Executives Are Being Asked to Show Their AI Work
Built Evidence: Executive credibility increasingly depends on showing what you built, deployed, improved, and learned through AI.
Adoption Matters: AI fluency alone cannot deliver transformation; leaders must earn stakeholder trust and change how organizations work.
Judgment Test: Repeated AI use can sharpen judgment, but unchecked trust in outputs may weaken critical decision-making.
Hiring Gap: Companies struggle to assess leaders who combine hands-on AI capability with experience leading cross-functional change.
System Update: Executive hiring still favors old signals, creating pressure to redesign promotion, succession, and interview practices.
Ask a senior leader to walk you through a problem they solved with AI and pay attention to the first 30 seconds.
JC Christian of executive search firm Christian & Timbers says he uses those conversations as a barometer. What first interests him isn't the solution. It's if the person can clearly explain the problem.
“Most people can't even accurately describe the problem itself, let alone get anywhere near a solution,” he said.
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When a candidate can share real depth on both the problem and what they built, that tells him something. If they can't, he treats it as a red flag.
The interesting part isn't the interview technique. Search professionals have been asking candidates for specific examples forever. It's what that evidence has started to replace.
For much of the modern executive era, credibility at the top essentially came from accumulated pattern recognition. You had sat through enough cycles to know roughly how this one might break, and your value was partly the interpretation nobody else in the room could offer. Twenty years of operating history bought you the benefit of the doubt on the next 12 months.
That model worked for good reason. Judgment under uncertainty is hard to teach and hard to fake over a long horizon.
It also depended partly on scarcity.
Brandon Sammut, chief people and AI transformation officer at Zapier, described what that looked like at a previous company. A couple of engineers were the only people in the organization who understood one part of the product architecture.
“As a result, they were effectively untouchable,” he said. “That's not super healthy or high functioning for anyone in that equation.”
Scarcity protected those engineers and trapped them. Nobody could afford to have them working on anything else. Sammut's broader point is that AI is accelerating the democratization of knowledge that the internet started.
The executive version was never so mechanical, but it rhymed. Information, interpretation and institutional memory were scarce. Scarcity helped set the price.
AI doesn't eliminate the value of experience. It changes what experience has to prove.
The Evidence Has to Be Something You Built
Christian draws a line between leaders who are AI-enabled and leaders who are AI-native, and he's unsentimental about how few have crossed it.
“I talk to people who are like, we just started using Copilot and now we're AI native,” he said. “And it's like, no, unfortunately you're not. You're at the very beginning of the AI-enabled adoption curve.”
He wants evidence of deployment: models placed into workflows, agents pointed at business processes and leaders who got close enough to the work to understand what changed.
The real leaders are going to get their hands dirty using keyboards. It’s almost impossible to be AI native and be an armchair anything.
He reads companies the same way, pointing out that public companies are eager to talk up AI returns, and he assumes some of those claims run hot. What interests him is silence.
“If we're not bragging at all, that's typically a red flag,” he said. “If you're the AI lead of deployment at a company and they don't have any AI ROI, it's pretty bleak for being able to claim your role there was a success.”
The shift he describes is that credibility becomes more demonstrable. “I led through three transformations” still matters. Increasingly, the follow-up is: What did you actually change in this one?
Where Demand Concentrates
That does not mean the market has simply decided technical fluency beats experience.
Christian says clients keep bringing him some version of the same problem.
“A lot of leaders say, ‘Hey, I have a 20-something-year-old whiz kid who lacks gravitas and transformation experience completely, but has built all this cool stuff and knows AI best.’ And I'm considering, do I promote them and make them my VP of AI adoption, or do I look externally?”
The technical barrier, in his telling, is often lower than executives imagine. Many AI transformation roles aren't engineering jobs, and curious people who stay close to the technology can learn the tools.
Then he complicates the story.
The same considerations that existed 20, 10, 50 years ago are in play today.
Transformation experience and the ability to work across process, technology and people still matter because building is only half the problem.
“Adoption is actually the biggest problem,” Christian said. “The technology is an easier problem. It's more solvable.”
Without stakeholder buy-in, he added, “adoption's going to be a mess. You're going to get zero value, maybe even negative value.”
So the new premium isn't really on youth, or even AI fluency. It's the combination of enough proximity to the technology to build and enough organizational credibility to get other people to change.
Christian says that is why demand concentrates between two archetypes:
- The experienced transformation leader who lacks hands-on AI fluency
- The younger builder who lacks experience moving an organization.
Companies that put the second directly into cross-functional leadership roles often run into a trust problem. Christian sees them fitting more naturally in chief of staff or second-in-command roles where they can pair with a more seasoned executive.
Two words are carrying enormous weight in that argument, and they deserve scrutiny.
“Gravitas” and “executive presence” have a long history of functioning as proxies for looking and sounding like the people already in the job. Any competency assessed mostly by feel risks reproducing the preferences of the people in the room where assessment happens.
But Christian's underlying requirement is more concrete: Can this person get a skeptical finance leader, operations leader or business unit head to change how their team works?
That is not polish. It’s influence.
And it suggests experience hasn't been devalued so much as forced to compete with a new form of evidence.
Judgment Can Compound or Decay Faster
Tony Castellanos, executive vice president of people at Nextdoor, offers the most useful account of how one part of that new credential gets built.
It isn't through another executive course.
It’s repetition. It’s at-bats. There’s not really a course and training that you’re gonna get for it. It’s the accumulation of the wins and the gut-punch bruises when you get something wrong.
AI can dramatically increase the number of those at-bats. A problem someone might have encountered a dozen times over several years can now be pressure-tested dozens of ways in an afternoon. That should create opportunities to develop judgment faster.
But there is an obvious complication. More output also means more opportunities not to look closely.
“I've tried to read most of the docs that have come my way, but honestly, they're just flowing in so fast,” Castellanos said.
Then there is trust.
Work closely with a model for six months, validate every output and exercise real judgment, and eventually a predictable human behavior begins to appear: the checking tapers off.
“You have a new hire, you don't automatically trust everything that they do, but over time they deliver, they get things right, and you more and more trust them, just like you're going to more and more trust an LLM,” Castellanos said.
“As they're sending things to you to proofread, to audit, to check, you're probably doing less and less and less of that over time.”
That creates an odd possibility. AI can increase the repetitions through which judgment is formed while simultaneously creating conditions in which that judgment gets exercised less often.
Castellanos' answer is not to demand perpetual human checking of everything. He imagines introducing a “third skeptic” — potentially another AI system — whose job is to challenge the working relationship between the human and the model they have come to trust.
Two systems disagree. The human has to decide why.
“What's the right call? What's the right thing for me? What's the right thing for my organization?”
The point is not that the skeptic knows better. It’s that disagreement forces judgment back into the loop.
That matters because AI fluency itself will eventually become less differentiating. Christian predicts that what looks unusual in executive candidates today will become baseline expectation.
Today's evidence that someone is unusually AI-capable becomes tomorrow's version of financial literacy.
The durable credential is not simply using the technology. It is being able to decide when it is right, when it is wrong, what matters and what to do next.
The Selection Machinery is Behind
This creates a problem for executive hiring and development.
The machinery that produces senior executives — succession plans, promotion criteria, leadership programs, P&L assignments and long apprenticeships inside a function — was built largely around accumulated experience.
Much of the machinery used to select them still is.
Christian said many executive interview processes have not changed substantially. Companies still lean heavily on familiar behavioral questions, formulas for extracting examples and, ultimately, a collection of gut checks from interviewers.
He gets asked what new questions companies they should be asking because the qualities they now want can be difficult to identify..
If executive credibility is being repriced around a combination of demonstrated agency, judgment and the ability to move people through change, then simply adding “AI fluency” to an old competency model won't do much.
Sammut's list of what becomes more valuable is revealing precisely because so little of it is new: judgment, taste, trustworthiness, reliability, accountability and the ability to influence without authority. AI makes those capabilities more important, not less.
And Castellanos arrives at a similar place from another direction. He expects expertise to remain important while more people operate across broader problem spaces, using AI to reach knowledge that once stayed locked inside a function.
That is the repricing. Experience still has value. So does technical fluency, but neither gets to stand in for proof anymore.
The more knowledge becomes accessible, the harder it becomes to build seniority around simply possessing it. What rises in value instead is the ability to do something with it. Can you identify the problem, build against it, know whether the result is any good and persuade other people to move.
For decades, the executive pipeline was tuned to find people who had seen enough to deserve the benefit of the doubt.
Increasingly, the doubt is in the interview.
