Russ Reeder is CEO of KeyDelta, an operator-led advisory helping CEOs & leadership teams fix operations, implement AI & increase valuations.gettyLast year, MIT put a number on something operators already felt in their gut: 95% of enterprise AI pilots deliver no measurable return.The study, "The GenAI Divide: State of AI in Business 2025," blamed a "learning gap" for that statistic. The tools don't adapt to how the work gets done. That's true, but it's a symptom, not the disease. I've spent 30 years running technology companies that help thousands of businesses solve real problems, and from what I've seen, the failure rarely starts at the model. It starts months earlier, in the operating model nobody wanted to fix first.Your Flat TireYou can drive around town on an almost flat tire. Sometimes you don't even know there's an issue; sometimes you feel the wobble. You baby it, keep it under 30 and get there. Then you merge onto the highway at 75, and the wobble becomes a blowout. Now you're not managing an annoyance. You're managing a crash.That's AI in most companies. The internal broken processes are often survivable at low speed; people are smart and route around them. They know which report is wrong and correct it in their heads. Then you automate it, and the humans quietly holding it together are no longer in the loop to catch the wobble.AI does not fix broken execution. It compounds it. Whatever you had, clarity or chaos, you now have more of it faster.Four things turn a wobble into a highway crash. I see all four constantly.1. You're Automating A Process That Was Already BrokenMost AI projects start with the wrong question: "What can we automate?" Instead, businesses need to ask, "What's broken, and should it even exist?"Teams often point the shiny tool at the loudest workflow and automate it exactly as it is, potholes and all. No one asks why it has seven approval steps, three of which are one person checking another person's work, because trust broke two reorgs ago. Automating that hard-codes the dysfunction, which becomes faster, cheaper, permanent and now invisible.If a process is broken, automation doesn't solve it. It scales it. Fix operations first. Build AI second.2. Everyone's Building, Nobody's Doing ProductAI handed everyone a builder's tool kit overnight. That's the good news and the problem in one sentence.Anyone can put together a tool over a weekend. But knowing the business is not the same as knowing how to scope a solution. Someone who knows the business often cold-specs what they want and asks for everything under the sun—every edge case, every nice-to-have and every "while we're at it" with no prioritization. That's not product management. That's a wish list with a deadline.Pragmatic product management is the discipline of subtraction. It names the single outcome that defines done and says no to 80% of the requests, so the 20% that move the number actually ship. Vision can be summed up in one question: What are we saying no to? Most AI projects never ask it.The constraint isn't knowing the business. It's the discipline to define one outcome and say no to everything else.3. The Business Never Reaches The EngineerThen the requirements get handed off, and the wobble gets violent.The person who knows the business throws a spec over the wall. The context and the exceptions—the "we never do it that way for the West region"—never make it onto the page. The thing on the other side of the wall used to be a human engineer who'd at least push back: "This doesn't make sense. What about X?" Now it's often an AI coding agent that questions nothing. It builds exactly what the flawed spec says, confidently and fast. Nobody finds out until it's in production and the numbers don't reconcile.The fix isn't a better document. Put a human who knows both sides between the business and the build. Have them watch the work happen. Trust but verify the spec against reality before a line of code ships.Requirements without context aren't requirements. They're a game of telephone, and your fastest, most literal player just stopped asking questions.4. You Shipped It: Now Who Owns It?This is the one that scares me most. An engineer builds an app, it works, they deploy it, and there's no governance anywhere. There's no approved platform, no security review, no standard for where the data lives and no answer to the questions that matter the day after launch: Who's allowed to update this? When does it get patched? What happens when the builder leaves? Who's accountable when it's wrong, and it will be wrong, because AI is confident and frequently incorrect?None of that was decided because the whole point of the new power was to skip the old gates. Now you've got dozens of ungoverned apps making real decisions on real data, with no owner and no off switch. MIT found companies buying from vetted vendors succeeded roughly 67% of the time. Internal builds only succeeded 33% of the time. Governance is much of that gap.Deploying without governance isn't going to speed you up. It's debt you'll repay with a breach, an audit or a rebuild.Lessons For Leaders​As you head into the next workday, keep three things in mind​:1. Fix the process before you automate it. Automating broken steps leads to being broken at scale.2. Every build needs one named outcome and one named owner. If either is fuzzy, it isn't ready.3. Don't deploy without a governance standard for your platform, security, update cadence and ownership. Decide it before launch, not after the breach.The MIT number will keep getting quoted—95%. People will keep blaming the models. But the model was never the problem. We're taking broken processes, half-scoped solutions, lost-in-translation requirements and ungoverned deployments and merging them onto the highway at full speed.You felt the wobble the whole time. Fix it before you hit the on-ramp.Fix operations first. Build AI second. Define it. Measure it. Own it. Close it. Scale it.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?