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When Incentives Go Wrong: The Hidden Costs of Motivating Creative People

In the early 1900s, commercial fishing interests along the New England coast had a problem: the abundant seal population was reducing the fish supply. Convinced that fewer seals would be the solution, Maine and neighboring states began paying bounties for every seal killed. As proof, one simply needed to bring a seal’s nose to a town clerk and collect the reward. 

But reality turned out to be far more intricate than what the policy’s creators envisioned.

For generations, the Passamaquoddy people of eastern Maine had hunted seals for food and clothing. By 1900, logging, fencing, dammed rivers, and mounting regulations had restricted the Passamaquoddy people’s access to their ancestral lands. Ocean hunting remained one of the few avenues left to maintain traditional subsistence practices, providing both food and clothing. As a result, when commercial interests pressured the state to eliminate local seal populations, they directly endangered an essential Indigenous livelihood.

In response, several Passamaquoddy hunters devised a clever workaround. Guided by traditional values that discouraged killing seals en masse, skilled artisans crafted convincing fake noses using small fragments of seal hide. A single pelt could thus produce dozens of bounty payouts. Suspicions rose in January 1904 after two Passamaquoddy men attempted to redeem 86 noses at once in Portland. The sheer scale of the operation was striking: claims spiked from 208 bounties in 1903 to 2,632 in 1904, prompting authorities to quickly end the program.

The scheme was illegal but reducing it to fraud over-simplifies the story. Commercial fisheries wanted to eliminate competition for fish, leading officials to establish a quantifiable metric (seal noses). Faced with a policy that threatened both their environment and their culture, the Passamaquoddy capitalized on the fact that the state was not truly paying for population reduction. It was paying for physical tokens. 

It is a wonderfully absurd example of a serious management problem. Incentives require us to translate what we really want into something observable enough to reward. But the moment we define that, the measure itself begins to compete with the mission.

This problem is particularly acute in creative work. Leaders want innovation, so they reward patents, product launches, ideas submitted, revenue generated, individual performance, or promotion-worthy accomplishments. 

But creativity works differently. So the right question is not, “Do incentives motivate?” because, of course, they do. The more useful question is: “What exactly do they motivate people to do?”

Research suggests that incentives shape creative work through at least three broad mechanisms: the effort type,  the orientation of thought, and the quality of social interaction.

1. Type of Effort 

In the 1990s, Safelite Glass Corporation changed how its windshield installers were paid. Instead of relying primarily on hourly wages, it introduced piece-rate compensation tied to output. Economist Edward Lazear studied what happened next.

Productivity per worker increased by roughly 44%. Part of the effect came from attracting and retaining more productive employees, but existing workers also increased their output. 

This is an important place to begin because incentives clearly work.

But notice the character of the work. Both Safelite and the workers know what a successful windshield installation looks like. You could consider this kind of work a “routine” or a “predictable” task, where both outcome and the process to achieve that outcome are very well defined. Employees, therefore, have considerable control over accomplishing the goal and incentives encourage people to achieve the goal faster.

Creative work is different because the process (and sometimes even the goal) often do not yet exist.

Imagine asking one engineer to install ten more windshields and another to invent a radically better way of replacing automotive glass. Greater intensity is likely to help the first. The second may need to slow down, explore a strange possibility, discard several promising ideas, or spend a week understanding whether the problem has been framed correctly.

A meta-study analyzed 183 studies involving more than 200,000 participants. They found that both intrinsic motivation and extrinsic incentives predicted performance, but their relationships differed according to the type of performance. Intrinsic motivation was particularly important for performance quality, while incentives were comparatively more important for performance quantity. 

Organizations often use the effort in the sense of intensity like working faster or longer hours. But creative work requires a different kind of effort that’s not as easily measured:  searching broadly for alternative solutions, experimenting, tolerating ambiguity, or persisting through roadblocks. 

A researcher under pressure to produce publications can work extremely hard while avoiding risky research questions. A product team can sprint heroically toward a launch date while failing to ask whether customers really need the product. An executive can relentlessly optimize quarterly results while starving experiments whose payoff lies years away.

In these cases, the incentives simply push effort in the wrong direction. 

If the path is well-known, extrinsic incentives can accelerate progress. However, if discovering the path is the work, pushing harder may simply get people to the wrong destination faster.

2. Cognitive orientation

In an elegant series of experiments, Teresa Amabile and her colleagues, had children and adults perform creative activities under different reward conditions. The key manipulation was whether participants understood the activity itself as something they were doing in order to obtain a reward. Across the studies, explicitly contracting to perform the creative activity for a reward reduced the creativity of the resulting work relative to relevant comparison conditions. 

The problem is not necessarily the reward itself. It is what the reward can do to attention. Without a salient external incentive, someone absorbed in a creative problem might ask: “What would happen if we tried the opposite?” or “What assumptions are we making?”

But when you make the reward salient, a different question enters the mental workspace:”What do I have to do to earn it?” That question can be highly productive when the task is unambiguous and predictable. But innovative work is often the opposite.

A meta-analysis of 60 studies found that rewards explicitly contingent on creative performance tended to improve creativity, particularly when accompanied by constructive, task-focused feedback and meaningful choice. Ordinary performance- or completion-contingent rewards, however, showed a small negative relationship with creative performance. 

In other words, incentives become attention-directing devices. Tell a team you need twenty ideas and twenty becomes important. Tell engineers that they will be evaluated on lines of code and code volume becomes important. 

Again, the employees aren’t behaving irrationally. They are learning what the organization has made salient and they are simply altering their effort accordingly.

3. Social interaction

The most consequential effects of incentives may not happen inside an individual’s head at all. They happen between people.

In one study, researchers looked at forced-distribution performance systems i.e. the practice of evaluating employees relative to one another. When participants performed a task individually, forced ranking increased their speed. But when work became collaborative, the results reversed. Not only did forced ranking slow down task completion, it also significantly reduced knowledge sharing. 

Consider a scenario where two people are collaborating on a new product. If one person discovers an insight that could significantly enhance the other’s part of the  project, passing that information along makes sense when the mutual outcomes are aligned.

However, if both people are competing for a single promotion, things get tricky. One study investigated this dynamic and found that strong promotion incentives were associated with greater individual effort but lower helping effort toward coworkers. 

A conventional performance-management system may record the first effect but completely miss the second one. For creative work, that blind spot is more dangerous. Innovation is rarely the product of isolated brilliance. Someone supplies an analogy, challenges an assumption, shares a contact, offers technical knowledge, or spends an hour helping a colleague escape a dead end.

Competitive incentives can put a price on that generosity. And under stronger competitive conditions, the consequences can move beyond withholding help.

In one study researchers created experimental tournaments in which participants could improve their chances of winning through productive effort or through actions that reduced a competitor’s performance. As the difference between the winner’s and loser’s rewards increased, participants exerted more productive effort and more sabotage. 

Most workplace sabotage isn’t very dramatic. It often looks more like a delayed reply,  a useful insight kept private, or an idea presented as “mine” rather than “ours.” Individually, these decisions can be rational but collectively, they can destroy the culture needed to support creative work.

That is why incentive design is ultimately also relationship design. It tells employees whether a talented colleague is primarily a source of knowledge, a partner in discovery or a threat to their own reward.

The real question behind every incentive

The Maine officials who paid for seal noses made an understandable mistake. They could not directly purchase their real objective, so they created a measurable proxy. 

More than a century later, organizations do the same thing with sales targets, KPIs, patent counts, performance ratings, promotion tournaments, publication metrics, and innovation bonuses.

The lesson is not that incentives are inherently corrosive. Incentives can increase effort, but they can also redirect effort, narrow attention and change colleagues into competitors. 

So before attaching a reward to creative work, leaders should think carefully about these questions: What behavior will this incentive intensify? What will it cause people to pay attention to? And how will it affect collaboration? These questions shift incentive design from a compensation problem to a problem of organizational architecture.

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What If Becoming a “Human in the Loop” Is a Bigger Risk Than Replacement?

By the late 1960s, the automobile assembly line stood as one of modern management’s great achievements. It had transformed carmaking into a system of extraordinary scale, speed, and consistency. Yet the same system was experienced very differently by many of the people working within it. Describing his job in a General Motors paint shop, one worker quipped:

“There’s a lot of variety in the paint shop. . . . You clip on the color hose, bleed out the old color, and squirt. Clip, bleed, squirt, think; clip, bleed, squirt, yawn; clip, bleed, squirt, scratch your nose. Only now the Gee-Mads [the General Motors Assembly Division industrial engineers] have taken away the time to scratch your nose”.

The worker remained essential to production but had little authority over the work as a whole. He was, quite literally, a human in the loop.

Several thousand miles away, another worker also repeated a small set of movements every day. Behind his ten-seat sushi counter in Tokyo, Jiro Ono sliced fish, shaped rice, pressed, and served with gestures refined over decades. From a distance, his practice could appear almost as standardized as the automotive work. Yet its apparent sameness concealed continual variation. Rice changed with temperature and humidity; different fish required different preparation; pressure appropriate for one piece could be excessive for another. What looked to a novice like execution of a recipe was, for the master, a sequence of situated judgments developed through attentive practice. Ono described his process simply:

“I do the same thing over and over, improving bit by bit. There is always a yearning to achieve more. I’ll continue to climb, trying to reach the top, but no one knows where the top is. Even at my age, after decades of work, I do not think I have achieved perfection. But I feel ecstatic all day.”

Ono’s repetition made his perception increasingly discriminating. Each iteration provided feedback, improving both the outcome in the present and his ability to judge more finely in the future.

The automobile worker and the sushi master therefore present a paradox. Both repeat, operate within constraints, and participate in larger production systems. Yet repetition degrades the work of one while making it meaningful to the other. The difference is not repetition itself, but how work distributes agency, cognitive challenge, and opportunities for learning. 

The contrast raises an important question for the age of generative AI:

Will AI make more of us like Jiro Ono or more like the assembly-line worker?

The answer may have less to do with the sophistication of the technology than with how we choose to design work around it.

A new kind of Fordism

Much of the conversation about AI and work focuses on jobs: Which jobs will disappear? Which professions are safe? What percentage of a role can be automated?

Those are reasonable questions, but long before AI eliminates an occupation, it can redistribute the thinking used in that occupation.

Imagine a teacher who once designed lessons himself but now increasingly asks AI to create them and then reviews the result. Their productivity might have increased but something important changed in the process. As AI performs more of the judgment-rich activities through which the person previously learned, experimented and developed expertise, human work becomes narrower, limited to prompting, checking, approving and correcting.

We call this emerging pattern cognitive Fordism: a division of cognitive labor in which AI increasingly performs the thinking through which people develop judgment, while humans are left supervising AI’s output.

This raises a fundamental question: Which parts of our thinking are we happy to outsource, and which parts do we need to keep exercising if we want people to remain capable?

Not every use of AI should look the same

One reason this question is difficult is that the same task can mean very different things to different people.Suppose three people ask an AI system to help write a report. 

The first person understands the subject thoroughly so writing the report might simply be a routine task for them. Having AI produce a draft may be an excellent use of automation for such a person.

The second person is new to the field. Writing the report is partly how they will learn to structure an argument and understand the material. Generating the answer for them may save time today while removing the very struggle that would have developed their capability tomorrow.

The third person is trying to invent a completely new approach. They may not want AI to give them an answer at all. They may want it to challenge assumptions, offer unusual alternatives and provoke new directions.

So while on the surface, the activity might look the same it needs three completely different interaction models with AI. 

This is why the conceptual framework in the figure above begins not with the task itself, but with the person’s intention. It describes three broad orientations for AI-mediated work: Execute, Learn and Create.

In Execute, the goal is reliable completion. We already know roughly what good looks like, so allowing AI to carry more of the workload makes sense.

In Learn, the goal changes. The immediate answer matters less than what the person will understand or be able to do afterwards. Here, the best AI may behave more like a tutor by offering explanations, questions or graduated hints rather than simply completing the task.

And in Create, the objective is neither efficient execution nor mastery of an existing solution. It is expanding the possibility space. AI becomes a thought partner: generating alternatives, challenging assumptions or helping connect ideas that might otherwise remain separate.

These orientations are not a ladder in which Create is somehow superior to Learn and Learn superior to Execute. Sometimes execution is exactly what we want. There is little benefit in forcing a senior scientist to manually perform routine writing if their attention would be better spent solving a difficult scientific problem.

The mistake is using Execute as the default relationship for everything simply because AI can produce an answer quickly.

Agency plays a strong role

Most moderately complex work does not stay neatly inside Execute, Learn, or Create. People continually move between them.

A scientist may first need to learn enough to understand an unexpected result, then create several possible explanations, then execute an experiment to test them. The outcome may trigger another round of learning, reframing, and experimentation. The same is true in many professional and educational tasks.

That is why agency matters so much in AI-mediated work.

If the appropriate relationship with AI changes as the work unfolds, people need the ability to change that relationship too. We call this directional autonomy: the ability to influence whether the interaction is oriented toward Execute, Learn, or Create. This is different from substantive autonomy, which concerns how much authority a person has over the work itself—its goals, framing, methods, and standards.

Both matter, but they are not equally available in every situation.

An employee may have little control over the objective they have been assigned. A student may not get to choose the learning outcome, assessment criteria, or even the method they are expected to use. In those situations, substantive autonomy is partly constrained by the surrounding organization, teacher, or institution.

But even when people cannot choose what they are ultimately trying to accomplish, they can still benefit from having some control over how cognition is divided between themselves and AI.

This is why an AI system should not lock someone into a single mode simply because it inferred that mode at the beginning of a task. The system might reasonably infer that the user is trying to Execute, Learn, or Create, but that choice should remain visible and easy to override.

Without that flexibility, AI risks pushing people toward a default role (often execution and supervision) even when the work actually calls for learning or creative exploration. 

From AI adoption to work design

Organizations can easily fall into a productivity trap when using AI. It’s easy to see how much coding was completed or how quickly an email was written. But human development is harder to see. Did the student deepen their understanding of the topic? Or, did a manager develop better judgment? These capabilities accumulate slowly, often through exactly the parts of work that initially feel inefficient: wrestling with uncertainty, considering alternatives, making mistakes and revising our thinking. 

Prioritizing short-term efficiency over deep engagement can erode the very skills needed for future work, putting sustained success at risk.

Viewed from a long-term perspective, incorporating AI becomes a work-design problem, not just a technology-deployment problem. At an organizational level, that might mean conducting a cognitive audit: examining where AI is taking over analytical, creative and practical effort; whether employees can reclaim or redirect that work; and whether jobs are becoming richer or slowly collapsing into narrow monitoring and approval activities.

The future of human–AI collaboration should be judged by more than the speed or quality of the output. A good system should help us produce better work without reducing our capacity to shape the work that comes next.

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AI, Layoffs and the Innovation Tax

The promise of AI is rapidly becoming a workforce question.

In a memo last year, Amazon CEO Andy Jassy told employees that as generative AI usage spreads through the company, “we will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs.” He went further: “in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.” 

Duolingo CEO Luis von Ahn’s “AI-first” memo said that they would “gradually stop using contractors to do work that AI can handle,” and that “headcount will only be given if a team cannot automate more of their work.” 

IBM CEO Arvind Krishna offered perhaps the clearest version of the attrition model. Speaking about back-office functions that could be automated, he said, “I could easily see 30% of that getting replaced by AI and automation over a five-year period.” 

Taken together, these statements reveal a new leadership strategy: automate and reduce headcount as AI expands, or in some cases, in anticipation of higher productivity. It is an understandable response to a technology that can write code, resolve customer tickets, draft marketing copy, summarize meetings, and generate analysis at extraordinary speed. 

But there is a danger in this story. It assumes that innovation is produced by tasks. In reality, innovation is produced by people working together in messier ways that defy easy automation – experimenting, discussing, dissenting, and learning through customer interactions. When companies cut too broadly, they may inadvertently remove the conditions that will allow AI-era innovation to compound.

The tricky question for leaders is whether they can distinguish between work that is truly automatable and work that looks inefficient only because its value is hard to capture in a balance sheet.

Impact of Downsizing on Innovation

The danger is not that every layoff is bad. It is that broad layoffs, especially when framed as an AI-enabled operating upgrade, can confuse labor reduction with organizational learning. A company can become leaner and less capable at the same time.

One of the earliest systematic warnings came from Teresa Amabile and Regina Conti’s 1999 study. They examined a large high-technology firm before, during, and after a major downsizing. Their central finding, apart from a worse morale, was that the conditions that support creativity deteriorated. Creativity, productivity, and perceived work-environment support declined during downsizing, while obstacles to creative work increased. 

That finding should land heavily in today’s technology sector. Innovation is rarely born from an isolated genius typing faster with better tools. It is more often a social process: someone notices an anomaly, someone else connects it to an unmet customer need, a third person remembers a failed experiment from two years earlier, and a fourth turns the conversation into a prototype. Remove enough nodes from that system and the org chart may still look coherent, but the creative network underneath it becomes brittle.

Newer research adds an important nuance. Ramdani and colleagues studied 122 UK firms over 22 years, using downsizing and patent data to examine how workforce reduction affects innovation outputs. Their conclusion is not “layoffs always reduce innovation.” It is more precise: downsizing has a dual effect depending on the firm’s resource position. In firms with resource slack, downsizing can have a positive effect on innovation. In resource-constrained firms, it has a negative effect, and the damage appears more quickly. 

This matters because many tech companies do not experience resource slack uniformly. One division may have layers of coordination, duplicated tooling, and unclear ownership. Another may have exhausted engineers, customer-support escalation queues, and neglected technical debt. From 30,000 feet, both may look like “headcount.” But only one will lead to higher innovation on downsizing.

The key question for leaders is: are you cutting fat, or are you cutting connective tissue? Because downsizing in a resource-constrained organization can significantly hurt innovation down the road. 

Psychological Effects of Layoffs

Why do layoffs damage innovation when there is little slack? The answer lies in psychology as much as economics.

The first mechanism is psychological safety collapse. Innovation requires people to take interpersonal risks: challenge assumptions, admit uncertainty, surface bad news, and suggest ideas that may initially sound naïve. Amy Edmondson and Derrick Bransby’s 2023 review of psychological safety research describes its importance for learning behavior, performance, and work under uncertainty. A meta-analysis of 94 studies also found psychological safety positively associated with both employee innovation behavior and team innovation behavior. After broad layoffs, however, the unwritten rule often becomes: do not look expendable. In that climate, people do not stop having ideas; they stop volunteering them.

The second mechanism is survivor syndrome and identity rupture. Research on downsizing survivors shows that employees who remain often experience reduced commitment and performance, a pattern commonly described as survivor syndrome. Van Dick and colleagues found that downsizing can reduce employees’ identification with the organization, which then harms survivor performance. The innovation consequence is direct. The person who no longer identifies with the company may still complete assigned tasks. But will they fight for an unproven customer insight? Will they spend political capital defending a long-term bet? Will they mentor the junior colleague who may one day become a breakthrough inventor? Often, the answer is no.

The third mechanism is job insecurity narrowing attention. When employees fear future cuts, their time horizon contracts. They focus on visible output, defensible metrics, and work that protects their standing. Niesen and colleagues’ research on job insecurity and innovative work behavior notes the paradox that organizations often expect restructuring to enhance innovation, even as insecurity can undermine the behaviors innovation requires. This is the problem with fear-based efficiency: it may increase activity while decreasing imagination. 

There is also a network mechanism. Corporate knowledge is not stored only in documents or AI retrieval systems. It lives in relationships: who knows which customer exception matters, why a particular architecture decision was made, or which workaround keeps a product alive. Broad layoffs sever these ties indiscriminately. AI cannot easily reconstruct what the organization failed to write down.

A 2024 HBR article based on a study of 146 companies found that engagement, morale, and loyalty can take years, not months, to rebound after layoffs. The authors cite Pixar director Brad Bird’s memorable observation: “If you have low morale, for every $1 you spend, you get about 25 cents of value. If you have high morale, for every $1 you spend, you get about $3 of value.” Whether or not one takes the math literally, the leadership lesson is still valid: the same dollar value produces radically different returns depending on the emotional state of the system. In a frightened organization, talent becomes defensive. In a committed organization, talent becomes generative. That difference will determine whether AI becomes merely a substitute for human contribution or a catalyst for the next wave of innovation.

The Way Forward

So, how can leaders figure out if layoffs are the right move? It starts with three key diagnostic questions.

First, where is work waiting? Slack isn’t always visible in just headcount ratios. Look for queues: unresolved customer issues, delayed experiments, or rising technical debt. If critical work is already backing up, you’re resource-constrained and cuts will only make things worse.

Second, where has learning slowed? Beyond simple experimentation metrics, track behavioral indicators. Are people trying fewer new things? Are you seeing fewer dissenting views? A drop in any of these suggests learning is stalling.

Third, where is human judgment still doing hidden work? AI can automate routine tasks, but leaders must map the judgment layer. This critical layer handles edge cases, ethical tradeoffs, and customer reassurance. Removing people who interpret these ambiguous situations could make the process faster, but the organization will get less intelligent.

Klarna learned their lesson the hard way. They cut about 700 employees citing the productivity of their AI assistant, but later had to reverse course when they had to reassign engineers and marketers back to customer-support roles when AI didn’t hit the mark. 

However, the deeper challenge for leadership is a philosophical one. The industrial-age instinct was simple: reduce labor when a machine increased productivity. That made sense when work was predictable. But in the innovation age, the question is entirely different: when a new tool boosts productivity, where do you redeploy the freed human capacity to gain a learning advantage?

That is the fork in the road. Companies using AI mainly for headcount reduction might win a margin story but lose their innovation future. Conversely, companies that reinvest human attention into discovery, customer understanding, and experimentation will compound their advantage.

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AI is Straining the Leadership Model That Built Most Companies

When Jos de Blok looked at Dutch home care, he saw a management model that had become part of the problem.

Home care nursing is not a tidy production process. Every patient brings a different mix of medical needs, family dynamics, living conditions, emotional realities, and sudden changes. Small signals matter and context changes fast. The people closest to the patient often hold crucial tacit knowledge that cannot be captured fully in a procedure manual or escalated up a chain of command in time to matter. 

In Cynefin terms, this is a complex environment: there are too many interdependent variables that make it impossible to create an efficient centralized process.  But the system that de Blok had known as a nurse and later as a leader was built as if home care were merely complicated. It leaned on specialization, managerial oversight, and layers of coordination designed to create control. But for this complex problem, those layers became part of the problem.

Harvard Business School’s account of Buurtzorg notes that de Blok had seen “counterproductive layers of management” undermine care quality and frontline discretion. So he made a radical wager: if the work itself was complex, the answer was not more hierarchy. It was a different theory of leadership. Buurtzorg organized care around small self-managing neighborhood teams, with minimal middle management and a lean support structure. The center stopped trying to out-think the edges and started enabling them. 

That story matters far beyond healthcare. It captures the mistake many organizations now risk making with AI: applying a top-down management model to challenges that are, at least in part, complex.

AI Is Not One Leadership Problem. It Is Two.

Most executive conversations about AI still assume a single challenge: implementation. Buy the tools, train the workforce, hire the experts, and move fast. But AI is creating at least two very different leadership problems.

Some AI problems are complicated. They require expertise, analysis, and disciplined systems. Think data architecture, cybersecurity, privacy, model evaluation, legal compliance, workflow redesign, and technical governance. These are not simple issues, but they are tractable. The right response is rigorous diagnosis, strong standards, and clear accountability. In Cynefin terms, leaders in this domain must sense, analyze, and respond. 

Other AI problems are complex. How will customers behave when AI becomes embedded in products and services? Which use cases will create durable value rather than just attention? How should judgment be divided between humans and machines? What happens to culture when some employees trust AI deeply, others distrust it, and many use it informally out of management’s sight? Those are not problems that yield to a leadership memo. They require leaders to probe, sense, and respond. 

This distinction sounds abstract until you see its consequences. If leaders treat a complicated problem as complex, they can drift into improvisation where rigor is required. But if they treat a complex problem as complicated, they over-centralize, over-standardize, and under-learn. That second mistake may be the defining leadership failure of the AI era.

The Shift From Answer-Giver to Context-Setter

For decades, many leaders rose by being decisive, analytical, and visibly in control. Those traits still matter. But in complex conditions, they are not enough. The leader who insists on having the answer too early can shut down the very learning the organization most needs.

This is where Buurtzorg offers such a powerful lesson. De Blok did not just become a more empathetic leader. He changed his model of what leadership is for. In a complex system, the leader’s job is to create the conditions in which good judgment can emerge throughout the system. That requires adopting a different mindset about authority. 

In the complicated parts of AI, leaders should tighten standards, elevate expertise, and demand rigor. In the complex parts, they should widen participation, encourage small experiments, protect dissent, and reward learning. The critical leadership skill is knowing when to switch.

Why Swarm Intelligence Matters More Than Executive Certainty

Business leaders often talk about “empowering employees,” but complex problems demand something more precise: they demand systems that let intelligence emerge from many places.

Research by Anita Woolley and colleagues found evidence for a general collective intelligence factor in groups. Strikingly, group performance was not tied to the highest individual intelligence in the room. It was more closely associated with social sensitivity and with more equal conversational turn-taking. In practical terms, groups get smarter when more people can meaningfully contribute and when interaction patterns allow insight to surface, not just status to dominate. 

That should provoke an uncomfortable question for senior leaders: what if your organization is full of intelligence that your culture cannot hear?

In complex AI environments, breakthrough insights often begin at the edges. A sales manager notices where customers actually trust the tool. A service employee spots a subtle failure mode. A product designer sees that the real opportunity is not automating the old workflow, but redesigning it entirely. A junior analyst challenges the executive team’s favorite use case and turns out to be right. In a complex environment, these become the raw material of strategy.

The organizations that learn fastest from AI will not be those with the most polished top-down vision. They will be those with the richest lateral sensing mechanisms: more experimentation, more challenge, more idea collisions, and more pathways for weak signals to travel upward and sideways.

Culture Is Your Operating Infrastructure.

That is why culture cannot be treated as a side topic in AI transformation. Culture determines how well an organization learns.

Amy Edmondson’s research on psychological safety showed that teams learn more effectively when people believe the environment is safe for interpersonal risk-taking. In sage cultures, people speak up more and admit mistakes sooner. They raise concerns before problems metastasize. Psychological safety is associated with learning behavior because it lowers the social cost of candor. 

Why does that matter in AI? Because AI adoption is full of ambiguity. Employees are constantly making judgment calls: when to trust the tool, when to override it, when to disclose its use, when to question the workflow, and when to challenge leadership’s assumptions. In a fearful culture, they will hide uncertainty, perform confidence, and quietly work around the system. In a learning culture, they will surface anomalies, share experiments, and improve the system in public.

Many organizations say they want innovation, but their incentives still reward obedience. They say they want initiative, but punish failed experiments. They say they want challenge, but subtly penalize people who question senior leaders. Instead of an innovation culture, it leads to a  compliance culture.

Buurtzorg worked because the shift was structural, not rhetorical. Frontline teams did not merely get permission to speak up. They got real discretion. The system was redesigned around the reality that those closest to the patient were best positioned to respond to complexity. 

What Leadership Looks Like in the AI Era

So what should leaders actually do?

First, diagnose the domain. Ask: is this AI challenge primarily complicated, complex, or a blend of both? That question should come before the org chart, the governance model, or the training plan. 

Second, match the leadership response to the problem. In complicated domains, clarify ownership, concentrate expertise, and build strong review mechanisms. In complex domains, run more small experiments, widen participation, shorten feedback loops, and let the people closest to the work challenge assumptions early.

Third, redesign incentives around learning. You cannot build collective intelligence in a culture where dissent is risky and failure is career-limiting. If leaders want employees to behave like owners, the system must make it safe to notice, question, and improve.

Finally, rethink the role of middle management. In too many organizations, middle layers still function mainly as transmission belts for approval and control. But in a complex environment, the best middle managers help signals travel. They turn the organization into a smarter sensing system rather than a slower permission system.

The Leadership Advantage That Will Matter Most

The AI era will reward many familiar strengths: technical fluency, strategic clarity, disciplined execution. But over time, the most valuable advantage may be more subtle.

It will belong to leaders who can tell when expertise should dominate and when emergence should. Leaders who know when to act like engineers and when to act like gardeners. Leaders who understand that hierarchy is still useful, but not universally wise. Leaders who stop asking, “How do I get the organization to execute my answer?” and start asking, “How do I build an organization capable of discovering better answers than I could alone?”

That is the deeper lesson of Buurtzorg. Jos de Blok did not save a struggling system by becoming a more forceful commander. He succeeded because he recognized that in a complex human system, the smartest move is to increase the system’s capacity to learn. 

AI now puts that same choice in front of every executive team. Some problems will still require experts, precision, and control. But many of the most consequential ones will require humility, experimentation, and trust in intelligence distributed throughout the organization. The companies that thrive will not just deploy better tools. They will build cultures where insight can rise from anywhere, where leadership adapts to the problem at hand, and where the search for the right answer matters more than protecting the illusion that it already lives at the top.

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The “Tiny Team” Organization Is Here and It’s Redrawing the Management Map

In the past year or two, the business world has felt more like a bumpy ride than a smooth “transformation.” Employees are dealing with a lot of uncertainty—roles changing or getting eliminated entirely, teams shuffling, and rules shifting mid-game. But leaders aren’t operating from a crystal-clear blueprint either. Many are making big cuts, not just because AI speeds things up, but because they honestly can’t see what the company should look like long-term. So, they reduce costs and complexity first, then plan to rebuild smarter.

The tricky part is that AI isn’t a neat replacement for people or their jobs. It absolutely makes many tasks faster, but it also creates entirely new work. Think about customer support: many companies use chatbots to handle volume, but now someone has to watch performance, check logs to find problems, fine-tune the prompts and rules, and constantly improve the system. The work doesn’t disappear; it just moves and changes form.

Still, one advantage is unmistakable. For decades, the biggest hidden cost in any company was the Coordination Tax. You know the drill: an engineer builds a feature, then hands it to a product manager, who syncs with marketing, who waits for a business dev lead to find a partner. Every handoff is a friction point and every meeting is a tax on productivity. But now, a single “pod” of 5 to 10 people—comprising a mix of engineering, design, and growth talent—can now execute faster than a 50-person department.

2026 looks like the year that some of these trends become normal: smaller, multidisciplinary, autonomous feature teams, and flatter organizations where the middle-management role evolves from “traffic cop” to “coach + systems designer.”

Trend 1: Smaller, multidisciplinary “feature teams” become the default

As AI lifts individual capability, it becomes feasible to staff product work like a small startup.

Instead of a large, functionally segmented machine, you get teams of 5–10 people who can handle a feature from the initial idea all the way through building, shipping, and improving it. Speed shoots up because there is less coordination and fewer approvals needed. Quality rises because feedback loops tighten.

We can already see executives publicly describing this “tiny team” dynamic.

  • Mark Zuckerberg recently noted that AI lets “a single very talented person” tackle projects that used to need “big teams,” and he’s actively pushing to “flatten teams.”
  • Tobias Lütke, Shopify’s CEO, gave his teams a powerful signal: before asking for more people, you have to prove why you “cannot get what you want done using AI.” This directly asks them to think of AI as a part of their team that handles work autonomously.
  • Duolingo’s CEO, Luis von Ahn, sees AI as a “platform shift” and is adjusting things like hiring and performance. They’re also reducing contractor work where AI can step in—another clear move toward “smaller teams that deliver much more.”

The common thread in these examples isn’t just “AI is useful.” It’s that they are rethinking the basic rules of how the company operates. The organization shifts from shuffling work between departments to empowering small, focused teams to fully own their results.

Ever watched a two-person team crank out something amazing over a weekend and thought, “How did they move so fast?” Now, imagine that kind of efficiency happening across dozens of teams, each one fully supported by AI.

That’s the emerging design pattern.

Trend 2: Flatter orgs and middle management under pressure

Once small, autonomous teams are successful, a second order effect kicks in: you just don’t need as many layers of management to keep them in sync. This is the point where the discussion about “flattening” an organization becomes very real, and uncomfortable.

Experts studying the workforce have been tracking “delayering” everywhere, not just in tech. Korn Ferry, for instance, talks about companies “thinning out their management midsections,” pointing out that middle managers were a big part of 2024 layoffs, a clear evidence that the traditional manager role is under structural pressure.

And major corporations are openly saying their restructures are about cutting bureaucracy. Amazon’s corporate layoffs, for instance, were reported as an effort to reduce organizational layers and operate more efficiently.

So, the pattern we should expect to continue seeing in 2026 isn’t a world with “no managers.” It’s a world with fewer managers whose fundamental job is completely different.

Which brings up an important, yet simple, question: If AI reduces coordination work, what exactly should managers coordinate?

The manager’s role is evolving

In most modern orgs, managers have been doing (at least) three different things:

  1. People development: coaching, feedback, growth, hiring, conflict navigation, culture
  2. Technical/project leadership: running execution, reviewing work, unblocking tasks, prioritizing
  3. Systems and strategy: setting guardrails, aligning across teams, shaping operating systems, long-term planning

AI and autonomous feature teams change the distribution of these responsibilities.

1) People development becomes more important, not less

As teams become more independent and things change faster, people really need grounding: clear direction, a safe space to work, ways to grow, and honest feedback. AI can help write a review but it can’t handle the human side of building trust, shaping identity, and finding meaning in our work.

2) Day-to-day technical leadership moves closer to the team

Here’s where the scope for many middle managers gets a little smaller. When you have a focused feature team, the day-to-day execution leadership often happens right within the group—think a senior engineer, the product and design leads, and a shared AI process. The manager’s job shifts away from being the daily air traffic controller.

3) Systems-level strategy becomes the manager’s differentiator

As pods proliferate, someone must design the system those pods operate within: how often do they operate, what are the quality rules, how do we control for risk, what are the portfolio priorities, and how do these teams talk to each other?

This is exactly the direction highlighted by McKinsey & Company in its writing on “agentic” organizations: we’ll see more M-shaped supervisors (broad generalists orchestrating agents and hybrid work across domains) alongside T-shaped experts (deep specialists safeguarding quality and exceptions). 


As agents take on more execution, managers are freed up from admin tasks. Their focus is shifting toward leading people and orchestrating these blended systems. In other words, the ideal talent profile is changing. It’s less about being “the smartest technical person in the room” and much more about emotional intelligence and the ability to think strategically and connect the dots.

The emerging operating model

So what does this look like in practice? Expect more organizations to formalize patterns like:

Autonomous Feature Pods: Small teams of about five to ten people, each with a crystal-clear mission, like “improve customer sign-ups.” These teams are accountable for everything, from building to shipping to measuring success. They have all the necessary skills embedded right in the pod—product, design, engineering, and data. What makes them so fast? They use AI agents as a built-in assistant for everything from research and drafting to testing and analysis.

Thinner Management Layers: This shift also means a leaner leadership structure. You’ll see fewer layers of management. Instead, managers will take on a wider scope, focusing less on directing tasks and more on coaching and ensuring the system is running smoothly. We’ll also see more senior, non-managerial roles, like staff or principal engineers, who provide technical leadership without adding more hierarchy.

Guardrails over Gates: Finally, the way work is governed is changing from “approval-heavy” to “principle-driven.” Instead of waiting for sign-offs on every step, teams operate within clear “guardrails”—security protocols, data policies, quality standards, and ethical rules. This allows teams to move much faster and ship products without constant delays.

The trend for the rest of 2026 is clear: Organizations will continue to shrink in headcount but explode in impact. We are moving away from the “industrial” model of management, where people were cogs in a machine, toward a “biological” model, where small, autonomous cells work together to create a living, breathing, and highly adaptive organism.