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Which Friction to Remove, and Which to Design Back In
Table of contents
Which Friction to Remove, and Which to Design Back In
For most of the last two decades, removing friction was the whole job. Every extra click, every approval step, every handoff that made a customer wait was waste, and the work of good leadership was to find it and delete it. We built entire disciplines around it. Lean. Six Sigma. Growth. Conversion-rate optimization. Design thinking, in its most reductive form, became a hunt for anything that slowed the user down. The companies that got smoothest fastest won, and they deserved to. AI has now made friction removal nearly free. Whatever obstacle is left in a workflow, there is a tool that will dissolve it this quarter. The drafting step, the research step, the first-pass review, the scheduling back-and-forth, the synthesis of twelve documents into one. The reconciliation, the summary, the first draft of nearly anything. Gone, or going. And that is the trap. When removing friction costs almost nothing, the temptation is to remove all of it. But not all friction is waste. Some of it was the only thing developing your people. Some of it was the only thing keeping a human close enough to the work to notice when something was about to go wrong. Strip that out along with the rest, and you get an organization that runs beautifully right up until the moment it needs judgment it no longer has. The discipline leaders need now is not friction removal. That skill is commoditized; the tools do it for you. The new discipline is friction discernment: the ability to tell the difference between the friction that drains people and the friction that develops them. Remove the first kind without mercy. Design the second kind back in on purpose. Everything else in the new friction follows from getting this one distinction right.
The two kinds of friction
Draining friction is friction that costs effort and returns nothing. The approval that exists because someone got burned in 2014 and no one has revisited it since. The status meeting that could have been a sentence. The reformatting of a report from one template into another. The manual data pull that a script does in a second. The form that asks for information the system already has. This friction does not build skill, protect quality, or surface insight. It just taxes attention and demoralizes the people subjected to it. AI should eat all of it, and you should let it. There is no virtue in preserving busywork, and no one should confuse what follows with nostalgia for it. Developmental friction is different. It costs effort and returns capability. The junior analyst who has to build the model by hand the first ten times, and only then earns the right to have a tool build it, because now they can tell when the tool is wrong. The reviewer is forced to articulate why an output is flawed before they are allowed to reject it. The team that has to argue its way to a decision instead of accepting the first plausible recommendation that appears on the screen. The new hire who sits in on the hard customer call instead of reading the AI summary afterward. This friction is slow. It feels like waste in the quarterly numbers. And it is the entire mechanism by which expertise, judgment, and trust get built. Here is what makes discernment hard, and why it is a discipline rather than a checklist. The two kinds of friction look identical on a process map. Both are steps that slow things down. Both show up in a time-and-motion study as cost. You cannot tell which is which by measuring duration, because duration is not the variable that matters. You can only tell by asking what the friction is producing. A leader who optimizes purely for speed has no way to see the difference, and will remove both with equal enthusiasm.
Why we are getting this wrong right now
The error is not stupidity. It is a structural asymmetry in what leaders can see. Efficiency is legible. It shows up in the dashboard the week after you automate something: fewer hours, lower cost, faster cycle time, a clean line that goes the right direction. You can put it in a board deck. You can attach your name to it. Judgment loss is illegible. It shows up nowhere, for a long time. It hides inside the year-over-year improvement metrics and the reduced headcount and the deliverables that ship faster and look clean, right up until a situation arrives that needs taste, or context, or the ability to know what is not in the data. By then the people who would have caught it have either atrophied the capability or never built it at all. JoAnna Vanderhoef gave this hidden cost a name: capability debt, the widening gap between an organization’s apparent efficiency and its actual adaptive capacity. Like technical debt, it accumulates quietly and charges interest later. Unlike technical debt, most organizations are not even tracking it. They are removing developmental friction at speed, booking the efficiency, and treating the judgment that disappears as if it were free. It is not free. It is borrowed, and the loan comes due on the worst possible day.
The evidence that friction can be load-bearing
This is not a motivational point. It is measurable. In a controlled study presented at the BIG.AI@MIT conference this year, Renee Gosline’s MIT team gave people cognitive tasks with AI assistance. In one condition, the AI made a recommendation and the person accepted or rejected it. In the other, the person first had to articulate their own reasoning, or predict what the AI’s reasoning was, before deciding. That single step took about thirty seconds. It measurably reduced over-reliance on the AI and preserved the person’s own critical thinking. Thirty seconds of deliberate friction kept the human’s judgment intact. Remove it, and the judgment quietly erodes until the day the AI is confidently wrong and no one in the room has kept the muscle to notice. The mechanism behind where this damage concentrates was formalized by a team of researchers at MIT, Yale, and Microsoft led by Mert Demirer. They studied what they call AI chains: sequences of work steps where the automatable steps are contiguous, so a human only has to verify the final output. The economic incentive is to keep extending the chain until the marginal cost of an error overwhelms the saved verification. The jobs that automate fastest are the ones where AI-suitable steps cluster together. Those are also, and this is the part that matters, the jobs where learning loops used to live. The junior who once did the research, drafted the slides, and watched a senior edit them loses three apprenticeship cycles per deliverable when the whole chain collapses into one automated unit. The work still gets done. The person stops getting made. So the friction you are tempted to remove fastest, the long contiguous chain, is frequently the exact friction that was developing your bench. Efficiency and capability erosion are not opposing forces you can balance. In the most automatable workflows, they are the same move
The test
Before you remove a piece of friction, run it through three questions. They take a minute, and they are the discipline in practice. First: what is this friction producing? If the honest answer is nothing, it is draining friction. Remove it without hesitation. If the answer is a skill, a judgment, a relationship, or a moment where someone learns to catch what a system would miss, you are looking at developmental friction, and removal has a hidden cost you need to price. Second: who was getting developed here, and where will they get developed instead? Most automation quietly deletes an apprenticeship without anyone deciding to. If you cannot name where the replacement reps come from, you are not saving time. You are borrowing capability from your future bench, and the interest rate is high. Third: what happens on a bad day? Efficiency holds until something breaks, and then recovery runs on the slack and the judgment you preserved, not the slack and judgment you optimized away. If removing this friction means no human is left who could step in when the system is confidently wrong, the friction was load-bearing, and you are about to knock out a wall. Run a concrete case through it. A team proposes to fully automate the first draft of every client proposal. Question one: what is the drafting producing? Not just a document. It is where account managers learn the client’s business well enough to defend the recommendation in the room. Question two: if the AI drafts them all, where do new account managers build that fluency? No one has an answer. Question three: when a client pushes back hard in a meeting, who has internalized the reasoning well enough to respond? The honest run-through does not say “never automate this.” It says automate the formatting and the boilerplate, and keep new managers writing the core argument by hand until they have earned the shortcut. That is friction discernment producing a different, better decision than “remove it” or “keep it.”
The moves
Discernment becomes real when it changes what leaders actually do. Three moves follow directly. Stop automating contiguous chains to the end without asking what skill the chain was building. The most automatable workflows are exactly where capability debt compounds fastest, because they are where whole apprenticeships used to live. Automate them deliberately, and keep a human in the loop where the learning was, not only where the legal liability is. The liability checkpoint protects the company this quarter. The learning checkpoint protects it in five years. Start designing developmental friction on purpose. Route a deliberate fraction of automatable work to humans anyway, so the capability stays alive. Require a thirty-second reasoning step before anyone accepts an AI output on a decision that matters. Run novelty drills, where work that could be automated is occasionally done by hand to keep the skill warm. Sample AI outputs not for quality assurance but for drift. Bring in someone who has not been close to a pipeline to ask whether it is still doing the right thing. None of these are productivity moves. All of them are capability moves, and the point is not to make the system slower. The point is to keep it teachable. Change what you celebrate. When a team automates forty percent of someone’s job, the reflex is to bank the savings and move on. The better move, which we have watched work, is to make the freed capacity a deliberate conversation: what harder, more developmental, more human work does this person now get to do? Organizations that celebrate only efficiency teach their people that the goal is to automate themselves toward the exit. Organizations that celebrate the redeployment teach them that AI is how they grow into more valuable work. Friction discernment is not anti-efficiency. It is efficiency pointed at the right target.
Why this is the leadership job now
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 mostly done, and the leaders still doing only that are optimizing a bottleneck that has already moved. The new job is friction discernment, and it cannot be delegated to a tool, because it is precisely the judgment about which judgments to keep. It is the one decision the AI cannot make for you, because the AI’s entire bias is toward removing friction, and the question in front of you is when not to. The organizations that get this right will look slower for a few quarters and less impressive in the efficiency reports. They will also still have, when the situation changes, the people who can do the work the model cannot. The organizations that remove every obstacle they can afford will discover, on the worst possible day, that they removed the ones holding the building up. Stop removing every obstacle. Learn to tell the difference. Remove the friction that drains your people. Design the friction that develops them. That is the discipline, and everything else in the new friction follows from it.
