The debate about artificial intelligence has become exhaustingly predictable.

On one side, we have doomsayers who celebrate every misstep—a misdrawn map of Europe, a miscounted number of r’s in “blueberry”—as proof that AI is fundamentally flawed. The word “hallucination” has been weaponized to dismiss technology that, despite its imperfections, has made extraordinary strides in reliability. On the other side, we have enthusiasts, armed with an ever-expanding toolkit of specialized models and applications, who rush to integrate AI into every conceivable business process.

Both camps, I would argue, are missing the point.

The skeptics’ position barely warrants discussion. Yes, AI makes mistakes. So do humans—with alarming regularity. The relevant question isn’t whether AI is perfect, but whether it’s useful. And on that measure, the evidence is overwhelming. Major language models have dramatically reduced their error rates, and their capabilities continue to expand at a pace that would have seemed impossible just years ago. Dismissing this technology because it occasionally stumbles is like rejecting automobiles because they can’t navigate every dirt path that a horse can.

But here’s where it gets interesting: even among those who embrace AI’s potential, most are approaching its implementation backwards. I call this the technology-centric trap. The thinking goes something like this: “We have these amazing AI tools available. Which of our existing business processes can we automate with them?” It’s a natural question, especially given the dizzying array of AI applications flooding the market, each promising to revolutionize some aspect of operations.

The problem is that this approach assumes our current business processes are fundamentally sound—that they just need a technological upgrade to run faster and cheaper. But what if the processes themselves are the problem? What if they’re outdated, inefficient, or built on assumptions that no longer hold in today’s environment? Bolting AI onto broken workflows doesn’t fix them; it just automates dysfunction at machine speed.

The correct sequence is elegantly simple, though harder to execute: identify the problem first, then find the solution. Not the other way around.

This isn’t theoretical musing. My experience with crowdsourcing taught me this lesson clearly. Successful crowdsourcing doesn’t start with assembling a crowd and asking what they can solve. It starts with identifying a specific problem, tracing it to its root cause, and defining it with precision. Only then do you present it to potential solvers. Skip those preliminary steps, and you’ll get solutions to the wrong problems—or no workable solutions at all.

The same principle applies to AI integration. Before asking which AI tools you should deploy, ask: What processes are genuinely holding us back? Where are the bottlenecks that constrain growth? Which workflows were designed for a different era and have simply persisted out of habit? These questions require honest, sometimes uncomfortable introspection about how your organization operates versus how it should operate.

Only after answering these questions does it make sense to survey the AI landscape. If appropriate tools exist, deploy them. If they don’t, consider building them or adapting what’s available. But the technology choice flows from the problem definition, not the reverse.

IBM’s recent paper on AI agent architecture makes this point compellingly. Their analysis reveals that many AI agent deployments stall after the pilot phase not because the technology fails, but because organizations are trying to force-fit advanced AI onto fundamentally broken workflows. Technology works fine; the underlying processes don’t.

This isn’t about being anti-technology or advocating for needless delays. It’s about being strategic. AI offers unprecedented opportunities to reimagine how work gets done, but only if we’re willing to question the status quo first. The businesses that will truly benefit from AI aren’t those that deploy the most tools the fastest. They’re the ones that take the harder path: examining their operations critically, identifying what needs to change, and then—and only then—leveraging AI to build something better.

The future belongs not to those who automate the present, but to those who redesign it first.