Gut Over Data. When Leadership Becomes Instinct-Driven in an AI World

 

Image generated with AI

“These are dangerous times. Never have so many people had access to so much knowledge, and yet resistant to learn anything.”

― Tom Nichols, The Death of Expertise: The Campaign against 

Established Knowledge and Why it Matters, 2nd Edition

I’ve spent a significant part of my life devoted to work in the data management space, from programming to designing data solutions, and also a significant part of it managing, analyzing, and influencing (a word from these times) the importance of data and information in the decision-making processes of organizations. Also, for the most part, I’ve been staying in my lane, not too much involved in politics or the politics of the corporate world in a public manner.

Yet, there is a growing paradox in today’s leadership landscape, one that is now too big to be ignored, at least by me, combined with an increasing politicization happening in the tech industry, or at least becoming more public.

On one hand, we are living through the most data-rich era in human history; organizations can measure almost everything: customer behavior, operational performance, market dynamics, and even employee sentiment. AI systems can process and interpret the data at a scale that was unthinkable just a decade ago, and yet, on the other hand, in parallel, we are witnessing a resurgence of leadership styles that seem to operate in defiance of that very reality.

A style that prioritizes instinct over analysis, narrative over evidence, speed over deliberation, and, perhaps most importantly and dangerously, opacity over transparency.

Of course, this approach has been prominently associated with figures like Donald Trump, but it’s not limited to politics. Today, variations of this leadership model are increasingly visible among certain tech leaders, startup founders, and even executives in large enterprises.

The problem with this is that the implications of these phenomena are reaching far beyond personality or communication style; they are influencing all aspects of our lives, including, in my view, the very data management space in which I move and work every day.

So, I think it’s important that we analyze or discuss the effect this leadership style trend has on the AI and data management sector, because this is about a deeper shift in how decisions are made and what role data, analytics, and AI will play in the future of leadership.

The Illusion of Decisiveness

“I will confess that in general decisiveness worries me; it is often an excuse for being impatient

with the details or insufficiently sensitive to other people's concerns.”

― Abhijit Banerjee

One of the defining features of instinct-driven leadership is the projection of decisiveness. Decisions are made quickly, often publicly, and framed as bold moves that cut through complexity. And yes, there is an appeal to this approach, especially in uncertain environments, because in times of crisis or rapid change, hesitation can be costly.

But decisiveness is not the same as effectiveness. In many cases, what appears to be decisive leadership is simply decision-making without sufficient grounding in evidence.

We have seen this pattern repeatedly:

• Sudden policy shifts announced without clear data backing
• Strategic pivots in companies based on executive intuition rather than market analysis
• Public statements that contradict internal metrics or expert assessments 

The result is often short-term momentum followed by long-term correction.

In business, this correction shows up as failed initiatives, wasted investment, or organizational confusion. In politics, the consequences can be even broader.

The real issue is not that intuition is inherently flawed; it is true that experienced leaders often rely on instinct as a synthesis of accumulated knowledge.

The problem arises when intuition replaces data rather than complements it.

The Erosion of Data Credibility

“A point of view can be a dangerous luxury when substituted for insight and understanding.”

― Marshall McLuhan, Canadian Communications Professor

Another consequence of this leadership style is the gradual erosion of trust in data itself. When leaders selectively use data to support pre-existing narratives or dismiss inconvenient facts altogether, they create an environment where data becomes negotiable.

This is particularly dangerous in organizations that claim to be “data-driven.” Consider a common scenario inside many companies:

An analytics team produces a report showing that a new product feature is underperforming; the data is clear, and the recommendation is to iterate or reconsider the strategy.

But leadership, already committed to the initiative, chooses to reinterpret the data, question the method, or simply ignore the findings.

Over time, this creates a subtle but powerful shift:

• Analysts become cautious about challenging leadership assumptions
• Data is curated to align with expectations, not the other way around
• Decision-making becomes performative rather than evidence-base

At this point, the organization is no longer data-driven. It is data-decorated.

Tech Leaders and the Myth of Visionary Intuition

“Never ignore a gut feeling but never believe that it's enough.”

― Robert Heller

The tech industry, ironically, has been one of the strongest advocates for data-driven decision-making. Concepts like A/B testing, experimentation, and analytics have become foundational for an industry that keeps pushing for the next intelligent software solution.

Yet, at the same time, the industry has also cultivated a mythology around visionary leadership.

The idea that great leaders “see what others don't" and that they rely on instinct to disrupt markets.

That data can sometimes be a constraint rather than an enabler.

And, ok, there is some truth in this narrative. Breakthrough innovation often involves moving beyond existing data; after all, data reflects the past and present, not the future.

But there is a fine line between visionary thinking and unstructured decision-making.

We have seen examples of tech leaders making high-stakes decisions based on personal belief rather than rigorous analysis:

• Massive product launches without clear user validation 
• Abrupt organizational restructurings driven by executive preference 
• Public commitments to strategies that lack operational feasibility 

In some cases, these decisions succeed, reinforcing the myth of intuition-driven leadership. Yet, in many others, they fail quietly or expensively.

The problem is that success stories are amplified, while failures are rationalized. Big fat tech companies can swallow failure; the same cannot be said for many other companies struggling to compete or survive.

AI as Both Enabler and Bypass

“Before we work on artificial intelligence, why don’t we do something about natural stupidity?”

— Steve Polyak

Ironically, the rise of AI adds another layer of complexity to this dynamic.

In theory, AI should strengthen data-driven leadership: Advanced analytics, predictive models, and decision-support systems can offer deeper insights and reduce uncertainty.

But in practice, AI can also be used to bypass critical thinking. Instead of carefully analyzing data, leaders, especially the ones we are talking about, may rely on AI-generated outputs without fully understanding the underlying assumptions.

So, instead of fostering transparency, AI systems can become black boxes that reinforce existing biases. In this context, instinct-driven leadership does not disappear; it evolves.

Leaders may use AI as a tool to confirm gut feelings rather than challenge them.

Examples I have a few of:

• Asking an AI system to generate arguments supporting a preferred strategy 
• Using predictive models selectively to justify decisions already made 
• Delegating analysis to AI without questioning data quality or model limitations 

The result? Certainly not more rational decision-making but a more sophisticated form of confirmation bias.

The Speed Trap

“You have to drive at a speed that won't affect your decision-making ability

and that may be well below the limit.”

— David Coulthard

Another factor contributing to this shift is the increasing speed of decision-making.

Digital platforms, real-time data, and continuous feedback loops create pressure for immediate action. Consequently, leaders are expected to respond quickly to market changes, public sentiment, and competitive moves. In this environment, deliberation can be perceived as weakness.

Instinct becomes a shortcut.

But speed comes at a cost. When decisions are made without sufficient analysis, organizations may refine for immediacy rather than sustainability; short-term wins can mask long-term risks.

This is particularly relevant in the AI era, where decisions about data governance, model deployment, and automation can have far-reaching consequences.

A rushed decision in AI is not just a tactical mistake. It can become a systemic risk embedded in algorithms and processes.

The Transparency Problem

And well, perhaps the most concerning aspect of instinct-driven leadership is the lack of transparency.

When decisions are based on data, there is at least the possibility of traceability. Assumptions can be examined, models can be confirmed, and outcomes can be measured.

When decisions are based on instinct, this traceability disappears, gone; it is no more. And this creates several challenges:

• Difficulty auditing decisions 
• Limited accountability for outcomes 
• Reduced ability to learn from mistakes 

In organizations, this often manifests as confusion; teams are expected to execute strategies without understanding the rationale behind them. Feedback loops break down, and alignment becomes harder to achieve.

With AI, the stakes can even be higher. If leaders themselves operate as “black boxes,” layering AI systems on top of that opacity will only amplify the problem.

The Future of Leadership: Augmented Judgment

So, where does this leave us?

The future of leadership in the AI era will, in my view, need to adapt to be not purely data-driven nor purely instinct-driven. It requires a balance in which the leadership model

• Enable data to provide the foundation 
• AI to enhance analysis and exploration 
• And most important, human judgment to integrate context, ethics, and strategy 

The key is not to drop intuition, but, in a way, to discipline it.

Effective leaders will need to:

• Challenge their own assumptions with data
• Use AI as a tool for exploration, not validation
• Create environments where evidence can contradict authority
• Embrace transparency as a strength rather than a constraint 

This is not easy; it requires cultural change, not just technological adoption.

So?

There is a clear irony in the current moment.

At the very time when we have unprecedented access to data and analytical capabilities, some leadership models are moving in the opposite direction, toward instinct, narrative, and opacity.

The question is not whether this trend will continue; it likely will, at least in some domains.

The real question is this:

In a world where AI can analyze more data than any human ever could, will leaders rise to meet that capability, or will they find new ways to ignore it?

Because in the end, the future of AI in organizations will not be determined by the technology itself. It will be decided by the mindset of those who choose how, and whether, to use it.