You Don’t Have an AI Problem — You Have a Problem Definition Problem

People jump straight to using AI.

“Build a model.”
“Optimize this.”
“Automate that.”

They skip the only step that actually matters.

Defining the problem.

And worse — defining the utility function.

So the system works.

And produces the wrong outcome perfectly.

That’s the failure.

An AI system is not intelligent.

It is obedient.

It does exactly what you define — not what you intend.

If your problem is vague, the system guesses.

If your utility function is wrong, the system optimizes the wrong thing.

And it will do it extremely well.

That’s why this step is not technical.

It’s foundational.

Start with the problem.

Not the tool.

A real problem is not:

“Improve customer engagement.”

That’s a slogan.

It has no edges.

No constraints.

No failure condition.

A real problem is specific:

“Increase repeat purchases from existing customers by 15% within 90 days without increasing acquisition spend.”

Now you have pressure.

Now you can be wrong.

Now the system has something to act on.

If you can’t define the problem in a way that can fail, you don’t have a problem.

You have a wish.

There is a deeper rule.

A good problem definition forces trade-offs.

What matters more?
Speed or accuracy?
Growth or margin?
Short-term gain or long-term stability?

If everything matters, nothing is defined.

And AI will optimize whatever is easiest to measure.

Usually the wrong thing.

That leads to the second step.

The utility function.

This is where most people fail.

The utility function is not a metric.

It’s the rule that defines what “good” means.

Maximize X.
Minimize Y.
Under constraint Z.

Simple in form.

Brutal in consequence.

Because whatever you encode here becomes the system’s goal.

Not your intention.

Your definition.

If you say:

“Maximize user engagement”

The system will keep people hooked.

Not informed.
Not satisfied.

Hooked.

Because engagement is what you told it to optimize.

That’s not a bug.

That’s obedience.

This is why utility functions are dangerous.

They are clean.

Reality isn’t.

You compress messy goals into a single objective.

And lose everything that didn’t fit.

Ethics.
Context.
Long-term effects.

Gone.

Unless you encode them explicitly.

There is another layer.

Most real problems require multiple objectives.

Not one.

You don’t just want growth.

You want sustainable growth.

You don’t just want accuracy.

You want reliable accuracy under changing conditions.

So the utility function becomes a balance.

A tension.

Maximize X while minimizing Y under constraint Z.

That’s harder.

Because now the system has to trade off.

And trade-offs are where bad definitions get exposed.

High-level operators don’t rush this.

They stress-test it.

“What happens if we optimize this too well?”
“What behavior does this encourage?”
“What are we ignoring?”

They look for failure before it happens.

Because once the system is running, it’s too late.

There is a final truth.

AI does not solve problems.

It locks in definitions.

If your problem is poorly defined, AI scales confusion.

If your utility function is misaligned, AI scales damage.

So the real work is not building the model.

It’s deciding what the model should care about.

Precisely.

Because once you tell it, it will not question you.

It will execute.

Relentlessly.

And if you got that wrong, it won’t fail.

It will succeed in the worst possible way.