The Real Reason AI Is Failing

In The Checklist Manifesto, Atul Gawande writes that failure stems from one of two things. The first is ignorance: we don’t know what we need to know. The second is ineptitude: we know, but fail to apply it correctly. He argues that we need more of eptitude, a rarely used word that means the ability to apply knowledge consistently and effectively.

This distinction struck me as I considered the state of artificial intelligence in business and society. AI is failing not because the technology doesn’t work. It’s failing because we, as humans, have not yet figured out how to work with it. We are failing in both of the ways Gawande describes.

There is ignorance, because most people still do not understand what AI is or what it does. This is not willful ignorance. It is confusion rooted in language. We describe AI as if it were an intelligent intern or a digital brain, anthropomorphizing it with terms like “thinks,” “understands,” or “knows.” But AI does none of these things. It calculates. It models patterns. It predicts outcomes based on vast datasets and statistical inference. It does not reason, imagine, reflect, or intuit. And yet, even professionals in the field often use language that suggests otherwise, reinforcing public misunderstanding and misplaced expectations.

There is also ineptitude, but not in the typical sense of failing to follow established rules. When it comes to AI, many of those rules don’t yet exist, or they exist in silos, hidden behind proprietary walls or fragmented across regulatory bodies. Organizations lack coherent, accessible, standardized frameworks to guide responsible implementation. Ethical guidelines are still in development. Best practices vary widely across industries. In many cases, leadership teams know they need to act, but don’t know how to do so safely, responsibly, or with long-term impact in mind. The result is a patchwork of AI efforts: technically impressive, but strategically misaligned or ethically fragile.

Psychologists warned about the human tendency to be overconfident in what we believe we understand. This “illusion of validity” is particularly dangerous when applied to complex systems like AI. Just because an AI tool appears intelligent does not mean we understand its logic, assumptions, or blind spots. The tool’s availability is not a substitute for clarity of purpose, readiness, or foresight.

How, then, do we overcome ignorance and ineptitude in this space?

We start by getting honest about what we don’t know. That requires humility at the individual, organizational, and societal levels. We need better education, not just in how AI works, but in how to ask the right questions of it. We need to train leaders not only to adopt new tools but to interrogate them, to challenge assumptions, and to build cultures where curiosity and skepticism coexist with innovation.

Ineptitude is harder to solve because it requires systems, not just skills. Leaders must build structured pathways for responsible AI integration. That includes governance mechanisms, cross-functional oversight, ethical review, and meaningful employee involvement. Without these in place, no amount of technological sophistication will prevent failure. Hiring the right people or buying the right platform is not enough. Success with AI is not just a matter of intention; it is a matter of infrastructure.

Teams need the space to admit they don’t fully understand a tool, to question its outputs, to raise ethical flags, and to learn in public. Organizational learning collapses when those conversations are shut down, either by hierarchy or hubris. Mistakes go unaddressed. Complexity gets buried. AI becomes just another black box.

Artificial intelligence is not the problem. Our relationship to it is.

Until we replace assumption with awareness, and ambition with disciplined application, we will continue to see failure. Not because we lacked intelligence but because we failed to apply it.

That is the difference between what we know and what we deliver.

That is the difference between failure and eptitude.