What You Think You Know About AI Is Probably Wrong

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3-minute read

Most senior leaders have been told they need an AI strategy. What most have not been told is that the opinions shaping that strategy may be pointing them in the wrong direction. Those opinions come from subjective sources such as vendor pitches, conference keynotes, and headlines written to generate clicks rather than clarity.

This is the first in a series written for C-suite and senior leaders, and it starts somewhere different. There are three things every senior leader needs to understand about AI right now: what it is, how to deploy it in ways that create real business value, and how to lead people through the transformation it creates. Each post explores one or more of these questions, grounded in what the research shows and what experienced leaders are genuinely grappling with.

Not Magic

The most useful thing to understand about AI is also the least glamorous. Every modern form of it, from the fraud detection your bank has run for years to the tools now drafting your team's emails, is fundamentally a system that finds patterns in data and makes predictions based on those patterns. That is powerful. It is also limited.

“The gap between what AI appears to do and what it does is where most leadership mistakes get made.”

AI does not understand in any human sense. It cannot reason through a novel problem, verify its own outputs, or recognize the limits of its knowledge. Which means it can be confidently wrong, perpetuate biases embedded in the data it learned from, and struggle badly with tasks requiring true reasoning or common sense.

Not One Thing

AI is not a single technology. The version generating the most headlines right now, the one behind ChatGPT (Open AI), Claude (Anthropic), and Gemini (Google) is generative AI, which creates content including text, images, code, and analysis. It works by training on vast amounts of text and learning to predict, with remarkable fluency, what words should follow other words. That fluency is useful. It is also misleading, because fluency is not the same as accuracy. Most AI applications are built on one of these three large language models.

There is also predictive AI, which forecasts outcomes and has quietly powered credit scoring, demand forecasting, and fraud detection for years. And then there is computer vision, which processes and interprets images, and is used in everything from medical diagnostics to manufacturing quality control.

Each type has different use cases, costs, and failure modes. The key is knowing which problem you are solving before selecting a solution. Most of the AI conversation in boardrooms is about generative AI, which is appropriate given how fast it is moving, but leaders who conflate all AI into one category will make poor decisions about where to invest and where to be cautious.

The Confidence Problem

The specific risk with generative AI that most leaders don’t fully appreciate is that it produces fluent, confident output even when it is wrong. One technology firm leader who has tracked this closely puts the error rate at around 30%, and notes that the better the writing, the harder the mistake is to catch. A system that sounds authoritative is not the same as one that is correct.

AI's usefulness is directly tied to the quality of what you put into it. That means leaders can't outsource their judgment to the tool. They need to develop a feel for where to trust it and where to verify it, and that only comes from using, testing, and refining the tools yourself, not from reading about them.

What This Series Covers

In a 2026 study of senior leaders across global enterprises, 93% identified human factors as the primary barrier to AI adoption. Not the models, not the infrastructure, not the data. The barriers were people, culture, and leadership. ¹

That is the gap this series is written for.

Fieldwork

Before you read further, try this exercise. Write down the three most influential things you have heard about AI in the last six months. Then ask where each one came from. A vendor? A headline? A peer who is implementing? A board member who read something? The source tells you how much weight to give it.

Then go test three AI applications your organization is already using. Notice how you choose to engage with each one, what the experience tells you, and how you might use it differently going forward. Have your senior leaders do the same and compare what you find.

About the Author

Dr. Melissa Fristrom

Founder, Core Allies

Melissa Fristrom is the founder of Core Allies, LLC an executive coaching and advisory firm that works with C-suite leaders navigating inflection points. She advises senior leaders on strategy, organizational change, and the human side of technology adoption. Before founding Core Allies, she held senior leadership roles in frontline positions up to CEO. She is based in Boston.

The artwork in this post is from fristrom.art. Melissa works in encaustic, pigmented wax layered to explore how color carries emotion, perception, and meaning. It is a practice that runs parallel to the questions this series is asking about leadership: looking more carefully at what is actually in front of you, rather than what you expect to see.

If any of this landed, useful or uncomfortable, that's worth paying attention to. I work with leaders and their teams on exactly these questions. I'd love to connect.

mfristrom@coreallies.com · (617) 444-9809 · coreallies.com

¹ Croft, Jazz, Sumer Vaid, Lily Cheng, and Ashley Whillans. “Where Senior Leaders Are Struggling with AI Adoption, According to Research.” Harvard Business Review, February 26, 2026. Based on in-depth interviews and focus groups with 35 senior executives across global enterprises. hbr.org/2026/02/where-senior-leaders-are-struggling-with-ai-adoption-according-to-research