AI vervangt leiders niet. Het legt ze bloot.

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AI is not primarily a technology challenge; it is a leadership exposure mechanism. By stripping away the delays leaders used to absorb uncertainty, questioning authority that was assumed rather than enacted, and revealing narratives that replaced explicit judgment, AI makes leadership behavior, decision quality, and accountability immediately visible. As a result, AI does not replace leaders, it exposes where judgment is avoided, ownership is unclear, and authority relies on structure rather than clarity. Organizations struggle with AI not because systems fail, but because leadership models were designed for slower, more stable conditions. AI strengthens leadership only where decisions are owned, judgment is explicit, and leaders are willing to be seen deciding.

Why I keep returning to AI and leadership

My interest in AI and leadership did not start with technology (maybe except for in the very beginning). It started with the realization that the patterns I have seen repeatedly over the past decades, now become exposed clearly. I mean the patterns I observed while working with senior leaders in operationally complex environments: when pressure increases, leadership systems tend to reveal their weakest assumptions. E.g. decisions slow down, accountability blurs and authority relies more on position than on clarity. And when things went bad, they has a narrative to get away with it.

For a long time, these weaknesses remained manageable. Time absorbed uncertainty. Hierarchy stabilized authority. Narrative explained outcomes after the fact.

AI is changing this. What struck me early was not what AI could automate, but what it made visible. Improvements became visible and achievable in days. Decisions that once took weeks were suddenly expected in days. Judgments that were previously implicit were now questioned. Leadership behaviors that had gone largely unnoticed became observable, sometimes uncomfortably so.

In conversations with executives, I began to hear the same refrain: “The technology works, but something feels off.” That “something” was rarely about models, data, or tools. It was about leadership under new conditions: speed, transparency, and comparability that existing leadership practices were never designed for. The exposure became overwhelming.

This article is the result of studying that disconnect. It is written from the conviction that AI is not primarily a digital transformation challenge, nor a workforce issue, nor a tooling decision. It is a leadership exposure mechanism. It accelerates reality and removes buffers that leaders have relied on, often unconsciously, for decades.

This shift matters because it forces leaders to lead differently, not by knowing more about AI, but by clearly owning decisions, explaining their judgment, and taking responsibility instead of relying on time or hierarchy. That is the lens through which this article should be read.

In my interactions, mainly during executive coaching, most executives still start with the same question: What can AI do for us? That framing is understandable, but incomplete. And increasingly, it is the wrong starting point.

AI’s most profound impact on organizations is not technological. It is behavioral. AI changes the conditions under which leadership operates, making decision quality, judgment, and accountability visible in ways leaders are often unprepared for.

AI does not replace leaders. It exposes the ones who were already struggling. So, here we go…

How AI changes the conditions of leadership

For decades, leadership operated with three powerful buffers. First, tijd. Decisions could mature. Uncertainty could be absorbed. Waiting was often indistinguishable from prudence. Second, hiërarchie. Authority flowed from position. Seniority often substituted for explanation. Third, narrative. Leaders could explain outcomes after the fact, shaping meaning once results were known.

AI collapses all three, not by intent, but by consequence. By stripping away the delays leaders used to absorb uncertainty, questioning authority that was assumed rather than enacted, and revealing narratives that replaced explicit judgment, AI alters what leadership looks like in practice. Decisions are faster, alternatives are visible, and comparisons are unavoidable. The result is not better leadership by default, but greater exposure of leadership behavior.

Leadership was not designed for this environment

Most leadership systems were built for a slower, more stable world. Leaders were promoted for experience, reliability, and the ability to deliver within established structures. Decision-making was often incremental, escalations were accepted, and ambiguity could be managed through process and alignment.

AI disrupts this equilibrium. When insights update continuously and recommendations are generated at scale, delay becomes visible. When teams have access to similar analytical power as their leaders, positional authority weakens. When alternative scenarios are instantly available, explanations must precede decisions, not follow them.

Leadership expectations did not suddenly rise. The environment simply stopped protecting outdated leadership patterns.

What AI actually exposes

AI does not primarily expose incompetence. It exposes immature leadership behaviors that were previously tolerated, or even rewarded. These include:

• delaying decisions until ambiguity disappears
• escalating responsibility instead of owning judgment
• relying on experience without articulating reasoning
• dismissing insights without explanation

Under AI conditions, these behaviors no longer remain internal. They become visible to teams, peers, boards, and markets. The question shifts from “What did we decide?” naar “How did we decide?” That shift is uncomfortable and unavoidable.

A Case to illustrate the point: Zillow Offers

A widely cited example of this exposure dynamic is Zillow’s Zillow Offers program. Zillow used AI-driven pricing models to make instant offers on homes at scale, aiming to outperform traditional real estate processes through speed and predictive accuracy. When housing market conditions shifted in 2021, the model’s assumptions broke down. Zillow continued purchasing homes at prices it could not profitably resell and ultimately shut the program down, taking a write-down of hundreds of millions of dollars.

The failure was not hidden. It was fast, public, and measurable. What made the case notable was not that the AI model was imperfect, most are, but that leadership choices became transparent. The decision to scale automated pricing faster than uncertainty warranted, the absence of effective human override, and the reliance on model output as a proxy for judgment were all visible.

AI did not make those decisions. Leadership did, and could not hide behind narrative or hierarchy once outcomes were traceable.

When judgment is avoided, authority erodes

In many organizations, early AI failures are framed as technical: model accuracy, data quality, or tooling limitations. These explanations are comforting, but often misleading. The more common failure is behavioral. Leaders confronted with AI insights tend to respond in predictable ways:

• deferring responsibility to “what the system says”
• rejecting recommendations without articulating why
• slowing decisions to preserve authority
• delegating AI discussions to technical teams

Each response weakens leadership legitimacy. Authority does not erode because AI is right. It erodes because judgment remains implicit.

This dynamic has surfaced repeatedly, from Amazon’s abandoned AI recruiting tool, where leadership assumptions about neutrality were exposed, to IBM Watson Health, where overpromising collided with governance realities.

In each case, AI did not fail quietly. It made leadership decisions observable.

Who feels the pressure first

The leaders most exposed by AI are rarely the least capable. They are typically those whose roles depend on things like:

• authority derived from position rather than decision clarity
• escalation as a default response to uncertainty
• experience without explicit reasoning
• stability rather than adaptability

These leadership roles functioned well in slower systems. AI removes the conditions that sustained them.

As a result, pressure first appears in middle and senior management layers, particularly in operational, coordination-heavy, and legacy leadership roles. The discomfort is subtle at first: more questions, more follow-ups, more demands for explanation. Authority has not disappeared. But it must now be earned differently.

What happens when leadership does not adapt

When leadership hesitates, organizations do not wait. They adapt informally. Teams experiment with AI tools quietly. Shadow systems emerge. Interpretations multiply. Accountability blurs. Initially, this looks like initiative. Over time, it becomes fragmentation.

Research on organizational sensemaking, including the work of Sensemaking in Organizations, shows that ambiguity without leadership direction does not remain neutral, it proliferates. AI accelerates this effect.

By the time leadership intervenes, authority has already leaked away from formal structures. AI does not create chaos. Leadership avoidance does.

The unavoidable leadership choice

AI forces leadership teams to confront a question they can no longer postpone: Who owns judgment when AI is involved?

Not in governance documents. Not in principles. But in daily practice. Which decisions must remain human? Who is accountable when AI-supported decisions fail? How are disagreements with AI recommendations explained and resolved?

Read here about five examples where AI should not decide: Five leadership decisions AI should never make – salomons.coach

The bottom-line is this: If leaders do not answer these questions explicitly, they will be answered implicitly, by systems, individuals, or politics. Vacuum never stays empty.

When AI strengthens leadership

AI does not inevitably weaken leadership. In some organizations, it strengthens it. The difference lies in conditions, not capabilities. Where AI reinforces leadership, several patterns are present:

• decision ownership remains explicit
• judgment criteria are shared and discussed
• leaders explain waarom, not just wat
• psychological safety allows weak patterns to surface
• authority aligns with accountability

Under these conditions, AI sharpens leadership rather than exposing its absence.

As the authors of Prediction Machines argue, when prediction becomes cheap, the value of judgment rises. Organizations that recognize this treat AI as an input, not a substitute, for leadership.

Waarom dit nu belangrijk is

The central challenge of AI is not whether leaders understand the technology. It is whether leadership systems, designed for slower, more stable environments, can function when speed, transparency, and uncertainty collide. AI removes the hiding places leadership relied on for decades.

What remains is judgment, ownership, and the willingness to be seen deciding. The leaders who succeed in an AI-shaped world will not be the most technical. They will be the most explicit.

Organizations will continue to invest heavily in AI. That is neither surprising nor wrong. The real risk is not moving too fast with technology, but moving too fast without recalibrating leadership.

AI accelerates decisions, exposes judgment, and makes accountability visible. Organizations that treat it as a tooling or efficiency upgrade risk discovering, too late, that their leadership systems were never designed for this level of speed and transparency.

Leaders who take this seriously start differently. They examine how decisions are made, where ownership truly sits, and which judgments must remain human. They test whether their operating model, governance, and leadership behavior can withstand AI pressure before scaling technology itself.

Much of my work with leadership teams now begins at that point: creating the space to examine judgment, ownership, and decision-making under AI conditions, before those weaknesses are exposed publicly. Not to slow adoption, but to ensure AI strengthens leadership rather than undermines it.

AI will keep advancing. The question is whether leadership evolves with it, or is revealed by it.

This article is part of a broader exploration of how AI reshapes leadership, decision-making, and organizational legitimacy, not through tools, but through exposure.