There’s a moment in most AI programs where someone needs to ask: Should we keep going?

Not whether it’s theoretically possible. Not whether the problem is interesting. Not whether the vendor says it’ll work.

Whether we should keep spending money on this.

And I’ve noticed something consistent: Organizations ask this question very late. After they’ve already spent more than they probably should. After they’ve built organizational muscle around the program. After it’s become politically difficult to stop.

By then, the conversation has already been decided by sunk costs.

“We’ve already invested $5 million. We can’t just walk away.”

“We’ve been working on this for eighteen months. We need to see it through.”

“The executive sponsor is committed. We can’t tell them it’s not working.”

These are all versions of the same underlying logic. We paid for it already. We can’t get that money back. So we might as well keep going.

This logic is backward.

Here’s what I mean. You’re a financial services company. You’ve invested in building an AI system to improve loan approval decisions. You spent months on requirements. You spent more months on data preparation. You spent more on model development. You’ve been building it for a year. The cost is significant. The team is big.

And then something becomes clear: The business actually cares more about speed than accuracy. The approval process needs to be faster, but your model is slow. Or the regulatory environment shifted and your model’s lack of interpretability is now a problem. Or the business priorities changed and what made sense eighteen months ago isn’t what matters now.

The question appears: Should we keep building this?

The wrong answer is “We’ve already spent $5 million. We’re too far to stop.”

The right answer is “What would we do if we could start over, knowing what we know now?”

If the answer to that question is “We wouldn’t build this,” then you probably shouldn’t keep building it.

I watched this play out with an organization that had committed to an enterprise-wide machine learning platform. The rationale was sound. They had many models. They wanted to manage them all through one system. The vendor claimed it could integrate with their infrastructure. The internal team was enthusiastic. The investment was made.

Eighteen months in, the integration was harder than expected. The vendor’s platform didn’t fit their stack the way they hoped. The maintenance overhead was larger than planned. The payoff wasn’t as clear. And more importantly, the business had moved on. Different problems seemed more urgent.

The team kept building anyway. They’d already spent enough that leadership hesitated to pull the plug.

Another year later, the project was finally cancelled. The system was never deployed. All that investment was gone.

That’s not a vendor failure. That’s not a technical failure. That’s a project management failure. More specifically, it’s a failure to ask the right question at the right time.

Here’s what should have happened at month twelve, when signs emerged that assumptions weren’t holding up:

The project manager should have asked: “If we could start over with what we know now, would we still build this?”

If the answer is no, the follow-up is: “What would we do instead?”

The critical question is: “How much more should we spend before we decide?”

This is where sunk costs become dangerous. Because the amount you’ve already spent doesn’t determine how much more you should spend. The only thing that determines that is future expected value.

The right framework is simple:

Every AI program should have a set of milestones with explicit decision points. Not just technical milestones. Business milestones. “At month six, we should know whether the business case holds.” “At month twelve, we should know whether the technical architecture works.” “At month eighteen, we should have evidence that the model actually improves the process.”

And at each milestone, the question shouldn’t be “Did we hit our technical targets?” It should be “Has anything changed about what makes this valuable?” And “If we started this project today with current knowledge, would we still do it?”

If the answer is no, or if the answer is “Maybe, but only if we change direction significantly,” then you have a decision to make.

Few organizations have this framework. So they drift. The sunk costs mount. The decision gets harder to make. The project becomes its own justification.

I’ve seen organizations rationalize spending millions more on projects they wouldn’t start if they could do it over. I’ve seen teams defend dying programs because the political cost of stopping was too high. I’ve seen executives avoid the conversation altogether because asking “Should we stop?” requires admitting something went wrong.

But here’s the thing: Something usually does go wrong. Not because the team is incompetent. But because the future is hard to predict. What seemed like a good use of capital eighteen months ago might not be today.

That’s not a failure of planning. That’s a fact of operating in uncertainty.

The failure is not adapting when that uncertainty resolves.

The organizations that handle this well have a pattern: They treat AI programs like venture capital investments. High risk. Frequent decision points. Clear go/no-go criteria. If something changes, you reassess. You don’t keep funding because of what you’ve already spent. You fund based on what you expect to get.

And critically, they build a culture where saying “This isn’t working anymore” is possible. Not as a personal failure. But as a normal part of learning what works and what doesn’t.

Because you will build things that don’t work out. You will make bets that don’t pay off. You will discover that assumptions were wrong. That’s how you learn what actually works.

The sunk cost fallacy isn’t just about money. It’s about how you treat uncertainty. Do you let past decisions trap you? Or do you use new information to make better decisions now?

The $5 million you already spent can’t be recovered. But the next $2 million might not need to be spent, if you ask the right question.

The question isn’t “Can we afford to stop?” The question is “Can we afford to keep going?”

Until you ask that clearly, sunk costs will keep winning.