categories: those based on bounded-learning (or static) models and those that learn and adapt in deployment. (See “Controls in Adaptive AI Governance Systems.”)

Bounded-learning systems operate within a fixed set of rules and parameters. Optimizing how those rules are applied, rather than changing them, is what improves their performance. Credit-scoring models, for example, refine risk estimates based on income or payment history, but they do not alter how those variables relate to one another. Many generative AI models are “pretrained” (static) and do not get updated during use. Contrast that with adaptive learning systems, which evolve by incorporating production data into their training data and by updating internal representations and relationships between variables. Algorithmic trading platforms and dynamic fraud-detection systems illustrate this approach.

Just as salient to the type of control required is the scope of domains affected by AI decisions, shown on the vertical axis of the figure “Controls in Adaptive AI Governance Systems.” This dimension determines how far and how fast risks can travel once a system goes wrong. At one extreme are narrow-scope systems, where errors remain contained within a specific function or task (such as detecting anomalies within a single transaction stream). At the other extreme are wide-scope systems that shape outcomes across multiple functions, geographies, or even industries, such as cross-border supply-chain optimization platforms. The difference is not incremental but exponential: As system reach expands, small errors interact, propagate, and amplify into second-order effects.

Based on our typology of AI systems, we believe that rules-based controls provide the baseline safeguards for all narrow, static AI systems. When such static systems operate at a wider scope, additional propagation-risk controls must be layered on to address broader downstream effects.

For adaptive learning systems, baseline safeguards remain necessary but must be complemented by ex post alignment controls, particularly those focused on explainability and legitimacy. When adaptive systems also have a wide scope, they require the most comprehensive approach: integrated controls that combine baseline rules-based measures with propagation risk management and alignment mechanisms. Let’s take a closer look at how each of them works in practice.

Rules-Based Controls

Rules-based controls are designed to prevent and correct errors in systems that operate within clearly defined parameters. They are particularly effective in narrow decision domains where logic is explicit and outcomes auditable, such as credit scoring, fraud detection, or the use of customer service chatbots. Rules-based controls embed relevant norms (such as ethical guidelines and industry standards) and compliance requirements into models, using them as design constraints. Rules-based controls also include processes such as validation testing or anomaly monitoring.

Consider the credit-limit-increase decision model mentioned earlier. A senior AI leader at the bank explained that it uses a statistical model rather than deep learning so that decisions remain interpretable. Before deploying a new model, the analytics team produces documentation called a model card that covers three aspects of AI risk management. First, data checks indicate whether the training data is complete, recent, and balanced and how the team will detect data drift over time. Next, decision logic and edge cases are checked to see how scores translate into approve/deny decisions; this includes explicit analysis of thresholds where a customer tips from no increase to an increase, so that customers in the “gray zone” are not unfairly treated. Finally, bias and discrimination tests are undertaken to check that the model does not overfit to particular customer profiles or systematically disadvantage certain groups.

The model card undergoes quality-assurance review by an independent model-risk unit, with input from credit-domain and regulatory experts. Internal auditing later verifies that these steps were followed. Only then does the model go live.

Human judgment is central even in rules-based settings. In one organization, each new lending model for midmarket clients underwent sample testing before deployment. Risk teams selected 100 existing client files and ran them through the model. Relationship managers then compared the model’s recommended lending decisions with their own assessments. Where recommendations diverged, the model team investigated whether the model had uncovered a genuine insight or was overfitting to idiosyncrasies in the data. Only once the sample review showed an acceptable level of alignment between model outputs and the judgments of the domain experts involved — and the sources of disagreement were understood — did the bank approve the model for live use. After launch, periodic sample reviews continued as part of the standard risk-and-control cycle.

Rules-based controls are effective because they make critical decision boundaries explicit, reviewable, and contestable across domains. They are adaptive because they can be recalibrated over time. Divergences between model outputs and expert judgment are treated as learning signals, feeding back into updated model thresholds, assumptions, and review routines as data, models, and decision contexts evolve.

Ex Post Alignment

The complexity of advanced AI systems, particularly those based on deep neural networks, render traditional traceability and explainability methods less effective. Rules-based controls depend on the ability to specify decision logic, yet that logic becomes increasingly opaque as models grow more complex. As a result, stakeholders must ensure that outcomes remain reliable, fair, and aligned with organizational and regulatory expectations. When such systems operate with significant autonomy, this need for explainability becomes especially critical, since decisions may be made and acted upon without immediate human review. Generative AI introduces an additional layer of difficulty because of its stochastic behavior, where the same prompt may yield different outputs.

This is where ex post alignment controls come in. They reveal not how a decision was made but whether its outcomes remain legitimate. They assess AI decisions against ethical, regulatory, and domain-specific standards. While some techniques carry over from rules-based approaches, the emphasis shifts from preventing errors upfront to detecting misalignment as systems operate, learn, and scale.

Organizations operationalize ex post alignment through layered evaluation processes that test outcomes against reference expectations. Microsoft, for instance, has developed a structured evaluation pipeline in which high-stakes models are assessed against libraries of expert-defined policies — such as what constitutes a “fair” or “acceptable” outcome. Evaluators annotate model outputs against these policies, while independent reviewers validate where the system falls short. In some cases, these evaluations can be partially automated — for example, when AI systems are continuously assessed against predefined policy benchmarks, fairness constraints, or risk thresholds, with automated monitors flagging deviations for human review.

This is why algorithmic auditing is a critical component of ex post alignment. After its deployment, a model’s behavior is systematically examined to detect hidden risks, evaluate fairness and performance across affected groups, and verify that outcomes align with organizational policy and ethical standards. Auditing proceeds in two steps. First, auditors identify plausible failure scenarios and define the full use case, including whom the system serves, who is affected by its decisions, and for what purpose it operates. They then monitor these risks by assessing decision outputs, input data, and internal logic against predefined criteria. This process helps organizations surface unintended consequences, such as disparate impacts; document recurring risk patterns; and trigger corrective action before harm proliferates. Frameworks for auditing algorithmic risk, such as those articulated in Cathy O’Neil’s work on auditing AI systems, provide practical tools and metrics to operationalize this approach and strengthen accountability.2 In this way, auditing functions as both a diagnostic mechanism and a foundation for continuous improvement.

A key part of ex post alignment is ensuring that people do not treat AI outputs as unquestionable truths. Because many AI recommendations are inherently probabilistic, organizations need to train users to interpret them as informed signals rather than final decisions. Helping managers understand when to rely on the system, when to challenge it, and how to spot unexpected or biased outputs is essential for keeping AI use legitimate, accountable, and aligned with organizational values over time.

Managing misalignment at scale can be a particular challenge, especially for systems that are designed to filter, prioritize, and escalate alerts in real time. Nasdaq’s AI-driven market surveillance, for example, monitors trading activity for irregularities — such as unusual volumes, price anomalies, or potential manipulation — and can generate hundreds of high-risk alerts per second. Cross-functional teams of compliance officers, data scientists, and domain experts review flagged activity through structured case workflows. Each alert is assessed to determine whether it reflects genuine market manipulation or a false positive triggered by unusual but legitimate trading behavior. Investigators document the rationale for their conclusions, and these outcomes are fed back to model developers to recalibrate thresholds, refine detection features, and reduce recurring noise in future alerts.

Escalation committees intervene when investigations suggest the involvement of coordinated bad actors or when anomalies indicate broader systemic risk. Audit trails capture key elements of this process, including the original alert, supporting data signals, the human decision taken, and any subsequent model adjustments made. Periodic governance reviews are conducted to evaluate patterns of false positives and missed detections to ensure accountability, regulatory compliance, and continuous improvement of surveillance rules. Even so, surges in alert volumes can place severe strain on teams, overwhelming response capacity and increasing the risk of error.

One effective approach to managing the impact of high volumes of alerts is to redesign workflows around AI outputs. This approach is well illustrated by a global bank’s experience with AI-driven fraud detection. Executives found that the main challenges did not stem from errors in the model predictions but from breakdowns in how fraud alerts were interpreted, routed, and acted upon across teams. Inconsistent handoffs between compliance, risk, and front-line staff members often led to delayed responses, duplicated effort, or missed follow-up, undermining the system’s effectiveness despite technically sound outputs. For example, alerts were sometimes routed to the wrong team, duplicated across units, or left unresolved because no group clearly owned the next step. Customer service employees occasionally contacted clients based on alerts that fraud teams had not yet validated, while high-risk cases were delayed because escalation criteria were unclear.

To address those problems, the bank mapped the alert workflow step-by-step and reassigned responsibilities at each decision point. Fraud analysts were given clearer authority to close low-confidence alerts, fraud operations focused on rapid escalation of confirmed cases, and customer service teams were engaged after a fraud review determined that outreach was necessary. Decision rules were standardized — for instance, when an alert should be suppressed, investigated further, or escalated — reducing delays, unnecessary escalations, and alert overload.

Ex post alignment focuses on evaluating AI decisions after they have been made, by testing outcomes against ethical, regulatory, and domain-specific expectations rather than reconstructing internal decision logic. Ultimately, successful ex post alignment does not eliminate risk; it sustains legitimacy by ensuring that high-agency AI outcomes remain contestable, correctable, and aligned with the standards that matter over time. Unlike traditional risk management, ex post alignment accepts that some misalignment is inevitable — and focuses governance on detection, contestability, and correction rather than prevention alone.

Propagation-Risk Controls

Rules-based controls and ex post alignment mechanisms share an important limitation: They tend to treat risk as largely confined, focusing on discrete errors or individual outputs. This approach can be effective when AI systems operate in relative isolation, but it produces incomplete outcomes when systems are interconnected through real-time data flows, APIs, and automated decision-making. The rise of agentic AI is a case in point. As AI systems increasingly initiate actions autonomously, coordinate with other systems, and pursue objectives across multiple domains, errors or misalignments originating in one system can propagate across others. The relevant concern, therefore, is interdependence and propagation risks that can have downstream effects that traditional, output-focused controls may overlook.

Regulators are increasingly recognizing the importance of propagation risks and the need for robust testing and oversight. The Bank of England, for example, has highlighted the risks posed by “deep trading agents” — AI-driven strategies that could amplify external shocks or coordinate in ways that evade human detection. In health care, biased diagnostic models can spread flawed heuristics across hospitals and insurers. In supply chains, algorithmic procurement platforms can amplify pricing errors across entire supplier networks. Similar dynamics can arise in any digitally interconnected system.

Propagation-risk controls represent a third layer of governance and are designed to surface second- and higher-order effects before they overwhelm downstream functions. In our framework, rules-based controls safeguard narrow and relatively static processes, alignment mechanisms address complex systems whose decisions are opaque, and propagation controls focus on interconnected systems. These controls are concerned with not only what happens within a system but what occurs when systems interact. Their central challenge is invisibility: Failures travel laterally, exploiting hidden interdependencies that often become apparent only when a disruption occurs. A minor logistics API error, for example, may be harmless in isolation, but when it is combined with a cyber incident affecting a payment gateway, it can contribute to systemic breakdown.

A governance framework built around a company-​centric view of risk is poorly suited to track such cross-boundary dynamics. Because propagation risks unfold across interconnected systems, often beyond the visibility or control of any single organization, managing them requires a shift from a company-centric perspective to an ecosystem-aware perspective.

This shift involves three complementary activities: mapping interdependencies, monitoring shared infrastructures, and institutionalizing anticipatory oversight. Together, these practices help surface risks that remain invisible when controls focus only on isolated systems or individual outputs. The European Central Bank’s sectorwide cyber-resilience stress tests show how ecosystem-level propagation-risk controls can be enacted. These exercises map interdependencies across clearinghouses, payment systems, and financial institutions; monitor shared infrastructures for cross-organization vulnerabilities; and simulate how localized disruptions could cascade through the financial system. These practices generalize beyond regulation to any highly interconnected environment.

Organizations can enact propagation-risk controls by redistributing visibility, accountability, and decision rights across ecosystems rather than relying solely on organization-level rules or ex post interventions. Because propagation risks are inherently cross-boundary, effective governance depends as much on coordination across organizations as it does on internal control. Some organizations must shift their cultural norms to encourage data sharing, coordination on standards, and co-investment in oversight infrastructures with partners, competitors, regulators, and, in some cases, open-source communities.3 Reducing propagation risks requires the understanding that resilience is no longer something a company can achieve on its own but instead is a property of the broader system it depends on.

As ecosystems become more densely interconnected, these risks are likely to intensify. The rise of agentic AI — capable of autonomously initiating transactions, negotiating contracts, or reallocating resources across networks — extends this logic, increasing both the speed and reach of failure propagation. In finance, logistics, and health care alike, errors may not simply spread; they may increasingly do so with limited human oversight.

Implementing Adaptive AI Governance

Once leaders have identified the AI risks that are salient to their organizations, and the corresponding controls that they need to have in place, the challenge is to integrate those controls into processes and systems, working within them and continuously adapting them. Doing so involves three key practices: embedding controls into workflows and incentives, building cross-domain fluency, and institutionalizing governance as a living learning system. Here is how to do that.

1. Embed risk-control protocols into operations. Risk protocols must be designed and hardwired into workflows, accountability structures, and incentives. Oversight should flow directly into planning, audits, and leadership reviews rather than sitting on a separate compliance layer. Only when governance becomes part of the operating fabric can AI be scaled with confidence. This is a necessary condition.

A global bank whose leaders we interviewed embedded AI controls into its standard lending workflow rather than treating them as a separate compliance step. For each approved AI use case, the bank’s AI use‑case committee documented (1) the risk tier (high, medium, or low) based on customer impact, regulatory impact, data sensitivity, and model type; (2) the mandatory controls associated with that tier (such as independent model validation, sample testing by relationship managers, or frequency of post‑deployment reviews); and (3) the decision rights (who could approve model changes and under what conditions). These requirements were then encoded directly into the credit‑approval process and systems. Relationship managers could not bypass model‑validation steps or deployment reviews; exceptions required explicit sign‑off from both the business and risk management teams. Oversight surfaced in regular decision-making cycles, not through ad hoc committees or audits.

2. Enable conclusive judgment across heterogeneous expertise and risk profiles. Adaptive AI governance does not require consensus or shared judgment. Quite the opposite: It requires mechanisms that enable conclusive judgment across heterogeneous expertise, methods, and risk profiles. This is often the hardest — and most decisive — task to accomplish. As AI risks shift across categories and cut through organizational silos, accountability cannot reside within any single function. Differences across domains are not a flaw but a feature: They reflect distinct expertise, evaluative methods, and risk tolerances. The governance challenge is therefore not to homogenize these perspectives but to create the conditions under which organizations can translate them into conclusive decisions at scale — while avoiding both judgment homogenization and uncritical rubber-stamping of AI outputs.

Among the central challenges to institutionalizing a durable capacity for conclusive judgment are that rules-based controls are often undermined by siloed knowledge when various domain experts do not share a common frame. To overcome those barriers, share knowledge across domains via joint model reviews and documentation (such as the model cards described earlier), and hold routine cross-functional validation sessions that make decision logic, assumptions, and thresholds explicit and contestable. In ex post alignment controls, the challenge involves not only knowledge silos but also misaligned risk tolerance and methodological approaches. Alignment can break down when different teams operate with different implicit risk thresholds — stopping judgment too early on the one hand or falling into analysis paralysis on the other. Relying on divergent methods to reconcile expected outcomes with observed results (such as analytical validation, controlled experiments, or case-based judgment) can also cause misalignment. In such scenarios, disagreement is not simply about what the model recommends but about how much risk is acceptable and what constitutes sufficient evidence that the model is performing as intended.

A critical response, therefore, is not merely to “build trust” in AI recommendations but to establish shared evaluative routines that surface and reconcile differences in both risk tolerances and methodological approaches. Systematic post-deployment evaluations anchor discussions in observed system behavior rather than abstract beliefs about model quality.

Organizations can do this through structured review routines that combine incident and near-miss analysis, performance-drift monitoring, and explicit comparisons between intended use cases and actual decision outcomes. Crucially, these routines create common reference points — agreed-upon risk thresholds, shared evidentiary standards, and comparable metrics — through which analytically driven teams, experimentation-oriented groups, and use-case owners can jointly assess whether the model is functioning as intended. Over time, this enables assumptions, thresholds, and controls to be recalibrated, reducing both premature shutdowns driven by excessive caution and analysis paralysis driven by methodological disagreement.

Propagation-risk control depends on a fundamental shift in mindset: from treating risk as a company-centric problem to governing it as an ecosystem-level phenomenon. As with digital business ecosystems, risks in AI systems propagate unevenly across actors that have different roles, incentives, and degrees of interdependence.4 Mapping these interdependencies beyond company boundaries is a necessary first step — and often a wake-up call — but it is insufficient on its own.

As research on ecosystem strategy has shown, coordination breaks down when accountability is diffuse, incentives remain locally optimized, and no actor is explicitly responsible for orchestrating cross-boundary trade-offs.5 Similar dynamics undermine AI propagation-risk controls. Teams remain incentivized to focus narrowly on their own systems; risk ownership is fragmented across organizational units and external partners; and downstream or reputational risks are treated as someone else’s responsibility.

Without leadership support for ecosystem-level accountability — and governance mechanisms that differentiate risk ownership by type of interdependence — interdependency mapping risks becoming a one-off analytical exercise rather than a sustained governance capability. An ecosystem mindset requires not only visibility into connections but also shared rules of engagement, escalation rights, and decision authority to manage how risks propagate across organizational and technological boundaries over time.

Overcoming these barriers is essential to creating the conditions for conclusive judgment that respects differences in expertise, methods, and risk tolerance rather than collapsing them into a single acritical evaluative frame.

3. Institutionalize governance as a learning system. AI governance cannot be static: Risks mutate, so controls must evolve.6 Effective governance therefore requires organizations to establish learning loops with clear roles, for capturing lessons from incidents and near-misses and for translating those lessons into updated standards, thresholds, and controls.

Rather than relying solely on controls, systems, or large-scale governance platforms, effective adaptive AI governance depends on building the right mindset and embedding practical learning loops into everyday oversight. This involves assigning explicit responsibility for reviewing incidents and near misses; systematically documenting what went wrong; and ensuring that insights are translated into revised policies, recalibrated thresholds, or strengthened controls. Over time, governance shifts from protocols and systems toward institutionalized continuous improvement, ensuring that AI systems remain aligned with organizational intent as models evolve, contexts shift, and new risks emerge.

Taken together, these steps mark a fundamental shift in governance of AI. Adaptive AI governance is not about multiplying controls, committees, or checklists. It is about identifying fit-for-purpose controls and hardwiring them into how the organization works, decides, and learns — into workflows and incentives, shared frames of judgment, and living systems that continuously absorb and act on experience. Organizations that treat governance as static will inevitably fall behind systems that learn, adapt, and propagate risk in real time. In contrast, organizations that institutionalize governance as a learning capability — one that connects strategy, execution, and oversight — can turn AI governance from a constraint into an enabler of scale. In the age of intelligent systems, advantage will come not from adopting AI faster but from governing it better — by embedding oversight where decisions are made, risks propagate, and value is created.

Generative AI presents an organizational puzzle. Businesses have collectively invested billions of dollars to give employees access to general-purpose large language models (LLMs) to enhance personal productivity while in most cases struggling to develop and adopt more strategic applications of the technology. Meaningful return on investment, much less competitive advantage, is likely to remain elusive unless companies can use GenAI to make innovative process improvements that scale across functions and business units.1

Our interviews with 87 practitioners in 23 large organizations revealed that leaders who scale value creation with generative AI cultivate three key practices. First, they expand the scope of use cases across processes rather than remaining focused on a specific task. Second, they treat each use case as a work in progress to be continually improved. And third, they quickly identify and abandon use cases that fail to bring measurable value to the organization.

However, most traditional companies are not structured to institutionalize these three practices. Many operate as multidivisional organizations characterized by multiple profit and loss units with duplicated functions, limited cross-functional information flow, and internal competition for resources.2 This setup makes it difficult to scale generative AI use cases across processes and units.

In our research, we found that the few leaders who are overcoming these challenges are moving beyond the classical hub-and-spoke models that many organizations have used to connect centralized AI technical expertise to each unit. They are developing a new kind of internal resource that we call the AI spine. It provides a flexible core structure for implementing, evolving, and abandoning LLM use cases at scale, keeping the generative AI portfolio both focused and current. Notably, rather than deploying technologists out into business units, as is commonly done, this structure pulls individuals with domain knowledge of business processes into the core and makes them part of the team.

A retail bank that we studied demonstrates the kind of scaling and value creation that an AI spine supports. Initially, the bank’s AI spine spearheaded the implementation of an email assistant for customer service employees. In an early, limited rollout, those using the assistant collectively saved about 700 hours. Once the tool was put into wider use, it reduced email handling time by 15%, allowing employees to dedicate more time to managing complex cases. Encouraged by that success, the spine oversaw the implementation of LLM-powered email thread summaries, call transcriptions, and analyses. That yielded data that provided new insights for customer service employees, leading the bank to start reengineering its approach to customer relationship management. The data was also used to develop the next iterations of the email assistant.

We observed another example at a medical coding company that applies standardized alphanumeric codes to documentation related to diagnoses, treatments, and procedures for the purposes of insurance and health care management. There, the AI spine provided a structure that allowed it to turn its first LLM application for automated coding into a new line of business. An internal LLM application had reduced coding time from 25 minutes per case to 2 seconds, cutting the cost of coding by 60% compared with having humans doing the work. (Affected staff members were able to take on other responsibilities.) The AI spine was able to build out the application into a product for insurance companies that need to verify whether bills have been correctly coded, increasing the company’s reach into the medical insurance market and creating a new revenue stream.

Connecting AI Efforts Across the Enterprise

The AI spine is a cross-functional backbone that is dedicated to diffusing and scaling LLM use cases across the organization, focusing on reducing duplicative efforts and achieving economies of scale as solutions expand across business units and processes. Because it is a central point for collaboration between technologists and those holding business domain knowledge, the spine holds the expertise required for rewiring and continually improving processes end to end and across functions, as in the bank example above. As we found at the medical coding company, this structure can be implemented not only in large organizations but also in small and medium-sized enterprises.

In the cases we observed, funding for the AI spine was allocated by top management, and the spine also got a cut of increased revenue or costs savings resulting from applications deployed. That mechanism creates the right incentives: It forces the organization to measure ROI, stay focused, and avoid disproportionate spending on “convenience” use cases that seem useful but don’t materially affect costs or revenue. By being independently funded, the spine maintains decision-making autonomy vis-à-vis other divisions so it can identify and encourage use cases that have the potential to improve processes cutting across divisions. A more traditional AI center of excellence with a hub-and-spoke structure is more likely to focus on cases within individual business units rather than across them.

The spine is overseen by a C-suite leader, who keeps its efforts aligned with overall strategic objectives; this may be a chief technology officer or chief digital officer, or one of their direct subordinates. Sitting within the structure are AI developers, risk and compliance personnel, and a technology owner. (See “The AI Spine.”)

The technology owner is responsible for preventing the fragmentation of data flows and tools. They typically oversee the creation of centralized data platforms, prompt libraries, models, and evaluation and technical performance metrics (including token consumption and financial costs). This oversight reduces rework and lowers marginal costs as applications are diffused across the organization.

To ensure that risk and compliance are managed throughout each iteration of a use case, the person managing those issues is a permanent fixture in the spine. Making that a permanent role expedites compliance issues and centralizes organizational learning and memory so they can be rapidly applied to other use cases. This means that governance is less ad hoc than it is within other structures for organizing AI work, where a general risk and compliance group sits outside and is often overwhelmed by issues specific to AI.

Sitting within the business units but working closely with the AI spine are business owners, knowledge owners, and designated end users. Business owners — that is, the heads of the business units working with the spine on use cases — are the primary bridge between their unit and the spine. They are responsible for identifying processes where they can make a good use case for GenAI and determining nontechnical baselines and targets (for example, star ratings assigned by end users, where a minimum 3 out of 5 stars would be the goal). They are also accountable for implementing proven use cases for the identified processes and making sure that use cases that underdeliver are dropped. This ensures that proofs of concept don’t stall out or remain confined to a specific business unit. The business owner also enlists representatives from each unit to serve as knowledge owners, and end users who can deliver feedback, to ensure that new applications create real value for the business.

Knowledge owners work with the technology owner to ensure that both explicit and tacit knowledge critical to the use case are understood by technologists and captured by the application. They curate ground truth and provide important feedback on the generative AI components that will become de facto repositories for organizational knowledge. Prompt libraries, for example, need to be current with company policies and safety guardrails to generate appropriate responses. Knowledge owners are able to quickly identify issues within their domain, reducing rework and costly escalations as GenAI use cases are implemented and evolve. They also help to rein in GenAI work when a tool’s performance is strong enough from a business perspective, regardless of technical performance. In other words, a solution may not be technically perfect but still good enough that employees find that it makes their work easier.

Select end users of generative AI solutions work with the spine to validate that a use case will be helpful to its intended users. This is important because underperforming use cases lead to low adoption rates, workarounds, and, in some cases, shadow AI. End users also provide critical feedback on post-implementation performance, such as identifying edge cases. Their input drives the next iteration of application improvements.

The AI spine serves as a central point for short standup meetings that are held regularly, perhaps every other week. At the bank, spine members use the meeting to share lessons learned, discuss ongoing and future priorities, and communicate changes that have been made to knowledge repositories, such as prompt libraries. They hold a summit event every month and invite end users to keep them updated with progress, show them demos, and gather their feedback. These events capture needed adjustments during the development process so that applications don’t have to be reworked after they go live. They also help manage users’ expectations and apprehensions.

Pulling the five roles above and the AI developers into a self-contained, highly connected structure helps to effectively align stakeholder interests, ensure that salient knowledge in the organization is contributed where it will have an impact, facilitate collaboration across functions, and more effectively diffuse knowledge and value across business units. Those activities support the key GenAI scaling practices we introduced at the beginning of this article: selecting use cases for implementation; gradually expanding their scope across processes; continuously improving each use case; and taking rapid action on underperforming use cases. The examples below show how the AI spine works.

Respecting Domain Experience and Expertise to Tune Performance

When the bank initially built its email assistant, the effort focused on technical issues, such as integrating information relevant to customer service from the company website and internal documents. While the assistant functioned as designed from a technical perspective, customer service agents were underwhelmed. They reported that responses to customer queries that the application generated were plausible but often incomplete or subtly misaligned with the bank’s standards, and that the tone of the messages was inconsistent. Agents frequently had to modify messages before they were fit to be sent back to customers. Those results underscored the importance of making better use of the tacit knowledge the agents possessed, which wasn’t captured in available data sets. The customer service agents’ feedback gave developers in the spine a foundation for subsequent iterations. Because those end users were represented at regular project review meetings convened at the spine level, and the spine had the authority to veto underperforming releases (while pushing forward when all criteria were met), the company had the opportunity to get the application right.

The AI spine also used mechanisms to surface more tacit knowledge. Customer service employees were compensated for rating each generated response on a scale of 1-5, and their manual edits to those responses were logged for review. This provided critical data to evaluate the performance of the email assistant, creating a feedback loop that surfaced the organization’s unwritten norms regarding tone, intent, escalation thresholds, and edge cases, which can take a significant amount of time to answer. Since then, the bank’s AI spine has routinized the use of ratings and the logging of edits to AI-generated contents as key metrics for other use cases, complementing technical metrics. That practice allows them to identify cases where efforts dedicated to addressing model or prompt shortcomings are likely to generate value, thus focusing human effort where it will be most valuable. For example, the email assistant’s performance was significantly improved when customer service employees shared their knowledge of how customers typically phrase requests.

Compensating employees for their efforts and directing them to higher-value tasks reduced attrition and alleviated their concerns about job security. Retaining employees with deep domain knowledge is critical for generative AI because they help curate data, refine and update prompts, and provide feedback on outputs. When GenAI is deployed in a business unit with high employee churn, over time there are fewer people who can confidently validate outputs without having to refer to documentation — a slow process that undermines productivity gains.

The spine structure brings discussion of both technical issues and business issues to a single forum and fosters a common language so that everyone can contribute to a multifaceted discussion of application performance. This played out at the medical coding company, where the coding application initially performed poorly and AI developers saw the problem as a technical issue that could potentially be resolved through additional data collection. However, nontechnical participants in the AI spine reviewed the results and challenged the engineers. On closer inspection, they found that ground-truth labels used to train the model were inconsistent and that the system was, in fact, outperforming human coders. The labeling inconsistencies arose because different groups of coders had differing levels of medical education. Discovering those inconsistencies allowed developers to implement a timely fix in the next iteration of the tool and avoid wasting time and resources training the model on an improperly curated data set.

Accelerating AI Adoption and Aligning Stakeholders Across Processes

Both the bank and the medical coding company set strategic goals for generative AI adoption, but business units were reluctant to directly fund GenAI use cases because of the high degree of uncertainty associated with such initiatives, given the unpredictability and unreliability that foundation LLMs are known for. Ideas would seem promising and sometimes work well on a small scale or as proofs of concept but fail to move further. Establishing the AI spine as a separate entity with a clear connection to executives helped maintain strategic alignment.

At the bank, the AI spine funds use cases across multiple phases through microgrants tied to meeting both technical and nontechnical performance criteria; a similar pattern was observed in the medical coding company. This approach helps build solutions and manage risk incrementally for long-term initiatives. In both companies, the spine acts as a catalyst for achievements and lessons that can benefit all business units. While moving funding decisions for generative AI initiatives away from business units was initially perceived as problematic, over time it resulted in bolder innovation.

Members of the AI spine in each company also realized that process knowledge was highly fragmented. Each business unit had a fairly clear idea as to how its part of a given process worked, but a clear vision of the entire process from end to end across units was missing. The spine championed the mapping of key processes where GenAI use cases had been proposed, involving stakeholders from each business unit until these processes could be drawn accurately. With these representations, they could clearly communicate the scope of use cases and their potential expansion, as well as see GenAI’s applicability across multiple use cases, as illustrated by the creation of a new revenue stream for the medical coding company. Unlike AI centers of excellence, which are primarily geared toward the centralization of technological know-how, the AI spine centralizes business process knowledge to fuel innovation.

Maintain Momentum for Continuous Improvement and Diffusion

The AI spine is set up not to deliver technology solutions to internal customers as in the typical hub-and-spoke arrangement but to codevelop them with business unit representatives and continually refine them based on user feedback and other performance metrics. That means business units are less likely to wind up shelving underperforming use cases that have been delivered by a team that’s moved on to something else.

The bank capitalized on its AI spine’s facility for iterative experimentation and treated generative AI as a constant work in progress. While the email assistant eventually led to significant time savings for customer service agents, getting there took multiple incremental rollouts that continually raised the bar. Clear technical and business performance targets defined an apparent goal at each iteration that determined whether the solution would be rolled out to end users. It also gave teams time to learn progressively and to assimilate new technological developments in a field that evolves quickly. Embedding varying degrees of work automation led to a more granular view of performance. One version of the email assistant was good at extracting information from customer messages, but the voice, tone, and style of its written responses were off. Messages that included a lot of bullet points and overly familiar phrasing felt more culturally attuned to the U.S. market than to the more reserved Swiss culture.

Keeping the momentum to continue innovating with GenAI requires a careful balance between the exploitation of existing use cases and the exploration of new ones. The AI spine’s orientation toward users, and the priority that its structure places on better understanding how employees interact with GenAI across processes, has helped the bank to develop new use cases. (See “Scaling GenAI at a Swiss Bank.”) By studying those user interactions through employee surveys and shadowing sessions, the bank was able to identify eight new potential use cases, two of which have since been deployed alongside the email assistant: the AskHR chatbot, which answers HR-related questions on topics such as employee benefits; and an employee handbook chatbot that responds to employee questions about what is permitted and forbidden in the execution of their jobs.

The Drawbacks of Simpler GenAI Structures

Among the 23 organizations that participated in our research, only two were able to meaningfully scale generative AI to a strategic capability, and both of them had created structures recognizable as an AI spine. While that is an admittedly small evidence base, companies achieving such results are currently in the minority, and we believe that their practices are worthy of close attention.

Building an AI spine requires significant top-level commitment, time, effort, and funding. The other organizations that were part of our research adopted one of two alternative structures — what we’ve termed GenAI units and GenAI squads — that we see as potential building blocks to a more robust capability. They were still able to exploit generative AI to achieve some economies of scale, but the scope of their efforts was narrower, and they faced challenges expanding their use of GenAI.

Thirteen of the organizations we studied set up a standalone, central GenAI unit. These are typically technology-focused and charged with providing solutions to the business units. While often the most feasible approach when resources are constrained, the GenAI unit can quickly reveal its limits regarding business (rather than technical) performance and user adoption. A Swiss health insurer illustrates this limitation. Its generative AI unit tried to roll out an email assistant for its customer service center agents. The model could produce fluent replies, but compliance specialists, consulted only after the pilot, pointed out that every customer message had to follow tightly regulated text templates. Fearing noncompliance, customer service center agents reverted back to approved texts, and so adoption plateaued and the project stalled. Without a mechanism to coordinate work across multiple functions (in this case, risk, legal, and customer service), the bank was left with a zombie GenAI use case with no clear path forward. This early failure increased organizational skepticism toward generative AI, making it increasingly difficult to garner support for future use cases.

A stand-alone GenAI unit can be useful for the early, exploratory stages of the technology: It concentrates talent and lets the organization explore GenAI’s potential before committing major resources. However, it cannot properly assess the specific needs of each unit or coordinate work across multiple use cases, and it lacks an end-to-end view of processes that could be improved with GenAI.

Another approach, taken by eight organizations in our study, is to embed small, cross-functional GenAI squads within each business unit. The companies using GenAI squads were able to take ideas to working pilots much more quickly than those using GenAI units, because they were better able to harness relevant business know-how and faced fewer coordination challenges. However, the squads struggled to manage use cases at various levels of maturity. A Swiss insurer’s GenAI squad launched a customer-facing chatbot for product information in record time. Just a few weeks later, curious users prompted the chatbot to recommend pizza recipes. That incident led to multiple iterations aimed at hardening the system — and revealed how rolling out the initial implementation of a generative AI solution is often easier than maintaining and improving it. As the portfolio of use cases expanded, the mounting maintenance burden of updating data sources and prompts stretched the GenAI squad to its limits, leaving little capacity for pursuing new use cases.

Because GenAI squads are funded and staffed by the business units, they can lead to inequities and inconsistent technology adoption at the enterprise level. Some units have the means to fund multiple use cases and hire their own staff. Others must lower their expectations, regardless of the potential value of a use case. This structure perpetuates silos and duplicative efforts; the lack of an end-to-end view of business processes limits value creation at the organizational level. GenAI squads can help to quickly spread generative AI adoption within the organization, but leaders need a different approach if their goal is to coordinate GenAI use cases across the organization.

Creating value at scale with generative AI does not come from giving everyone access to an LLM and hoping that something magical will happen.

Instead, we advise leaders to begin by creating and visibly supporting a small, cross-functional AI spine that can coordinate across business units. The spine should be charged with standardizing and centralizing core building blocks (processes, data, evaluations, prompts, and models).

That mandate must be matched by financial resources. Central funding is essential because it enables the spine to pursue cross-business-unit opportunities that no single business unit alone would sponsor. In addition, executives must define early on what “value” means, to help identify underperforming use cases and normalize dropping them.

Finally, while creating value at scale with GenAI initially depends on a top-down decision by business leaders, sustaining and expanding that value over time depends on continued contributions from business units. Executives should empower and expect the AI spine to convene the technology owner, risk and compliance, business owners, knowledge owners, and end users in continuous collaboration so that their respective expertise can be combined to surface and capture tacit knowledge, align performance with real operating standards, and ensure that improvements compound rather than stall after the pilot phase.

How should companies effectively use or respond to an unwieldy array of customer opinions? While consumer feedback can be invaluable, three recent research articles suggest that it may also be influenced by gender, niche preferences, or sky-high expectations, complicating whether and how companies should respond.

1. Not all users post critical reviews. A study of 1.2 billion online reviews across five major platforms identified a consistent and meaningful gender gap. Women’s ratings are, on average, more favorable than men’s, despite little difference in both groups’ “real” attitudes. The researchers found that women are less likely to share negative reviews when dissatisfied — likely due to societal gender expectations. Women are concerned about possible backlash: being negatively evaluated themselves after posting an unfavorable review.

Notably, however, when users were first prompted to report their opinions anonymously and only then asked to submit an online review, the gender rating gap disappeared. The researchers suggest that businesses could encourage women to share their opinions more openly by introducing a similar process.

2. Adopting user community feedback can backfire with mainstream users. Product developers can gain valuable insight by consulting user communities, but recent research finds that acting on such community feedback can sometimes undermine commercial success. An analysis of video games whose development was influenced by feedback from users who received early access found that the preferences voluntarily shared by self-selected community members can differ greatly from those of more mainstream consumers.

These unrepresentative preferences are particularly salient in lower-priced games or those with niche appeal, suggesting that companies should ignore community feedback for such products until and unless they attract more broadly representative users.

3. Expert ratings can inflate consumer expectations — and deflate reviews. In the prestigious Michelin Guide, experts award star ratings to denote restaurant quality. Favorable expert opinions — more stars — do benefit sales, but a recent study suggests a mixed effect on diner perceptions. Researchers compared reviews by diners of Michelin-starred eateries versus a control group of fine-dining restaurants on the TripAdvisor platform and found a marked “expectation effect.”

Restaurants with multiple stars can find it harder to meet the inflated expectations of customers, who in turn lower their ratings when those expectations aren’t met. While an increase in stars showed no impact on consumer ratings, a decrease in stars improved ratings as consumers became less demanding.

Together, these research findings demonstrate that interpreting and acting on customer feedback can be a tricky and uneven endeavor. The customer, it seems, is not always right.

Of the potential weaknesses of any security system, the human layer has always posed a key risk. The arrival of AI tools has made human cognition even more of a vulnerability.

Companies face three overlapping security threats from AI’s effects on human cognition. First, weaponized persuasion lets attackers manipulate employees’ judgment through personalized, adaptive deception. Second, plausible hallucinations deliver confidently false information that survives casual review. And third, as employees offload cognitive work to AI systems, they engage in less independent reasoning. Together, these dynamics make humans both the primary vulnerability and the key defense.

A simple new protocol dubbed “Think First, Verify Always” (TFVA) addresses these threats by urging employees to take two steps. “Think First” requires employees to form their own judgment before consulting AI. “Verify Always” requires that they cross-check critical AI-generated information against independent sources before acting. The protocol aims to bolster independent judgment and verification and reduce risk, even when manipulation goes unrecognized.

This simple, structured critical-thinking habit can effectively reduce AI risk. In a randomized controlled trial with 151 participants, a three-minute micro-lesson on TFVA improved decision quality by 7.87 percentage points, with a 44% relative improvement in ethical judgment and 25% improvement in information verification. After the micro-lesson, participants were tested on 18 scenario-based tasks (like spotting AI-generated phishing and suspicious executive requests) and scored 65.3%, compared with 57.4% for a control group.

At RSM France, the audit, accounting, and consulting firm where I work, we deployed the protocol in onboarding and training programs for our 1,600 employees. Early feedback suggests that it reduces risk and builds organizational trust.

Managers can embed TFVA in onboarding, security awareness training, and generative AI access policies and reinforce the habit in each instance with a simple three-minute training session. The return is a workforce that treats AI as a powerful tool requiring judgment, not a trusted authority requiring obedience.

Every year, multinational corporations invest billions in global mobility programs. The standard playbook includes training in the customs, values, and communication styles of the host country. However, our meta-analysis of research on migrants, including relocated workers, suggests that cultural knowledge plays a minimal role in expats’ successful adjustment.

In a study recently published in Nature Communications, we and our colleagues conducted the largest-ever meta-analysis of migrant adaptation. We synthesized data from 1,114 primary studies covering 571,260 people, including business expatriates, international students, and economic migrants, to identify the factors that actually correlate with a successful move.

We found that cultural differences are not the primary driver of migrants’ difficulties; stressors like discrimination and navigating new systems have far bigger negative impacts. Social resources like connection and support contribute most to fitting in, navigating daily life, and functioning effectively in the new environment.

The most striking finding for business leaders emerged when we looked at who provides that support: A supervisor’s support was one of the strongest and most consistent predictors of success. It was substantially more pivotal than support from the employee’s own community or compatriots — or even their spouse or family, in many cases.

Why does the local boss play such an important role? First, because feeling connected to others and not lonely are the absolute strongest predictors of adaptation; supervisor support signals legitimacy to the rest of the team and confers belonging. Second, supervisors can clarify role ambiguity (a major stressor for expats), help an employee navigate bureaucratic hurdles, and explain the unwritten rules of the workplace. Third, a supportive supervisor fosters inclusion, mitigating perceived discrimination — the factor most likely to hurt adaptation.

The bottom line: The success of your global talent strategy doesn’t depend on how well expat employees learn the culture. It depends on how well your managers support them.

In this episode of the Me, Myself, and AI podcast, Philips’s chief medical officer Carla Goulart Peron shares how artificial intelligence is reshaping health care — not by replacing clinicians but by expanding access, improving diagnostics, and freeing doctors to focus more time on patients. Drawing on her experience practicing medicine in Brazil’s strained public health system, she explains how technologies like AI-assisted imaging and remote collaboration can bridge critical gaps in care. Carla also explores the challenges of trust, bias, interoperability, and women’s health data in the next era of AI-enabled medicine. She offers a grounded, global perspective on how technology can make health care more human.

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Transcript

Allison Ryder: How is one clinician thinking about applying AI to health care in additive ways that improve access to care, clinician confidence, and patient experience? Find out on today’s episode.

Carla Goulart Peron: I’m Dr. Carla Goulart Peron from Philips, and you are listening to Me, Myself, and AI.

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

Our guest today is Dr. Carla Goulart Peron, chief medical officer at Philips. Philips is a health care technology company behind imaging systems, patient monitoring, and a growing suite of AI-based clinical tools. What I find fascinating about Carla’s perspective is that she started her career as a physician in Sao Paulo’s public health system, where demand far outstrips resources. She’s carried that lens into the C-suite. She’s now leading medical strategy for a company that’s betting heavily on AI to close gaps in care. Carla, welcome to the show.

Carla Goulart Peron: Thank you very much, Sam.

Sam Ransbotham: Many listeners might still associate Philips more with consumer electronics, but can you tell us about the company in terms of health care and the kinds of things you’re doing?

Carla Goulart Peron: It’s a company that has [been around for] 130 years and has been in many areas, but the last few decades, Philips shifted into health care fully. We started with imaging — so the diagnostic area, X-ray, CT, MRI, ultrasound — and then we [went] into the interventional therapy, into the cath labs. We also are very much present in the ICU and any area of the hospital where you are monitoring signals coming from those patients. [We’re] also heavily invested in monitoring things outside of the hospital. And then the last piece, but not less important, [is] definitely AI that is supporting all those areas of care and then keeps growing [as] a very hot topic right now.

Sam Ransbotham: As you were listing these technologies, I was thinking, “Those are classic applications where AI has made huge advances with imaging and these sorts of things.” I mentioned earlier, you trained as a physician and you worked in both the public and the private sector, sometimes in the same day, I think, switching back and forth. What did that experience teach you about where health care breaks down in a way that maybe technology can help?

Carla Goulart Peron: You are right. In the morning I was working in the public sector in Brazil, where scarcity of health care workers, technology, information sometimes is very much present, so [you] need to work within the best of your capacity to offer care for those patients. But it’s universal health care, which means everybody has access. So there is a benefit there too.

Sometimes in the afternoon or in the night, I was working in the best of the best hospitals in the private sector, with everything available. I think that teaches you a lot of resilience, personally, as an individual, as a physician, but also gives you the chance to try different things and learn from those experiences. But [it] also makes so much clearer to an individual like me how much technology can actually build a bridge and help support more patients [who] are expecting to get access to health care overall … because it’s expediting the way we’re seeing those patients, because it’s connecting the data points of information, or even allowing collaboration across the specialties that may not be present in the public sector.

Sam Ransbotham: Was there a specific moment that you said, “This is something that technology could really help with or could help fix?” Was there anything that made you think technology might be the answer?

Carla Goulart Peron: Many times. I love sharing an example of ultrasounds [from] when I [was] coming out of residency, actually, and starting to see patients on my own. [The] ultrasound is one of the biggest diagnostic tools that we use in the OB-GYN practice. But we [did] not always have access to those machines in the hospital setup. Sometimes we had access to those machines, but we are not qualified to use them.

Technology [is coming] into reality today — I’m very jealous about the people [who] are learning today [in] their own clinical practice — in a way that you have clear collaboration. So you can really open the technology, open the ultrasound machine, get access to an expert [who] can be anywhere [on] the planet, let’s say in the same city, just to make it easier from the clinical practice perspective, to guide you, to see what you are seeing [in] that same imaging, help you to capture the right imaging, and expedite the technology. In some other places, like the ones that were practicing in the public sector, I would need to transfer that patient, sometimes to another facility, which means call an ambulance, be stuck in traffic, just to get the image captured and then [take the] patient back for you to be able to take a final diagnostic and initiate therapy. I mean that was like wine and water, unfortunately, between those two worlds that I was living in.

Sam Ransbotham: You mentioned the traffic. Not too long ago I was in Sao Paulo, and that was a big thing I remember there — just how long it took to get from one place to another.

But actually, ultrasounds [are] expensive machines that can’t move, but then there [are] also the other parts that information can move. I think you were sort of making a distinction between those. You’ve got some aspects of what you’re doing that seem to rely very heavily on sophisticated equipment. On the other hand, you also have information [that] can flow without that bottleneck of Sao Paulo traffic.

Carla Goulart Peron: I mentioned the ultrasound, which is highly dependent on the imaging that you are actually seeing on time, right? So you use that imaging on time to make the diagnostic. But when we think about a CT or MRI, they produce hundreds of images that the radiologist can see from anywhere, and that is also facilitating drastically the way that we are actually reporting imaging. Also, you are highly dependent on the users [who] are actually placing the patient into the machine, making sure that the patient is well positioned, [who] is holding [their] breath if needed, or [who] is kind of moving accordingly. And now with AI coming on board, you also can get that done very quickly, precisely, with not much support or training from the technical perspective. So that has also been a game changer.

I would say the way we are leveraging technology, AI, automation, to position those patients, to make sure that the exam can be as fast as possible, but also how we are processing the imaging that is coming out of those big machines, has been very different.

Sam Ransbotham: I think I was too simple. I was talking about the machine and then the results of the machine, but actually, you’re bringing up an important point, which is there’s also a knowledge transfer and an information transfer about how to get the best image in the first place. I think I glossed over that.

You started [with] bedside [care], but now you’re running medical strategy for Philips. How does that inform what you think about the health care strategy and how that works, your deep background in actually doing medicine?

Carla Goulart Peron: It’s interesting because I think when you go into med school, most of the people [who] decide to take this pathway don’t think about anything differently than just seeing patients on a one-on-one basis. At least when I started med school, that was the reality — I think today is very different.

But in that journey of understanding the health care system, how companies that are developing drugs, medical devices, or other types of equipment work, I [learned] that there is a role to play [on the] industry side, [on] the corporate side that can be as rewarding and as interesting as seeing patients on a one-on-one basis, probably [on] a much bigger scale. So I think I needed to convince myself that by moving from the bedside [to] corporate, I was not changing my background as a physician or I was not leaving my professional [training] behind. I was actually just applying that knowledge in a different way.

I’m fascinated by innovation. I’m fascinated [with] ensuring that whatever innovation we are investing in, in the corporate environment, can actually reach the patients [who] are going to benefit most. And that’s really the biggest part of my job: ensuring that the ideas that our engineers are developing in partnership with hospitals and physicians are going to meet the requirements of the regulation, because we need to prove that it’s safe and it’s effective, but also [ensure] that we will have a good plan in how this technology can be incorporated by the health care system in the way that can actually reach the patients [who] are going to benefit most.

It’s very different, but it’s fascinating, because I keep learning every day because it’s new technologies, new areas of care, new types of health care systems. If you think about a company like Philips that has a global presence, it’s very different to think about commercializing something in Brazil than it is in the U.S. or in Europe or in Africa or in Asia, so you need to have that globalized thought in mind when you are thinking about developing technology.

Sam Ransbotham: Let’s talk about some of these specifics. I think Philips just got [Food and Drug Administration] clearance for SmartHeart, which is an automated cardiac MR [magnetic resonance] planning tool. First, explain to people like me what that actually means, and then how does that actually change a radiologist’s day? What’s different?

Carla Goulart Peron: SmartHeart is a great example. As you think about an MR machine, it is a technology that can capture imaging from your entire body. In order for you to do that, you need to have a technician [who] understands exactly why you are actually being requested as a patient to do that exam.

In this case, [the] physician — a cardiologist most likely — wants to actually see how your heart is functioning. So imagine that a technician needs to know exactly how he or she should be positioning you on the MR table, at which angle, if you are tall or short, if you are someone [who] is big or small, if it’s a kid or if it’s a female or a male — there are so many different data points that a technician needs to understand in order to capture the right level of imaging with the right quality [so] that a radiologist can actually do a diagnostic out of it.

SmartHeart is an AI-driven, one-click automation that plans all setups that drive how the cardiac imaging needs to be captured. This happens in 30 seconds. So that sounds simple, but for the operator [who] is actually doing multiple exams in different parts of the body with different indications, that can be from 15 minutes to 30 seconds. It makes the machine a lot more accessible. It makes the technician capable of doing a lot more exams. It also reduces the dependency of having someone [who’s] highly trained. The burden on the technician is also something that today is a big issue.

Sam Ransbotham: I like the idea that we have a very expensive machine that we can increase the throughput for. You said 15 minutes — that’s four exams an hour, even without any setup and putting on the gown. But you talked about 30 seconds, and suddenly I feel like, “Hey, we have an expensive machine that we can use a lot more.”

Carla Goulart Peron: It is definitely [about the] right speed. There is a burden on health care providers and nurses, physicians overall. But [it] also [involves] “first time right.” So sometimes if you don’t have something like this, you’ll go through the exam, send the images to the radiologist, and they’ll say, “You need to call this patient back because we are missing one or two views.” With something like that, this doesn’t happen.

Sam Ransbotham: Nobody likes that. Nobody likes to go back and forth.

Carla Goulart Peron: Especially on a machine — you don’t want to be there, it’s small, and it may not be that comfortable for the patients overall.

Sam Ransbotham: Radiology is one of the areas that I think a decade ago people were saying, “Oh gosh, we’re never going to have radiologists again. The machines are going to do everything.” You know, that narrative has really not played out at all. But I think it’s a great example of, in general, how artificial intelligence might affect the future of work and what we do. But is there a risk that if this process works too well, hospitals are going to start thinking they need fewer radiologists? How’s that going to play out, do you see?

Carla Goulart Peron: I personally believe that AI is here to add, not to take over. Maybe this conversation will be very different five years from now because I think we’re learning that environment.

Sam Ransbotham: Predicting is so tough in this world.

Carla Goulart Peron: But I think [in] the radiology space for now, radiologists are wasting their time on things that are not valuable at all, reviewing images that were not captured precisely, or doing reports, or reassessing a lot of normal images. Interesting enough, a few weeks ago, I was with one of the radiologists’ medical societies, and they were talking about what if we could have AI defining all normal images, and then radiologists will be looking at only abnormal [images]? What’s fascinating about AI and [its] potential, someone in the audience raised their hand and said, “Well, how are we going to get the radiologists trained in what is abnormal if they are not going to be seeing normal?”

So I think the answer for your question is still TBD [for] what the future will look like, to your point, but I don’t see AI taking over. I think it’s actually helping us to see more patients because there is a big gap out there [and] also to be dedicating our time to things that are unique that we can do as clinicians.

Sam Ransbotham: One way I think about this is we would have a very different story if every possible patient in the world was completely satisfied with a perfect supply of radiology. If everybody that needed this treatment was currently getting it, then I think we maybe could be talking about this replacement type of thing. But you know far better than I do that’s just not the case. There’s a massive undersupply of these types of technologies, and a lot of it is driven by the certain return on investment and the cost structure now, which I believe this can change. How do you see that aspect changing, in terms of serving more?

Carla Goulart Peron: I personally believe that there is such a big gap out there in access to care that as we incorporate technology, we’ll be able to do more with the same, not with less. Because I was born and raised in Brazil and practiced in Brazil, I feel very comfortable in going [along] that pathway [in] low-income countries.

In those areas, the gap is really huge. You can be waiting in line for months in order to have, as an example, an MRI or CT scan done, right? Sometimes too late in the game, that actually can change the patient outcome. Well, we cannot fool ourselves. That reality also exists in the U.S. and in Europe and in Asia. We have deserts, right? We have areas where people don’t have any access to care. So I don’t personally see a timeline where I’ll say, “Well, this will quickly fix that gap in a way that is going to be reduced.”

Sam Ransbotham: I love the idea that what this leads to is better matching of need with supply.

I’ve got a beef to raise with you. You had this Future Health Index that you put together. Normally I like to skim these things before a call, but it was actually quite interesting. So it cost me a lot more time looking at it than I wanted. But I think part of your finding in the Future Health Index was that 79% of the health care professionals are optimistic about AI, but half of the patients are worried that it’s going to reduce their face-to-face time. How are we going to reconcile these two different perspectives from a market [where] both parties are important?

Carla Goulart Peron: I think the perspective of the patients is very important in this one, right? If the patients start to feel too uncomfortable and reject AI, it may become a challenge.

I also think that this is slowly changing. We’re going to be launching a new version of the assessment a little later this year. But for me, the ability to reconcile those two things from the health care professional perspective is ensuring that we are validating, we are getting good access to data that is actually being used to train the machines. So physicians are on board but really a little bit cautious about how much evidential bias do [they] get in the AI. Can I fully trust AI? How much do I need to review what’s happening? I think [from] the physician’s perspective or the health care worker’s perspective, it’s more towards data-driven.

While [from] the patient’s perspective, I think it’s more about the experience. And I think AI became a big buzzword, and so people don’t know exactly what to expect. There is this misperception that physicians are going to be substituted by machines. The reality is, the physician’s time is actually being freed up to be actually dedicated more to the empathy piece, to the touch, to that one-on-one, eye-to-eye, which I think is going to make a big difference.

But to the second point, where I think it’s very interesting, there are some studies already out there that show if you are talking to a real physician or to an AI version of that physician, sometimes the AI can learn how to be more empathetic than the physician. So I think this is going to be a journey of us as individuals actually learning how to incorporate AI into our lives and trusting a little bit that help that I think we’re going to start getting [in the] future.

Sam Ransbotham: In our research, we found that individuals who trust AI are twice as likely to use it regularly. That trust is an important part, but when we were studying it, we were sort of thinking in general [about the] use of AI. It strikes me that’s different about trusting Netflix’s recommendation versus health care, but perhaps there is something transferable between the consumer levels of artificial intelligence and the greater understanding there, and health care. Is there something unique about building trust in this clinical setting that is different than my Netflix example?

Carla Goulart Peron: I think people are more concerned about their health than eventually getting advice of which movie I should be watching or which series. But one of the areas that I think AI is actually already doing for the patients is enabling. I think even the name we gave to that individual [who] is in the center of everything we do as clinicians — patient — it’s almost like, “You stay there, be patient, and wait until somebody tells you what to do.”

While now with, first of all, AI enabling interoperability data points, you are giving more visibility to the overall health. Also, what are the options that those patients may have in front of them? And I think this is, in my view, going to change drastically the way that we, physicians and patients, will be embracing health care for the future, because I think [when] I went to med school many years ago, patients didn’t have a say.

It was really, that’s the protocol, and that’s what we’re going to do, and you just follow it. I think more and more we’re starting to talk about precise medicine, where patients will be able to be offered one, two, three potential treatment pathways with pros and cons and the ability to choose. And I think AI may enable those patients to make more informed decisions at least.

Sam Ransbotham: Actually, I really like the framing of the word patient because I feel like I often am not. We had Josh Weiner from CVS Health on a couple of episodes ago, and one of the things we got into was, “Hey … forget all this AI stuff; I’d just be happy if I didn’t have to put my name in over and over again and wait for a long time.” You can talk about all the cool AI stuff you want to, but let’s get some of those simple things done.

I think you’re very focused on women’s cardiac health. Maybe orient us. What are some of the specific gaps in care for women, and how do you see AI perhaps connecting and either exacerbating or helping with that?

Carla Goulart Peron: I’m very passionate about the topic because I always say we need to remind people that we’re not mini men; we are different as women, and we do have a heart, and it needs to function like any other heart in the man’s body.

There is a big gap. Cardiac cases [have] the highest mortality rate present in females. Despite that, females have a much longer waiting time until [they] get the diagnostic, because we experience symptoms differently sometimes, because most of the protocols that have been designed have been designed based on studies that included only males. So there is a big gap out there that needs to be covered.

What technology definitely can help in covering that gap is ensuring that the nuances and the differences in the physiology in the type of response that females used to present is actually incorporated into the way we are designing the diagnostic tools.

We talked about MR. The position of the heart is slightly different in [a woman’s] chest compared with the men’s chest. It’s a very detailed, minor thing, but it can impact the way you are capturing your imaging. If you are capturing cardiac rhythm, for example, the algorithms need to understand that the female heart has a pattern that’s slightly different from the male heart, and so I think AI will quickly get that information into those algorithms because of the speed, and be able to equalize that.

Sam Ransbotham: That sort of presumes the fact that we’ve got these perhaps underrepresented or undertreated populations within our data sets. We had Ziad Obermeyer of University of California, Berkeley, on the show a couple of seasons ago, and he was talking about how these algorithms can actually build up this equity. But it was depending on having that raw data to start with. What can we do to try to get better sampling on those underrepresented populations?

Carla Goulart Peron: The first thing is what we are doing right now — talk about it, right? So there is an opportunity for us to do better now. You are spot-on. Most of the drugs are developed based on a very limited number of females or even other diverse types of population. You name it.

The same thing for medical devices. And most of the protocols and guidelines that are created are also created based on trials that were developed a long time ago, which didn’t necessarily include the right level of variety. That’s the first point.

The second point is using technology and AI to capture that information pretty quickly and reiterate rather than starting from scratch. I learned a big lesson two years ago when I attended the WEF [World Economic Forum] for the first time, and we were talking about women’s health. I’m an OB-GYN, I have more than 20 years of clinical practice. Someone was telling me the story that when a woman gives birth, there is a standard that defines that 500 milliliters of blood loss is normal. So if you have that, you don’t need to take any actions.

Then I heard a question from the audience during the WEF discussion. “How was that established?” I paused because I had never asked that question. I never had the curiosity. That was in the book. I just kind of assumed that a very good methodology was put in place. And that standard was developed based on nine females in Germany, and exploited and used [on] the entire planet. Can you imagine how that translates to India or China, where there are much smaller bodies? So they quickly were able to iterate that with data points and create the correlation, and established that in India, that number should be 300 milliliters. So that makes a huge difference in how you’re going to be treating your patients.

I think technology, that interconnectivity, that not only the fact that we’re going into automation, but that now AI can analyze such a big data set so quickly, can really improve the way we are practicing medicine.

Sam Ransbotham: Actually, that’s an interesting example because it strikes me as a slightly different approach, which is, in your example, you had to go to the World Economic Forum to have that question get raised. But somehow I can also imagine a very simple job for agents would be, “Hey, go through all of our clinical practices in every area, and find the root study for that, and assess how that plays out.” I would feel like your nine people in Germany sample should rise pretty quickly to the top of that list. That seems pretty exciting.

Carla Goulart Peron: Pretty exciting, exactly. It’s fascinating what can be done.

Sam Ransbotham: Let’s look forward for a minute, though. You started in public health in Brazil. There’s a lot of resource constraints there about technology. If you could pick one AI capability to deploy globally that would make the biggest difference — you get to choose right now, wish list — what would you push out to the world? What do you think [is] the greatest application of the use of artificial intelligence in health care that we could push globally?

Carla Goulart Peron: Interoperability. That is going to change completely the way we practice medicine. Because today we’re very much closed or restricted to the health care system that you are operating. So the ability to see the patients longitudinally without those barriers, I personally believe, is going to change outcomes significantly. If I need to pick one, that will be my choice.

Sam Ransbotham: That’s not at all where I thought you’d go with that. That’s pretty fascinating. It’s so cross-cutting, and it affects everything. All right, so I’ll bite. What’s the biggest barrier to that actually happening?

Carla Goulart Peron: That’s a big question. That’s why it’s a dream. You said you can pick anything, don’t be restricted. I think there are many. The first one is making sure that we have access to good quality data, but also that we start thinking about that from the get-go. If you don’t have some level of standardization, it’s very difficult to think about interoperability. I think that’s the first piece, which is science. It’s how we drive this for [the] future.

The second is how we think about incorporating this new era. How do we think about incorporating reimbursement and access to technology into the discussion about AI? We are still restricted by the reimbursement systems. What kind of code coverage do we have? What is the incentive? We may have something that can actually pretty quickly take the patients out of the hospital, reduce lengths of stay. But if that’s not the incentive from the health care system perspective, this is not going to happen. So for me, that’s another big thing that we need to think about.

The other piece, which I think is how we need to partner, is regulation. Regulation will need to evolve with this new environment that we are getting into. The type of regulation that brought us here is not going to take us to the future because the future is very different than the one we are playing today.

Sam Ransbotham: That seems really hard because, yes, we built this regulatory system and we built these reimbursement processes, and so many processes we built off of the way things used to work.

Thank you for joining us. I think what really comes through so clearly is that this isn’t abstract for you. This is something that you know deeply, and you’ve seen what happens when care isn’t accessible. And I liked some of the ideas that you mentioned about how do we make the use of technology, not just AI, but technology in general, part of the solution and not just sort of a headline. Thanks for joining us.

Carla Goulart Peron: My pleasure, Sam. [I] really appreciate the conversation.

Sam Ransbotham: Thanks for tuning in today. On the last episode of Season 13, I’ll be joined by Bernard Hampton, a corporate learning leader at Bank of America. Speak to you then.

Allison Ryder: Thanks for listening to Me, Myself, and AI. Our show is able to continue, in large part, due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcasts review or a rating on Spotify. And share our show with others you think might find it interesting and helpful.