For most of the last 30 years, leadership capability meant becoming more informed. Today, AI has changed the economics of information and data. Data is now abundant. Attention is scarce. The differentiator is judgment. The best leaders are no longer those who consume the most data. They are the ones who can distil and identify the key signals that actually predict success.
Data is not the decision
One of the defining moments of the 2026 FIFA World Cup came when Cape Verde faced Lionel Messi’s Argentina. A nation of fewer than 600,000 people came within minutes of producing one of football’s greatest upsets. It was one of several David-and-Goliath contests in the tournament. Together, they pointed to a striking trend: the gap between football’s traditional superpowers and emerging nations is narrowing. Talent still matters. Experience still matters. But increasingly, so does data.
Coaches now have access to more data than ever. They can see player load, sprint speed, recovery patterns, pressing intensity, injury risk and opposition trends. They can model formations, compare matchups and test scenarios before a player or team walks onto the pitch.
Yet coach still makes the final decision in context.
Who’s the best fit for the way we want to play this match? Who gives us the strongest team, not just the strongest individual statistics? What trade-offs are we prepared to make between attack, defence, control and risk? Who performs best when pressure rises?
The data matters as It can influence the decision, sharpen the debate and challenge assumptions. But the coach, supported by the people around them, still must make the judgment call.
Leaders face the same challenge.
More data has raised the standard
AI gives leaders access to more data, faster analysis and more options. Market analysis, competitor profiles, customer sentiment, risk assessments, board papers, financial models and strategic options can now be produced at speed.
For many organisations, this is already changing how people work. McKinsey’s latest global AI research points to organisations redesigning workflows as they deploy generative AI and putting senior leaders into critical roles, including AI governance.
The leadership issue now is whether leaders can identify which data should shape the decision.
More data can make leaders feel better informed while drawing their attention to the wrong indicators. It can create the appearance of rigour while slowing decision-making. It can make a dashboard look complete, even when the most important issue does not show up in the data.
When leaders give weight to the wrong data, the consequences are practical: capital goes to the wrong work, risks stay hidden, decisions slow down, and stakeholder confidence can weaken.
The leadership skill is knowing which data deserves attention, what weight to give it, and how it should shape the decision.
Context changes what the data means
Leaders need to ask better questions of the data in front of them. What decision are we trying to make? What is this data helping us see? What is it missing? How much weight should this data carry in the decision?
This is where leadership judgment becomes commercial.
Data does not make its own meaning in a leadership team. Leaders give it meaning by connecting it to strategy, timing, risk, organisational capability and capacity to execute.
A dashboard may show falling costs and suggest efficiency is improving. It may also reveal underinvestment, service pressure or capability risk. Customer sentiment may appear positive, even as a specific segment is already starting to shift. A productivity metric may look strong, while the team delivering it is carrying pressure that will eventually affect quality, service or execution.
The data itself has not changed. What changes is the leader’s ability to interpret it in context, test it against experience, and decide what it means for performance, risk and execution.
AI can accelerate analysis, challenge assumptions, generate options and widen the field of view. The leadership work starts when those options must be tested against strategy, risk, timing, capability and the organisation’s capacity to execute.
Judgment is trained over time
Leadership judgment is built over years. It comes from decisions that worked, decisions that failed, risks that were underestimated, trade-offs that proved more costly than expected, and moments when a leader had to act without perfect data.
Over time, those experiences build decision memory. Leaders learn what to notice, what to question, when to pause, when to challenge and when to decide. AI can support that process, but it cannot replace it.
Leaders know the quality of trust in their executive team. They know which part of the organisation is already stretched. They know which stakeholder may read a decision as a shift in strategy, a loss of confidence or a signal to slow down. They also carry accountability for the consequences.
The practical discipline is to test data through experience, context and judgment.
This is also where coaching can play a valuable role. Just as elite athletes train before pressure tests them, leaders benefit from space to test their thinking before the moment of decision. In coaching, the work is often to sharpen judgment: to examine the data, surface assumptions, pressure-test trade-offs and understand the stakeholder dynamics before a leader has to act.
Three filters for executive judgment
Three filters help leaders test whether data deserves attention.
- Evidence – does this data focus the decision in front of us, or is it distracting attention from the core issue? Executive teams can spend a long time debating data that is accurate but not material. The question is whether it informs the decision that needs to be made.
- Predictive – does this data help us understand what is likely to happen next, or does it only explain what has already happened? Many measures are useful but retrospective. Leaders need indicators that help them anticipate customer behaviour, execution risk, capability and capacity pressure, market movement or strategic drift before the impact becomes visible.
- Commercial and actionable – will this data change what we do, stop, fund, de-risk, challenge or communicate? If it does not affect priorities, resources, risk or execution, it may not deserve leadership attention.
In an executive leadership meeting, these filters move the team away from debating every available metric and towards the data that sharpens priorities, aligns trade-offs and improves the quality of the decision.
Under pressure, leaders often ask for more data because it feels responsible. Sometimes it is, and other times the request for more data delays the harder work: making the call, naming the trade-off, challenging the assumption, or accepting that the decision will never be risk-free.
This is where leadership teams lose velocity.
Constructive challenge helps teams test assumptions. Decision velocity helps teams move at the right pace. In the AI era, judgment helps leaders decide which data deserves attention and how it should influence action.
Better judgment improves execution
The organisations that perform well will not be the ones with the most comprehensive dashboards or the most automated reports. They will be the ones whose leaders use data well without becoming captive to it.
Better judgment improves the pace and quality of decisions. It helps leaders allocate capital with more discipline, reduce execution risk, align teams around the right priorities and move faster when conditions change.
AI is changing how leaders access data. It is changing how quickly options can be generated, tested and compared. It is changing how teams prepare, plan and challenge their thinking. Yet it will not remove the central act of leadership.
A leader still has to decide what matters, understand the strategy and context, make the trade-off, and bear the consequences.
The advantage will go to leaders who can read the data, test it with judgment, and make the call.
Frequently asked questions
Why does leadership judgment matter more in the AI era?
AI gives leaders access to more data, faster analysis and more options. The leadership challenge is knowing which data deserves attention, how to interpret it in context and when to make the call. Better judgment helps leaders make sharper decisions, manage risk and keep teams focused on execution.
Does more data lead to better decision-making?
Not always. More data can improve decision-making when leaders know what to look for and how to apply it. But it can also slow decisions, distract attention or create false confidence if leaders give weight to the wrong indicators.
What data should leaders focus on?
Leaders should focus on data that is relevant, predictive and actionable. The most useful data helps clarify the decision, anticipate what may happen next and shape what the organisation should do, stop, fund, de-risk, challenge or communicate.
How can leaders strengthen judgment under pressure?
Leaders strengthen judgment by testing assumptions, learning from past decisions and creating space to reflect before they act. Coaching can help leaders examine data, pressure-test trade-offs and understand stakeholder dynamics before making high-stakes decisions.
What role does AI play in executive decision-making?
AI can accelerate analysis, generate options and help leaders challenge their thinking. But AI does not understand the full organisational context, the quality of trust in a leadership team or the consequences of a decision. Leaders still need to interpret the data and carry accountability for the call they make.
How does better judgment improve execution?
Better judgment improves the pace and quality of decisions. It helps leaders allocate capital with more discipline, reduce execution risk, align teams around the right priorities and move faster when conditions change.
