Executive Leadership in the Age of AI
Executive decision-making in the age of AI depends less on technical expertise than on something distinctly human.
University Professor, Claremont Graduate University
EY just put a price on human judgment. At the end of August, the global professional services firm committed $100 million to reward U.S. employees who develop what it calls “future-focused” human skills — business acumen, judgment, adaptability, and the ability to use technology to innovate and improve client service. Individual employees and teams whose work makes a material difference to the firm can earn awards of up to $25,000, according to Fortune.
Why would one of the world’s largest professional services firms spend $100 million on human capability at exactly the moment AI is becoming extraordinarily good at the technical work those people were hired to do?
Because AI changes what executive leadership means.
When Answers Are Cheap, Questions Become Valuable
For decades, executive leadership was associated with experience. Executives accumulated knowledge, recognized patterns, and learned which decisions produced results.
AI changes that equation. Analysis that once required a team of people and weeks of work can now be produced in minutes. AI will summarize a market, profile your competitors, model scenarios, write the code, and argue against its own conclusions if you ask it to. Information is abundant. The scarce resource is no longer access to information, but knowing what to do with it.
The concern extends well beyond EY. When KPMG surveyed 906 of its summer interns in July — the most AI-fluent group ever to enter the workforce — 76% said career success will require both strong human skills and the ability to direct AI effectively. Another 43% named over-reliance on the technology and the erosion of their own critical thinking as their leading worry.
When asked what had taught them the most, 57% pointed to direct coaching from another person. Three percent chose AI-assisted learning, which finished last.
That is the executive challenge of the AI era, and the people now entering the workforce can already see it.
From Knowing the Answer to Knowing Why
AI is extraordinarily good at producing plausible answers, which creates a new problem for leaders: plausible is not the same as correct.
I learned that lesson long before anyone worried about chatbots. Early in my career, I wanted to know how much people trusted one another, so I did what most researchers did then and asked them. The answers were useless.
People lie — not maliciously, but because we ask questions they cannot honestly answer. How motivated are you? How much do you trust your manager? Why did you really buy that? They hand back the most plausible story they can assemble on the spot, and we build strategies on top of it.
That is why my colleagues and I ended up drawing blood from people’s arms immediately before and after they made decisions. It was the only way to find out what was actually happening in their brains.
Executives now face the same problem in a new form. Distinguishing evidence from assertion, correlation from causation, and an attractive explanation from one that survives empirical scrutiny — these are research skills, and they have become executive skills.
Leaders who can formulate a precise question, identify the evidence required to answer it, test competing explanations, and revise a decision when the data demand it hold an enormous advantage in an AI-rich environment. The effective executive today does not simply consume AI-generated analysis. The executive interrogates it.
AI Raises the Value of Human Judgment
Better AI increases the importance of human judgment, because each is good at different things. AI can generate 20 strategies before breakfast. It cannot determine which problem deserves the organization’s attention in the first place.
That distinction matters more than it sounds. Organizations rarely fail because nobody can produce another spreadsheet, presentation, or strategic framework. They fail because leaders pursue the wrong problem, rely on weak assumptions, misread competitors, or keep executing a strategy after the evidence shows it isn’t working.
Judgment is also far less uniform than we like to believe. In a study my lab published in 2024, we brought 75 people into the laboratory, measured nine physiologic responses and 15 psychological factors while they made seven different kinds of decisions, and looked for the profile of a good decision maker.
We discovered the best decisions were context dependent. For example, higher cognitive ability made people considerably more patient but considerably less cooperative. The analysis also showed that men with high testosterone were less altruistic, generous, and cooperative, and also took greater risks to gain a reward.
You might have guessed some of this, but none of these 75 people would have had these insights about themselves because the process of making decisions is hidden from conscious awareness. It is experience across context that translates information into wisdom.
The critical executive competencies therefore move upstream: framing problems, asking consequential questions, designing ways to test assumptions, evaluating evidence, and translating discoveries into action. AI accelerates every one of those activities without eliminating the need for them.
The Executive as Researcher
This is why doctoral-level training is increasingly relevant to executives.
Research begins with uncertainty. Instead of hiding uncertainty behind assumptions and the status quo, researchers turn it into questions they can investigate.
What is actually causing customer churn?
Why does one team consistently outperform another?
Which elements of our culture drive innovation?
Does a new technology genuinely improve productivity, or move work from one part of the organization to another?
What happens to our business model when AI makes expertise dramatically cheaper?
These are not academic questions. They are management questions with enormous financial consequences, and they share a feature worth noticing: no AI system can answer a single one of them, because the data does not exist yet. A language model has read everything ever written about customer churn. It has never met your customers.
Rigorous research provides a disciplined method for moving from “I think” to “the evidence shows.” It also teaches restraint.
A few years ago, my lab ran a study at a police department designed to raise organizational trust and thereby department performance. The intervention raised trust by 8% and job retention improved by 10.5%, a modest but useful finding when officers are leaving policing at high rates. We published all of it exactly that way, because the number is the number.
Knowing the difference between a statistically significant effect and a meaningful one is what a research training gives you, and it is precisely the judgment that fluent, confident AI output tends to erode.
Drucker Was Asking the Right Questions
Peter Drucker built his life’s work around five questions. What is our mission? Who is our customer? What does the customer value? What are our results? What is our plan?
He did not answer them for anyone. He sent executives to find out, then made them do it again when conditions changed. AI does not make those questions obsolete; it makes them more urgent. When technology changes what organizations can produce, how quickly they can produce it, and what expertise costs, executives must repeatedly reconsider the assumptions underlying their organizations. The ability to investigate those assumptions systematically becomes a competitive advantage.
That’s why we created the Executive PhD program at the Drucker School of Management. It is designed for accomplished executives who want to become rigorous practitioner-scholars — leaders capable of applying advanced research methods to problems that matter both inside organizations and to society. Enrollees bring twelve to thirty years of leadership experience: chief executives, chief strategy officers, Air Force colonels, founders, and hospital and technology leaders.
Participants do not leave their executive experience behind. They extend that experience to inform advanced analyses. Over three years, they learn to turn observations into research questions, intuition into hypotheses, organizational data into evidence, and evidence into knowledge that can improve decisions, and then apply it to a real problem inside their organizations.
The New Executive Advantage
EY’s $100 million investment is an early signal of where executive value is heading. AI will keep improving, and executives will have access to increasingly powerful systems that can search, analyze, predict, write, code, and recommend actions. Knowing how to use AI will be necessary. It will not be sufficient.
The competitive advantage will belong to leaders who know what to ask, what to measure, what evidence to trust, what conclusions the evidence supports, and when the evidence requires them to change course.
Those are the competencies of a researcher. They are rapidly becoming the competencies of an exceptional executive, and the Drucker School Executive PhD is designed to develop them.
Become the Executive Who Asks the Right Questions
This is not a detour into academia. The Executive PhD is built for senior leaders who want to take a problem they already own, study it with real rigor, and produce something their industry does not yet know — work that advances the field, not just the next quarter.
- Explore the Executive PhD at the Drucker School of Management.
- Request information, or contact Michael Mirabella at mirabella@cgu.edu to talk through whether the program fits the problem you want to work on.
About the Author
Paul J. Zak is a University Professor at Claremont Graduate University and founding director of the Center for Neuroeconomics Studies, where his research applies neuroscience to trust, decision-making, and organizational performance. He has published more than 200 papers and is ranked among the most-cited scientists.
His two decades of research have taken him from the Pentagon to Fortune 50 boardrooms to the rainforest of Papua New Guinea, and he helped start the interdisciplinary fields of neuroeconomics, neuromanagement, and neuromarketing. He is the author of Trust Factor: The Science of Creating High-Performance Companies, Immersion: The Science of the Extraordinary and the Source of Happiness, and The Little Book of Happiness: A Scientific Approach to Living Better, and he teaches in the Executive PhD program at the Peter F. Drucker and Masatoshi Ito Graduate School of Management.