You’ve spent fifteen years building the intuition that got you to the top. It worked. But in boardrooms today, intuition is increasingly asked to show its sources. AI hasn’t replaced executive judgment — it has raised the bar on what justifies it.
In that quiet shift sits a new strategic competence: executive research skills. They are not academic credentials in waiting. They are the specific, learnable capabilities that turn experience into defensible reasoning — the ability to formulate a verifiable question, read evidence critically, and defend a conclusion in front of peers without falling back on seniority.
The differentiator in 2026 is no longer how quickly a leader can decide. It is how rigorously they can interrogate what they decide on.
The debate on upskilling has become a corporate ritual. Reports, summits, dashboards — all turned toward the operational workforce. Almost none turned toward the people running it.
According to LinkedIn’s 2025 Workplace Learning Report, 49% of learning and development professionals say their executives are concerned that employees lack the skills to execute the business strategy. The concern is sincere. But it is always directed downward. Whether senior leaders themselves have the skills to formulate that strategy in the first place is rarely raised in the same conversation.
Deloitte’s 2025 Global Human Capital Trends echoes the paradox. While 85% of leaders say it is critical to build the organization’s ability to adapt at the pace required today, only 7% say they are actually leading on it. The gap between strategic intent and strategic execution is wide. At the top of every organization, that gap carries a name rarely spoken aloud: the cognitive capabilities required to keep pace with the systems being deployed underneath.
The taboo at the C-suite is not « I need to learn new tools. » It is « the way I think may not be enough anymore. » That sentence is rarely said. It is even less often acted on. And yet, in a market where every operational layer is being augmented, the executive layer is the one left to upgrade itself in silence.
Executive intuition was built for a specific kind of world. Stable markets. Predictable cycles. Slow-moving information. In that world, fifteen years of pattern recognition was the closest thing to a superpower.
That world is gone.
The cycle of strategic relevance has shortened dramatically. The signal-to-noise ratio of available information has inverted: AI now produces more data, more correlations, and more apparent insights than any executive can absorb in a quarter. Intuition without method does not adapt to that environment. It defends against it.
Recent research published in Harvard Business Review found that executives who used generative AI to make forecasts produced significantly worse predictions than those who did not — because the tools amplified, rather than challenged, their existing assumptions. A separate HBR analysis on cognitive bias in AI-augmented decision-making describes the same dynamic: users with confirmation bias selectively accept AI outputs that align with their preconceptions, building an echo chamber in which bad assumptions go unchallenged.
The point is not that intuition is wrong. It is that intuition is now a precondition, not a conclusion. Experience tells a leader where to look. Research tells them whether they saw what they thought they saw.
In the AI era, the executives who treat their judgment as defendable rather than self-evident are the ones who keep their seat at the table.
The phrase « research skills » carries baggage. It evokes labs, journals, peer reviews, and 400-page methodology chapters. For an executive, that picture is misleading — and often paralyzing. Executive research skills are not academic credentials in waiting. They are a cognitive toolkit, transferable to any strategic role.
The first skill is the ability to turn a messy business problem into a verifiable question. Not « should we expand into Southeast Asia? » — but « what would have to be true for Southeast Asian expansion to deliver above-cost-of-capital returns within 36 months? » The first version invites opinions. The second invites evidence. The shift between them is what every strategic conversation in 2026 increasingly turns on.
The second skill is the rigor of the reasoning chain — not the volume of the slides. It means reading a study critically, recognizing when a correlation is being mistaken for causation, interpreting a data point without over-interpreting it, and defending a conclusion in front of peers without retreating into seniority.
These are not academic indulgences. They are the operational skills of every executive who needs to convince a board, an investor, or a regulator that a strategic decision was not improvised. They are also, increasingly, the skills boards themselves screen for when filling non-executive seats.
AI executes. It accelerates. It synthesizes. What it does not do — and what no current architecture suggests it will do in the near term — is formulate the right question.
In McKinsey’s 2025 State of AI report, 88% of organizations now use AI in at least one business function, up from 78% a year earlier. Adoption is not the issue. Impact is: only about 6% of respondents qualify as « AI high performers » — those whose deployment translates into more than 5% of EBIT attributed to AI. The gap between using AI and extracting value from it is not technological. It is cognitive.
In ISC Paris’s Transformation Compass podcast, Jean-Christophe Lessie — Partner at Boston Consulting Group, former CIO of Veolia, and ISC Paris alumnus — puts the same diagnosis in plainer terms: « 70% of companies are falling short on their objectives in terms of digital transformation. Most of the time it’s because of organizations or human factors. » His 10-20-70 rule captures the asymmetry he has seen in the field: roughly 10% of the effort goes into the algorithm, 20% into technology and data, and the remaining 70% into transforming people, processes, and the organization.
The McKinsey data identifies what separates the two groups at the leadership level. AI high performers are roughly three times more likely than their peers to have senior leaders who actively model AI use, sponsor initiatives through ambiguity, and protect AI budgets during cost-cutting cycles. They are not necessarily more technical. They ask sharper questions of their teams, their data, and the AI itself.
MIT Sloan Management Review made the same observation in its 2025 piece on the future of expertise: the value of a senior professional is shifting from content to context — from being the person with the answer to being the person who knows which question is worth asking. AI can compress strategic decision cycles meaningfully, but only when it is paired with disciplined human inquiry. The acceleration is real. The discernment isn’t automatic.
If the question — « what should I be researching, not just deciding? » — is the one that has been postponed too long, the DBA at ISC Paris is built around it.
A quiet but steady movement is taking shape across European business schools. Experienced executives — often in their forties, with fifteen to twenty years of leadership behind them — are enrolling in doctoral-level programs designed for working leaders. Not to leave their careers. To make those careers defensible at a different altitude.
These programs go by several names: Executive DBA, doctoral residency, structured peer-learning cohort. The format with the highest internal coherence is the Doctorate of Business Administration. A DBA is built around a single principle: research applied to a real business problem from the candidate’s own organization. It is not a thesis written in isolation by a junior academic. It is a 36-month process in which a senior executive learns to investigate one problem with academic rigor — and to apply that rigor to every decision they make afterward.
Once an executive accepts that executive research skills matter, the next question becomes which format actually builds them — and how a DBA differs from the more familiar MBA or PhD routes.
The distinction matters. An MBA optimizes for breadth and speed of decision. A PhD optimizes for theoretical contribution. A DBA optimizes for something different: the structured ability of an experienced leader to interrogate their own field, contribute knowledge to it, and return to their organization with a sharper instrument of thought. The DBA isn’t a diploma. It is an intellectual infrastructure — installed once, used for the rest of a career.
The temptation, at the end of an article like this, is to close on action. To list five things to do this week. To compress the message into a checklist.
That would betray the point.
The most powerful proof of this shift is what happens when executives translate research methods into organizational practice — when the question they have spent months refining becomes the question their team can no longer ignore. That compounding effect is rare, and it is exactly what happens when applied research is brought inside an organization. It does not start with a course or a certificate. It starts with a single, uncomfortable question, asked in private, before any application and before any decision.
The question is this: would the strategic decisions made over the last twelve months hold up under the scrutiny of a research committee?
If the honest answer is « I’m not sure » — that is not a failure. It is the beginning of something. Executive research skills are not a credential to acquire. They are the discipline of asking that question, repeatedly, and refusing to live with the easy answer.
This conversation around AI, executive judgment, and the new geometry of leadership runs further in the ISC Paris podcast on AI and leadership. The January 2027 intake of the ISC Paris DBA is built for executives who have started asking the question above — and who are ready to make the answer the next chapter of their career.
