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Monday, April 20, 2026

Why Most Executives Are Faking Their AI Confidence

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Wendy Lynch
Wendy Lynch
Wendy Lynch, Ph.D., is an analytics and AI translator who helps organizations turn complex data into actionable business outcomes. A board member, keynote speaker and consultant, she specializes in bridging the gap between analytics teams and business leaders to drive measurable impact.

Between the leaders rushing to adopt AI they don’t understand, and those too afraid to touch it, a dangerous gap has opened — and it starts at the top.

Ask a room full of executives whether their organization is leveraging AI, and almost every hand will go up. Ask them to explain what their most important AI system actually does, how it makes decisions, or what happens when it’s wrong — and you get silence.

This is not corporate secrecy. It is the defining leadership struggle of this technological moment. AI arrived faster than any prior technology in modern history — faster than the internet, faster than the personal computer, faster than any organizational learning curve was designed to absorb. Unlike those earlier shifts, AI arrived with an unspoken professional mandate: that competent leaders should already be fluent in something many experts are still figuring out.

The pressure to exhibit fluency before having it is intense. Executives are asked to have an AI strategy, adopt AI tools, and demonstrate AI readiness, but stakeholders are often just as uncertain about what those things mean. The expectations are real. Fluency on both sides of the table is frequently not.

That gap — between the talk of AI literacy and the practice of AI operations — is where organizational risk lives. And it produces two very different, equally dangerous behaviors.

An Impossible Position

Before we judge these leaders, let’s acknowledge what they’re up against.

Generative AI has spread faster than almost any technology in history. Within two months of launch, ChatGPT reached 100 million users — dwarfing Instagram, Netflix, and every consumer technology before it. Adoption more than doubled in a single year, from 33% in 2023 to 71% in 2024. The internet and personal computer unfolded over decades, giving executives time to learn and recalibrate. This time, there was no grace period.

The headlines offer no useful compass. On one side: genuine productivity gains, reduced headcount, and compelling success stories. On the other: a hallucinated chatbot answers, erasing $100 billion in shareholder value within hours, and insurance algorithms deny care at one claim per second. Reputational risk is now the top AI concern among S&P 500 companies, with executives warning that bias, misinformation, or failed implementations can quickly erode customer trust and investor confidence.

Also Read: What Two CEO Exits Tell Us About Leading Through AI

Two Responses to the Same Problem

Leaders tend to fall into one of two camps. I’ve started calling these FOMO and FOCO.

FOMO — Fear of Missing Out — drives the over-adopters. They see competitors announcing AI initiatives, hear boards demanding efficiencies, and feel the anxiety of being left behind. They greenlight tools, make announcements, and delegate implementation — without asking: What are this system’s failure modes? Who is accountable if it causes harm at scale?

The danger isn’t enthusiasm. It’s undiscriminating trust — and where that trust lands. FOMO-driven leaders don’t trust AI itself. They trust people who don’t fully understand AI either. The CTO without first-person experience. The VP who came back from a conference converted. The vendor whose limitations were never disclosed. Each link depends on the one before it. No one holds the full picture. It is building the airplane after taking off.

FOCO — Fear of Catastrophic Outcomes — drives the resisters. This fear is not irrational. Leaders with FOCO have seen implementations unravel: Air Canada’s chatbot invented a bereavement fare policy that didn’t exist, leaving the airline legally liable. Claims systems enabled reviews so automated that they became meaningless, triggering litigation. A flawed model doesn’t make one bad decision — it makes the same bad decision thousands of times before anyone notices.

What makes FOCO immobilizing is the unknown ceiling of harm. A biased hiring algorithm doesn’t affect one candidate — it affects every candidate, invisibly, for months or years. When you can’t gauge the potential damage, freezing feels rational. They promise the flight will leave, but keep taxiing on an endless runway.

FOMO tells leaders to jump. FOCO tells them to freeze. Both are understandable. Both are dangerous. Both trace back to the same unmet need: a genuine understanding of what they’re dealing with.

The Trusted Advisor Problem

Executives don’t make AI decisions alone. They rely on people they trust: technical leads, consultants, digitally fluent peers. Those advisors are often only one step ahead: better informed than the CEO, but not fully versed in a system’s failure modes, data quality issues, or real-world limitations.

The leader’s trust is genuine — but placed in a chain of partial knowledge where enthusiasm does the work that evaluation should. Someone they once trusted may not grasp the implications of today’s AI environment. Plus, AI is still widely treated as a technology problem for the CIO rather than a business issue requiring broad leadership. That framing keeps business context out of AI decisions and technical knowledge out of business conversations. The gap between those two groups is precisely where failures are born.

Also Read: By the Time Your Credentials Appear in a Dump, You’re Already Behind

What Actually Closes the Gap

The answer isn’t a training module, though literacy helps. It’s building conditions where informed confidence replaces the performed kind.

That starts with questions before tools: what decision are we trying to make, what would a wrong answer cost us, and would we even know it was wrong? These are leadership questions, the ones most often skipped in the rush to demonstrate AI capabilities.

It means involving leaders in shaping the problem before analysis begins. And it means having someone who bridges both worlds: technical enough to represent AI outputs accurately, business-savvy enough to connect them to decisions that matter. Someone who says not just “there is a super cool new way to do this”, but also “there are steps we should take, and boundaries we should set.”

That translation function is what converts performed confidence into something organizations can build on.

Executives are wandering in new territory. They are navigating unprecedented terrain without adequate maps. The goal isn’t to eliminate FOMO or FOCO — both reflect real stakes. It’s to replace AI buzzwords and aspirations with AI literacy as practice. That starts with admitting what we don’t know — and building organizations where skilled translators and honest answers, not flashy demos and promised profits, move us forward.

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