Define the Problem Space

Stop Solving for the Wrong Thing. The fastest way to waste time and money with AI is to start with the tech instead of the problem. This is where most teams go wrong. They build something interesting that no one actually needs.

A good AI strategy starts by getting painfully clear on the problem.

Don’t Build AI Features. Solve AI-Sized Problems

Before you write a single line of code or prompt a single model, ask the team:

• What pain is the user already feeling?

• Where are they overwhelmed, slow, confused, or stuck?

• What decisions do they wish the system could make for them?

AI isn’t the answer unless the problem is real, recurring, and rooted in complexity. If the user just wants a button to work faster, you don’t need machine learning. You need good design.

Use Jobs-to-Be-Done, Not Just Feature Requests

People don’t want AI. They want outcomes.

They want to spend less time sorting, planning, typing, or guessing. They want to feel smarter, safer, more in control. That’s the real job.

Use tools like:

• User interviews (with open-ended follow-ups)

• Jobs-to-be-Done mapping

• Support ticket analysis

• Behavioral funnels and drop-off data

These show where the real friction is and where AI could become invisible help.

Don’t Fall for “AI for AI’s Sake”

It’s tempting to chase the cool demo. The pitch deck magic trick. The thing that makes investors lean in.

But novelty wears off fast. Utility lasts.

Before you greenlight anything AI-powered, ask yourself:

• Would this still be valuable if the user didn’t know AI was involved?

• Does this help them accomplish something better, faster, or smarter?

• Is this solving a problem or just showing off?

Focus Before You Forecast

Defining the problem space isn’t a one-time exercise. It’s an ongoing habit. You’ll revisit this often as your users evolve and as your AI capabilities grow. But starting here keeps your roadmap grounded.

Coming Up Next

Now that you know what not to build, it’s time to explore what you can. In Part 3, we’ll break down AI capabilities and how to map them to real product value.