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An executive approves a recommendation supported by AI. The analysis looks coherent, the conclusion fits the commercial objective, and the team proceeds.
Then someone asks: “Why did we decide this was the right course of action?”
“The system recommended it” leaves the essential question unanswered. Who examined the assumptions, considered what the analysis might have missed? And who decided that the consequences were acceptable?
For senior executives, AI introduces a practical test of Leadership Capital: the accumulated trust, credibility, influence and organisational confidence that enable their capability and judgement to produce results.
Access to sophisticated analysis does not settle that test. The executive still needs to demonstrate how the analysis informed a decision they are prepared to own.
Leadership Capital becomes visible when the answer is uncertain
Authority allows a leader to approve a course of action. Whether others trust that decision depends on more than the right to make it.
Consider an illustrative commercial decision. An AI-supported analysis recommends reducing service coverage for a customer segment. The projected savings are attractive. However, an account director knows that several customers in that segment influence purchasing decisions elsewhere in the market.
The leadership question is how that information enters the discussion. Does the executive examine whether the analysis captures these relationships? Does the team have permission to question its assumptions? Is there a clear account of the trade-off being accepted?
Leadership Judgment is expressed through what the executive asks, challenges, weighs and ultimately decides. The quality of the presentation cannot substitute for that work.
Emerging research offers a useful prompt
In their September 2026 preprint, Mustafa Akben and Leslie Coyne introduce an AI Leadership Battery organised around observable behaviours. Its abstract describes a framework addressing judgement, learning, adaptation, transparency and accountability in AI-enabled work.
This is emerging measurement research. It provides a useful prompt for reflection; it should not be treated here as proof that a particular leadership behaviour produces better business outcomes. The practical questions below are Mentor EU’s advisory interpretation, not items from the proposed instrument.
Three decisions that deserve the leader’s attention
1. What kind of support is appropriate?
Before considering the output, clarify the task. Is AI helping to generate possibilities, summarise material, compare options or recommend action?
Those uses place different demands on review. A draft agenda and a recommendation affecting employees’ livelihoods should not receive the same level of scrutiny.
The executive should be able to explain why the chosen use is appropriate, what checks are required and where the system’s contribution ends. This is a question of managerial responsibility; specific legal obligations depend on the context.
2. What would make us reconsider?
A persuasive recommendation deserves an explicit test. Ask which assumptions matter most, what relevant information is missing and what evidence would reverse the proposed decision.
Apply that discipline to personal experience as well. A leader’s intuition may identify a weakness in the analysis, but intuition also needs examination. The aim is to make the disagreement investigable.
A useful question is: “What would we need to learn before either of us could be confident?”
3. Who has the authority to intervene?
Assigning someone to review output is insufficient if they cannot delay, escalate or challenge the resulting action.
For a consequential process, establish who checks the recommendation, who can override it and who resolves disagreement. Responsibility should come with enough time, access and authority to be exercised meaningfully.
Where those conditions are absent, individual vigilance cannot compensate for the design of the process.
Transparency should explain the decision
Saying that AI was used is only a starting point. A meaningful explanation describes its contribution and the human work around it.
An executive might explain:
- The contribution: what the system helped the team analyse or produce.
- The checks: which assumptions, sources and implications were examined.
- The judgement: why this option was selected over the alternatives.
- The boundary: what remains uncertain and when the decision will be reviewed.
The appropriate level of detail depends on the audience and the decision. A board, an employee and a customer may need different explanations. Each should be able to understand the reasoning relevant to them, without receiving confidential information they are not entitled to see.
Review a real decision before adopting another framework
Choose one recent decision in which AI played a meaningful role. Reconstruct how it moved from initial question to final action.
Who defined the problem? Who checked the output? What objections emerged? What changed because of those objections? Who could explain the decision now?
Look for evidence: a revised recommendation, a documented assumption, a concern investigated or a decision deliberately postponed. Statements about valuing transparency are less informative than a specific account of what happened.
This exercise can reveal both individual development needs and organisational weaknesses. An executive may need to improve how they invite challenge. The organisation may need clearer escalation routes. Those require different responses.
Stewarding trust as decision-making changes
From Mentor EU’s perspective, Leadership Capital requires deliberate attention whenever the conditions of leadership change. AI creates another setting in which trust, credibility and influence must be earned through conduct.
An executive does not need to claim certainty about every technical detail. They do need to recognise the limits of their understanding, obtain appropriate expertise and explain the judgement behind consequential choices.
The question to carry into the next leadership meeting is straightforward: Could we explain this decision clearly, including the role AI played, and stand behind the reasoning?
If AI is changing how you make decisions, lead people or exercise authority, a confidential Strategic Conversation with Mentor EU offers space to examine the implications for your Leadership Capital.
Would you like to assess your Leadership Capital? Send me an e-mail to: beata.staszkow@mentoreu.com and I will send it over. Ready to discuss your results? Request a Strategic Conversation.
Research reference: Akben, M. and Coyne, L. (2026), Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations. Preprint posted 16 September 2026. The research description above is limited to the available abstract.
