What Gets Harder When AI Gets Better
In 1903 two teams were racing to achieve powered flight. One had a Congressional grant and the most powerful aircraft engine ever built. The other had a bicycle shop in Dayton, Ohio.
Samuel Langley, Secretary of the Smithsonian Institution, was the most credentialed aeronautical scientist of his era. A Congressional grant of $51,000 (roughly $1.9 million in today's dollars) funded his work on the Aerodrome, powered by a 125-pound, 53-horsepower gasoline engine designed with his assistant Charles Manly.
The Aerodrome was launched from a catapult atop a houseboat on the Potomac in October 1903. With news reporters watching, it promptly collapsed on itself and fell into the water. A second attempt on December 8 produced the same result. As Smithsonian Magazine recounts, the press was unsparing. The New York Times ran "Flying Machine Fiasco." The Washington Post called the craft "a total and admitted failure."
Less than ten days later, Wilbur and Orville Wright achieved sustained powered flight at Kitty Hawk. They had built the Flyer for about $1,000 (roughly $38,000 in today's dollars), funded from their bicycle shop in Dayton. They constructed a homemade wind tunnel and asked a different question. The unsolved problem was how a pilot would balance an aircraft against unstable air. Their three-axis control system is still the foundation of every aircraft built since.
Langley solved the wrong problem with extraordinary precision. The Wrights had spent years watching how birds held themselves steady in unstable air, and imagined a flying machine that could do the same.
AI is the Langley engine of this moment. It will optimize, model, and forecast at a power no leadership team has had before. It cannot tell anyone when the engine is pointed at the wrong problem.
Sixty percent of US CEOs say changing the business model is their top priority for boosting profitability this year (Conference Board). PwC's latest CEO survey adds two more numbers to the picture. Just thirty percent of CEOs are confident about revenue growth, the lowest reading in five years, and only twelve percent say AI has delivered both cost and revenue benefits. Pressure is up, conviction is down, and the space between them is what senior leaders are now being asked to close.
AI was supposed to close it. Three years after it moved to the top of every leadership agenda, the senior leaders who see what's getting harder are keeping it to themselves, and the conversation that could change the trajectory isn't happening.
What gets harder when AI gets better.
AI is consolidating the work senior leaders have excelled at for decades. What remains is the harder work of picturing what isn't there yet and deciding what deserves to exist. Most are out of practice.
The Reinvention Problem Is Not a Strategy Problem
Every offsite is a reinvention offsite now. The board has been explicit. Original strategy, original positioning, something a competitor hasn't considered. Several rounds in, with AI threaded into every transformation memo, the plan has become indistinguishable from what every competitor is producing. Sharper language, updated metrics, same trajectory.
The pattern shows up anywhere a senior leader is being asked for a move no one has made yet. It's the sixth version of the investor pitch the audience has already heard, the annual letter that could have been written by three peer foundations, the product roadmap that mirrors every competitor's, the marketing that reads like every other CMO's. Same outcome, different rooms.
CEOs know the model has to change but don't believe they're moving fast enough. PwC research finds the top third of companies that reinvented their business models outperformed industry peers by 71 percentage points on a combined measure of profit margin and revenue growth. The advantage isn't from better execution. The companies that fall behind tend to be the most disciplined of all, running playbooks with precision against a picture that no longer matches the terrain.
Two more signals never surface. The senior leaders the institution is counting on for the next move have started pulling back. They're still in their seats, but they've disengaged in a way that engagement surveys won't catch for six months. The one accountable for what comes next is sitting with an unsettled feeling. The instinct is to dismiss it. Not one of those offsites produced a move a competitor couldn't replicate.
The feeling is signal, running ahead of what the surveys and the market will eventually confirm.
AI is consolidating the cognitive work senior leaders have been rewarded for throughout their careers — pattern recognition, optimization, risk modeling, scenario planning, and forecasting. Machine learning now matches or exceeds human performance on most of it, and the consolidation is accelerating.
What's left is what AI cannot do. That work belongs to imagination as a competence. Sensing what's missing, choosing what to commit to, reading what the data can't show, recognizing whether the question on the table is even worth asking.
That cognition has been atrophying in senior leaders. The work AI now handles is the work the institution trained them to do, promoted them for doing well, and reinforced at every level of seniority.
AI is consolidating what the institution rewarded. The work that's left was never cultivated with the same intent.
The Atrophy Is the Diagnosis
This is not a willpower problem. It's how expertise works.
Erik Dane's 2010 paper in Academy of Management Review makes the mechanism specific. As people develop deep expertise in a domain, their thinking gets locked inside the schemas of that domain. Dane calls it cognitive entrenchment. Domain mastery produces inflexibility in problem-solving, adaptation, and creative idea generation. The deep specialization that makes a leader excellent at one way of thinking entrenches the schemas for that approach, while the ones not in use grow less accessible. The competence that built the authority becomes the wall against everything else.
What makes the entrenched expert's blind spot distinct is the confidence behind it. The novice gets stuck because nothing looks familiar. The entrenched expert gets stuck because everything does. A novel situation activates the schema that solved the last one, and the expert responds before examining the question.
This holds across fields. It holds for the partner who has won the same kind of case for fifteen years. It holds for the operator who has scaled the same playbook through three companies. It holds for the founder whose pattern recognition built the company and now blocks them from seeing what comes next.
Imagination is the competence this era demands. It operates in three modes. Navigational plans the path forward, optimizing, sequencing, and executing. Protective scans for threat and defends what's working. Generative pictures what doesn't exist yet and decides what deserves to exist. Same engine, different directions.
The first two modes work inside an accepted frame. Navigational asks how to get there. Protective asks what could go wrong on the way. Both presume the destination is correct. Generative interrogates the destination itself. It's the mode that tests whether the right question is on the table.
AI accelerates Navigational and supports Protective. Generative is beyond its reach.
Generative fades first under sustained pressure. The institutions that shaped most senior leaders rewarded the other two for years, anchored to quarterly cadences and risk dashboards, promoting careers built on optimization and never being wrong. The leader can still execute and still defend, but Generative has been crowded out by everything the other two demanded.
The muscle hasn't disappeared. Atrophy describes what happens when a competence stops being used. It rebuilds with practice, and the practice doesn't happen in the same rooms that caused the atrophy.
Recognition Before Practice
What rebuilds the muscle is small, deliberate practice on unfamiliar ground.
Recognition comes first. The leader who can name which mode is operating can change it. The one who only feels stuck can't. Most leadership failures right now come from running the wrong mode. The moment needed Generative, the room defaulted to Navigational, and no one had the framework to name what was happening.
The work after that is unglamorous. It looks like protected time on the calendar that isn't optimizing, defending, or executing. It looks like a different set of questions in the strategy session, the discipline of catching the dismissed idea before it leaves the room, and practice with other senior leaders sharpening the same question against different contexts. The muscle rebuilds faster when the thinking happens outside your own industry, your own habits, and your own defenses.
This is the work senior leadership now demands. None of it will show up in the metrics that get tracked.
Vision Isn't a Trait
The directive coming down is the same everywhere. Be more visionary. Bring more vision to the strategy. Help us see around the corners.
The ask is clear, but the muscle isn't there. Senior leaders take the note, sit down to do something with it, and find that nothing moves. Trying harder won't help. Vision is what imagination yields in Generative mode, the output of a competence that hasn't had reps.
What gets harder when AI gets better is the work that was always human, and was always the first to fade under pressure. Senior leaders who recognize this and rebuild the muscle will define what comes next. The ones who don't will keep optimizing what they're already doing — faster, against the same picture as everyone else.
The work that was always human is what imagination does. That's the work that's still yours.
The Executive Imagination Lab on June 9 brings twelve senior leaders into this question for ninety minutes — the same problem, worked from twelve vantage points, sharpened against each other.