Wrong Algorithm? Or Wrong Problem?

Last month, a startup showed me their customer churn model. 93% accuracy. Clean dashboard. Executives loved the demo.

It sat unused for four months. Why?

They built a classifier when what they actually needed was a priority queue. The model could say “this customer will churn.” It could not say “call this customer by Tuesday.”

Same data. Different problems. Different algorithms. This is the real AI crisis nobody talks about.

Most AI projects don’t fail because of the model. They fail because the problem statement was wrong.

Here’s what that looks like in practice 👇

When “recommend similar items” goes wrong

❌ Bad framing: “Predict what users will buy”

• Team builds complex classification models
• Months of feature engineering
• Core need gets missed

✅ Clear framing: “Find the 10 most similar items based on behavior”

• Points directly to similarity matching (e.g., nearest neighbors)
• Shipped in two weeks
• Actually gets used

One team spent six months. The other shipped in two weeks.

So how should leaders test a problem statement before approving AI work?

Start with three checks.

1. Force the decision. If the model succeeds, what specific action changes on Monday morning?
2. Name the output. Is the output a label, a score, a ranking, or a number? If you can’t say that clearly, the wrong algorithm will follow.
3. Identify the cost of being wrong. Is a false positive worse than a false negative? Does timing matter more than precision?

AI doesn’t create clarity. It scales whatever clarity—or confusion—you already have.

Leaders who slow down here ship faster and get real value. Everyone else gets impressive demos that quietly expire.

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