The New Friction

Why AI Transformation Stalls and What to Do About It

Most writing about AI change management is about using AI to do change management work. AI-powered surveys, AI-coached stakeholder communications, AI-generated resistance reports. That angle dominates the top results on this search term. McKinsey, Prosci, IBM, ICAgile, Udemy all lean there.

We mean the inverse: managing the human and organizational change that AI adoption requires.

This is not a semantic distinction. The gap between those two framings is exactly the gap between organizations that get AI working and organizations that stall for two years wondering why. When you treat AI change management as a tooling problem, you solve for the wrong variable. The tool is rarely the reason transformation fails.

The case that makes this concrete involves two trucks and a container port.

Two trucks break down in a port. They are thirty meters apart, on the same lane, carrying the same cargo. One port zone recovers from the disruption in seventy minutes. The other takes more than two hours. The zones share everything that matters: the same bridges, the same lane widths, the same weather, the same sixty-second mechanical fault. The only difference is coordination. In the slow-recovery zone, a single algorithm dispatches every vehicle. In the fast-recovery zone, that same algorithm shares infrastructure with a fleet of trucks driven by independent logistics companies, each operating under its own objectives.

That is the finding M. Dalbert Ma, a researcher at London Business School, reported to the BIG.AI@MIT conference this year, after studying approximately one year of operations at one of the world’s largest container terminals. The autonomous zones ran 3.8% more efficiently under normal conditions. A single sixty-second fault cost them a 12.2% delay on the operations that followed. Rain, which forces every vehicle to slow and creates temporal buffer between sequential operations, erased the fragility entirely.

This is what most AI transformation stories leave out. The efficiency gain is real. So is the cost you pay when something disrupts it. Real AI change management is the work of managing that tradeoff before the fault arrives, not after.

The 5 Frictions of AI Transformation

Every AI transformation engagement we have worked runs into the same five blockers. Not technology failures. Human and organizational frictions that the tool vendors do not mention and the training programs do not cover.

We call this the 5 Frictions framework. Each friction is distinct, each stalls transformation in a different way, and each requires a different response.

The Identity Friction. When knowledge workers are asked to share their specialized knowledge with AI systems, a real fear surfaces. The fear is not irrational. Their expertise is the basis of their value. AI that learns from their domain know-how threatens the moat they have spent years building. At one of our executive dinners this spring, the framing that landed cleanest was this: when people are asked to give their knowledge away, they experience it as becoming disposable. Identity work is not soft. It is the most operational blocker on your AI adoption agenda, and leaders who skip it discover the passive resistance later, when workflows are technically live but adoption stays flat.

The Leadership Friction. Leaders who are not personally using AI cannot guide teams that are. If you are not using AI on the order of every hour, you cannot evaluate which of your teams’ experiments have merit, which are theater, and which represent a genuine capability gain. You are coaching a sport you have never played. The practitioners in the room can tell immediately. This framing has now come up at every executive dinner we have run across Boston, Boulder, Houston, and Dallas, without prompting. The corollary the Boulder room added sharpens it: even when the CEO mandates AI adoption, if there is no facilitation and no design behind the rollout, the burden falls on already-overwhelmed individual contributors and the initiative fails.

The Capability Friction. Every AI-first workflow your organization designs makes a structural tradeoff. When execution time collapses, coupling tightens. When coupling tightens, buffer disappears. The same mechanism that produces the efficiency also produces the fragility. And beneath the efficiency numbers, something is accumulating invisibly: the growing gap between your organization’s apparent capacity and its actual adaptive capacity. JoAnna Vanderhoef named this Capability Debt at the BIG.AI@MIT conference in 2026. We will return to it in detail below, because it is the friction with the most solid research base and the most counterintuitive implication.

The Measurement Friction. The metrics organizations reach for first, time saved, tokens consumed, story points closed, either cannot be measured cleanly or create perverse incentives. At three consecutive dinners this year, we asked the room what they were measuring and whether it was working. Not once did a satisfying answer emerge. What surfaced instead were stories about measurement going wrong: algorithms that extrapolated to billions of hours saved, CEOs who set token-consumption targets that had teams running meaningless jobs at night just to hit the number, story-point metrics that broke down the moment AI made commits larger. The measurement problem is real, and the solution is not a better metric. It is a different understanding of what counts as progress during early-stage AI adoption.

The Sequencing Friction. Who does what, when, in what order? Most organizations have not answered this. They have an AI strategy document and a handful of enthusiastic early adopters and no clear answer to the role questions: who is the AI champion, who is the AI lead, what decisions belong to an AI governance council, what does AI ops mean for their context. Without those answers, every initiative stalls at the first ownership dispute. The sequencing friction is often invisible until it surfaces as a conflict, and by then it has already cost the organization months.

These five frictions do not appear on procurement spreadsheets. They are not solvable with a training event. They are the actual work of AI change management.

What the Evidence Shows

The Capability Friction is worth dwelling on, because it has the most solid research base and the most counterintuitive implication for how organizations should design their AI adoption roadmaps.

JoAnna Vanderhoef’s concept, Capability Debt, describes the growing gap between an organization’s apparent efficiency and its adaptive capacity. It accumulates subtly, as absence. Absence of novelty detection. Absence of the junior employee who stumbled into the strange request and learned how to triage it. Absence of the reviewer who noticed the model’s output was technically correct and strategically wrong. Absence of the senior whose judgment was trained on edge cases the automated pipeline now handles without them.

You do not see the debt until you need to do something the system was not built for. By then, the people who would have done it have atrophied the capability, or have never built it at all.

This is the part of AI transformation that is easy to underweight in a board deck. Efficiency is legible. Judgment loss is not. It hides inside the year-over-year improvement metrics and inside the reduced headcount and inside the deliverables that ship faster and look clean until a situation arrives that needs taste, or context, or the ability to know what is not in the data.

A team of researchers at MIT, Yale, and Microsoft, led by Mert Demirer, formalized the mechanism for where the debt accumulates fastest. They call it AI chains. An AI chain is a sequence of production steps in which the automated steps are contiguous. The human at the end verifies only the final output. The economic incentive is to keep adding steps to the chain until the marginal failure probability overwhelms the saved verification cost.

The jobs that get automated fastest are the ones where AI-suitable work clusters together. Lecture preparation is one such job. Research, drafting, slide generation, and example synthesis are all AI-suitable, and they are sequential. A single verification at the end is sufficient. The chain collapses into one unit of human work.

Tutoring is the opposite. AI-suitable steps are interleaved with diagnostic steps that require real-time human judgment. The chain cannot form.

The second consequence is more important than the first. Jobs that form long AI chains are also the jobs where learning loops get shortest. The junior who used to do the research, draft the slides, and watch the senior edit them loses three apprenticeship cycles per deliverable. What was formerly a sequence of moments where skill formed now happens inside the model.

When your team maps its AI automation roadmap, the blocks to be careful about are the contiguous ones. They are where the efficiency gain is largest. They are also where the Capability Debt compounds the fastest.

What We Actually Saw in the Field

Research describes the mechanism. The practitioners in our dinner rooms describe the texture.

Two patterns showed up without prompting at every table this spring.

The first is the training blip. John Ippolito, at the time VP of Enterprise at Miro, shared a Gartner workplace-event graph at our Boston dinner that became the anchor reference for the rest of the evening. The graph shows a flat line of token consumption over time, with a single one-day spike coinciding with formal AI training, then an immediate return to baseline. Adoption of tools is rising. Real usage is not. Every practitioner at the table confirmed it independently. Rachel Brown from CIBC described what works instead: an every-other-week internal showcase where early adopters demonstrate live, the most junior employees show what they have built, and the room asks questions in a safe space. Not training. Social learning, designed and facilitated deliberately.

The most wasted line item in most AI transformation budgets is the training event that produces a one-day blip and nothing durable. The replacement is cheap and repeatable. But it requires someone willing to design and facilitate it, and that role has no title yet in most organizations.

The second pattern is measurement going wrong, and the stories are consistent enough across cities to treat as a pattern rather than an anomaly. Rachel’s head-of-AI at CIBC built a time-savings algorithm that extrapolated to billions of hours saved company-wide. Obviously wrong, and obvious only after the number became absurd. Jason Fournier, CEO of Imagine Learning, described a split his team lives with cleanly: they can measure curriculum creation precisely (from eighty thousand dollars and eight months down to four hundred dollars and four weeks), but cannot measure knowledge-worker productivity gains with any confidence the numbers mean what they appear to mean. Morgan Brown from Wayfair measured AI coding tools by story points, found the metric broke down, and discovered on further investigation that commits were growing in size even as counts fell. Ben Tao from Rockwell put the early-KPI trap cleanly: codify performance targets too soon, and you suppress the experimental behavior that would have surfaced the valuable patterns. “Do that too early, you’re suppressing the good seed.”

A third pattern, specific to organizations that have worked through the early frictions: the role redesign. At our Boulder dinner, one attendee described a customer service team that celebrates publicly when someone automates a meaningful portion of their work, specifically the phrase “automated 40%” landing in a team all-hands, followed by a deliberate conversation about what that person should do with the freed capacity. Two emergent role shapes are forming in that team: a white-glove tier for the most escalated and complex customer interactions where human judgment is irreplaceable, and an agent-orchestrator tier for the people who supervise and maintain agentic workflows. Those are not job titles they inherited from an org chart. They are shapes the team discovered by working through the frictions rather than around them.

For how to structure measurement that accounts for the phase your organization is actually in, rather than where you wish it were, see our piece on how to measure AI transformation success beyond productivity.

The Design Move Most Organizations Skip

Here is what separates the organizations that stall from the ones that scale.

Renée Gosline, in a MIT study presented at the BIG.AI conference, calls it beneficial friction. Her team ran a controlled experiment. Participants worked on cognitive tasks with AI assistance. In the control condition, the AI made its recommendation and the participant accepted or rejected it. In the treatment condition, before accepting or rejecting, the participant was asked to articulate their own reasoning, or to predict what the AI’s reasoning was. That small intervention, which took thirty seconds, measurably reduced over-reliance on AI and preserved the participant’s critical thinking.

This is the design move most organizations skip. They treat friction as waste. They are correct that some friction is waste. They are wrong that all friction is waste. The friction that forces a human to articulate their own judgment before anchoring on the AI’s output is the friction that carries the capability forward.

At the organizational level, beneficial friction looks like this. Decision rights reviews before an AI pipeline goes into production, where the team has to name who owns the outcome the pipeline is producing. Novelty drills, where a percentage of the work that could be automated is routed to humans anyway, so the capability stays alive. Signal sampling, where humans regularly review a random sample of AI outputs not for QA but for drift. Shadow-session reviews, where someone who has not been in the pipeline’s daily operation comes in and asks whether the pipeline is still doing the right thing.

None of these are productivity moves. All of them are capability moves. The point of beneficial friction is not to make the system slower. The point is to keep the system teachable.

AI Change Management Is a Leadership Problem

The organizations navigating this well understand something the organizations that are stalling do not. The new friction is not a technology problem. It is a leadership problem.

When execution was expensive, leadership’s job was to clear the path: remove the blocker, approve the budget, unstick the review cycle. That job is largely done. The organizations still doing it well at the leadership level are optimizing a bottleneck that is mostly already gone.

The new job is different. When execution is cheap and judgment is scarce, leadership’s job is to carry the organization’s judgment capacity forward. That means designing the decisions that matter, surfacing the dissent that would otherwise stay hidden, ensuring that the people who will need the skill later are getting the practice now. Getting executive buy-in for AI initiatives before the first pilot, not as damage control after, is one of the clearest signals we see between transformation programs that sustain and ones that die in the third quarter.

This is facilitation work. Not facilitation in the narrow sense of running meetings well, although that is part of it. Facilitation in the broader sense of helping groups think together, decide together, and build the shared judgment that a single expert, however capable, cannot hold alone. Why AI amplifies the need for great facilitation is something we return to across multiple pieces in this series, because it is the most consistently underweighted factor in every transformation program we have seen.

The organizations that treat AI change management as a tool rollout are solving for the wrong variable. The tool is the easy part. The hard part is building the organizational muscle that keeps judgment distributed across the people who will need to exercise it when the situation changes. And situations always change.

The port example makes this visceral. The efficiency advantage held until the sixty-second fault. Then the organization that had preserved coordination independence recovered faster, because it had not consumed the slack the recovery required. Your organization is running the same experiment right now. You will not know the outcome until the fault arrives.

What to Do About It

The organizations working through this well share three habits.

They take Capability Debt seriously as an accounting category. Not formally on the balance sheet, but in the same way a good engineering team takes technical debt seriously. They know where it is accumulating. They know what they are choosing to trade for it. They revisit the decision when the debt load feels wrong. How to structure an AI transformation roadmap that actually works is fundamentally a question about which automations to sequence in which order, and that sequence question is not just a technical planning decision. It is a capability preservation decision.

They clarify roles before the conflict forces the issue. The question of whether you need an AI champion versus an AI lead, and what an AI governance council actually owns, is not administrative. It is the answer to who is accountable when the model produces something strategically wrong, and who has the standing to say so before it ships. Most ownership failures in AI transformation are not failures of intent. They are failures of structure that nobody bothered to define in advance.

They treat facilitation as infrastructure, not as a soft skill. The change management framework for AI adoption in the enterprise we work from is built around this premise. It is not a template. It is a diagnostic. Where is the Capability Debt accumulating? Where is the Identity Friction blocking adoption? Where is the Leadership Friction showing up as strategy without the fluency to back it? Running a cross-functional AI alignment workshop before a pilot goes into production costs a day. Running a post-mortem after the pilot fails costs a quarter and the trust of the team that ran it.

The capacity to carry judgment through an organization is the durable advantage. Tools will change. Models will change. The organizational capacity to decide well under uncertainty will not.

What Is at Stake

The organizations that hold the line on beneficial friction will move slower in the short term. They will look less impressive in the quarterly efficiency reports. Their AI transformation stories will be harder to tell in press releases.

They will also move further in the long term, because they will still have the people who can do the work the model cannot yet do, and the judgment that closes the gap when the data does not.

The organizations that optimize everything for speed will discover the fragility on the worst possible day. Not because the AI failed. Because the people who were supposed to catch what the AI missed have atrophied the capability to catch anything.

The new friction is not a problem to be eliminated. It is a signal telling you where your organization’s judgment is concentrating. Work with it, and the organization gets stronger. Optimize it away, and you are running Dalbert Ma’s automated zone, waiting for rain.

Frequently Asked Questions

How is AI change management different from using AI in change management? The dominant interpretation of this phrase, the one that fills the top search results, treats AI as a tool that improves how change management is done: faster surveys, smarter stakeholder analysis, AI-generated communication plans. Our interpretation is the inverse: AI change management is the practice of managing the human and organizational change that AI adoption itself requires. The tool is not the problem. How organizations navigate identity, leadership development, capability preservation, measurement, and role sequencing is the problem.

Why do most AI transformation initiatives fail? Most stall because organizations treat AI as a technology rollout when it is actually a leadership and facilitation problem. The tools work. What breaks is the judgment capacity of the organization, the shared decision-making the model cannot replicate, and the distributed expertise that gets quietly hollowed out when contiguous workflows are automated end-to-end.

What is Capability Debt in AI adoption? Capability Debt, named by JoAnna Vanderhoef in 2026, is the growing gap between an organization’s apparent efficiency and its adaptive capacity. It accumulates when AI absorbs work that used to build human judgment. The debt is invisible in productivity metrics and only shows up when the situation changes and the people who would have handled it have atrophied the skill.

How does beneficial friction improve AI outcomes? Beneficial friction is a small intervention that forces a human to articulate their own reasoning before accepting an AI output. Renée Gosline’s 2026 MIT study showed a thirty-second reasoning step measurably reduced over-reliance on AI and preserved critical thinking. At the organizational level, beneficial friction looks like decision-rights reviews, novelty drills, signal sampling, and shadow-session reviews of automated pipelines.

What role does leadership play in AI transformation? When execution was expensive, leadership cleared the path. Now that execution is cheap and judgment is scarce, leadership’s job is to carry organizational judgment capacity forward: design the decisions that matter, surface dissent, and ensure the people who will need a skill later are getting the practice now. That is facilitation work, not project management.

How do you maintain judgment when automating workflows? Treat AI automation roadmaps as capability preservation decisions, not just efficiency decisions. Be most careful with contiguous AI-suitable steps, since those are where Capability Debt compounds fastest. Build beneficial friction into the workflow as a structural feature rather than a removable safety check. Keep humans in the chain even when the model could handle the step, because the capability is the thing the organization is actually buying.

Ready to work the new friction?

If your organization is navigating these frictions, there are ways to go deeper.

Talk to us about the AI Transformation Program. We will help you map where your organization is accumulating Capability Debt, where the five frictions are showing up, and what to do about it.

Read the full frame. Our pillar page lays out the thesis and the three pillars: New Friction, Multiplayer, and Spark.

Build the capability. Our facilitation certification teaches the skills that matter most when the bottleneck is judgment, not execution.