A few years ago, building a software product needed a lot of coding. Today, AI tools can help you build a working prototype in a few hours.

You can describe your idea, ask an AI coding tool to build it, and see the result on your screen.

This is great. But there is one problem.

Just because you can build something doesn’t mean people need it.

As a product manager, I believe choosing the right problem is still more important than building the solution quickly.

Here are a few things I keep in mind.

1. An idea is not a problem

Let’s say you want to build an AI tool for sales teams.

Sounds good. But what problem will it solve?

  • Are salespeople spending too much time finding customers?
  • Are they struggling to follow up with leads?
  • Or are they spending hours updating their CRM?

These are different problems. Each one needs a different solution.

Before you start building, understand who has the problem and why it matters.

2. A working product doesn’t mean people will use it

AI tools can help you build a website, create screens, connect APIs, and make a working prototype.

But this doesn’t mean people will use your product.

They may try it once and never return. They may not find it useful. Or they may already have a better way to do the same thing.

Before spending more time building, show your product to a few potential users.

Watch how they use it. Ask for honest feedback. Learn what is missing.

3. Start small

You don’t need to build everything on day one.

Imagine you want to build an AI tool that helps product managers understand customer feedback.

Your first version could be very simple.

A user uploads a CSV file. The tool groups similar feedback and gives a short summary.

That’s enough to test the main idea.

You don’t need user accounts, payment pages, team access, and a fancy dashboard at the start.

Build the smallest version that helps you test your idea.

4. Talk to people before you start coding

When you have an exciting idea, it’s tempting to open your AI coding tool and start building immediately.

I have to remind myself not to do this.

First, talk to people who may use your product.

Ask them how they solve the problem today. What takes too much time? What do they find difficult? Have they tried any other solution?

Don’t ask only, “Would you use my product?”

Many people will say yes to be polite. What people actually do is more useful than what they say they might do.

5. Use AI to test your ideas, not just to write code

AI coding tools are useful even before you build the final product.

You can use them to create a simple prototype, try different designs, or test two ways of solving the same problem.

For example, you may be unsure whether users need a dashboard or a chat interface.

Instead of spending weeks deciding, create a basic version of both and ask users to try them.

This helps you learn what works before spending too much time on development.

6. Don’t add features just because AI makes it easy

Earlier, building a new feature could take days or weeks.

Now, AI may help you build it in a few hours. So it’s easy to keep adding more features.

But every feature adds some work. You still need to test it, fix bugs, explain it to users, and maintain it later.

More features can also make your product confusing.

Before adding anything, ask yourself: Does this help the user solve the main problem?

If the answer is no, you probably don’t need it yet.

7. Building the product is only half the work

Imagine you have built a really useful product.

How will people find it?

Will they discover it through Google, LinkedIn, YouTube, or a community? Why should they trust you? What will make them pay for it?

These questions matter from the beginning.

You don’t need a big marketing plan for every small project. But you should have some idea of how your product will reach the right people.

A simple product that reaches the right audience can do better than a great product nobody knows about.

8. Learn before you try to grow

When you launch a new product, don’t focus only on the number of users.

First, try to understand whether people are getting value from it.

Ask yourself:

  • Can users complete the main task?
  • Does the product save them time?
  • Do they come back to use it again?
  • What problems do they face?
  • Would they miss the product if it disappeared tomorrow?

The answers will help you decide what to improve next.

9. Not every problem needs AI

Just because AI is popular doesn’t mean every product needs it.

Sometimes a simple search box, a filter, or a few rules can solve the problem better.

Use AI when it makes a real difference, such as understanding natural language, working with unstructured information, or helping users complete a task that was difficult before.

Don’t add AI just to make your product look modern.

Start with the problem. Choose the technology later.

10. Product managers still have an important job

AI can help us write code, create prototypes, and test ideas faster.

But someone still needs to decide what to build, which problem matters most, and what can wait.

Someone needs to understand users, make trade-offs, and decide whether the final product is actually useful.

This is where product management comes in.

When building becomes easier, choosing the right thing to build becomes even more important.

My takeaway

AI has made building software easier. But it hasn’t made every idea worth building.

You can build more products in less time. But that doesn’t mean you should.

Don’t focus only on how quickly you can build something. Focus on whether you’re solving a problem people actually care about.