AI Can Generate a Thousand Answers. But Who Defines the Right Problem?

A few days ago, I was experimenting with AI to solve a product problem.

Within minutes, it generated multiple approaches, implementation plans, user stories, and even edge cases.

Ten years ago, this would have taken a team several days.

My first reaction was excitement.

My second reaction was a little uncomfortable...

If AI can generate solutions so quickly, what happens to the role of people who were valued for solving problems?

The more I thought about it, the more I realized that we may be asking the wrong question.

The real value was never in generating solutions.

It was always in defining the right problem.

Answers are abundant; questions are not

Over the last few years, AI has become remarkably good at creating content.

It can write articles.

Generate code.

Build presentations.

Analyze data.

Design interfaces.

Soon, most routine knowledge work will become significantly faster.

But while AI has become better at answering questions, I haven't seen it become equally good at deciding which question deserves to be asked in the first place.

And that's an important distinction.

Because in the real world, problems rarely arrive neatly packaged.

Customers don't say...

"I need Feature X."

They struggle with something and describe the symptom.

Businesses don't say...

"Our process is broken."

They complain about cost, delays, or customer satisfaction.

Leaders don't say...

"We have an organizational design issue."

They ask for another dashboard.

Finding the actual problem often requires observation, empathy, conversations, and sometimes challenging the very assumption on which the discussion started.

That is messy work.

And it is deeply human.

Too many solutions, not enough alignment

One lesson I have learned across product management, operations, and business transformation is that teams rarely fail because they lack solutions.

In most cases, there are already too many solutions.

The challenge is that different people are solving different versions of the problem.

I've seen teams spend months improving a process, building a feature, or automating a workflow, only to discover that the original assumption was flawed.

The quality of the solution rarely exceeds the quality of the problem definition.

A team can spend months building exactly what was asked for...

...and still fail because nobody questioned whether that was the right problem.

Some of the best product decisions I've seen came from someone asking a simple question:

"Are we solving the right thing?"

That single question can save months of effort.

AI can accelerate execution.

But it cannot replace judgment.

At least not yet.

Why AI makes design thinking more valuable, not less

I actually think AI will make Design Thinking more valuable.

Not because Design Thinking competes with AI...

...but because they complement each other.

Design Thinking helps us understand people.

AI helps us explore possibilities.

Design Thinking frames the challenge.

AI expands the solution space.

Design Thinking asks why.

AI suggests how.

The organizations that combine both will have an enormous advantage.

The ones that simply automate existing thinking may become faster...

...without becoming better.

From problem solvers to problem definers

There is another shift that I find even more interesting.

For years, organizations rewarded people for having answers.

Today, answers are becoming abundant.

Anyone with a good AI tool can generate dozens of them.

Scarcity is moving elsewhere.

It is moving toward context.

Toward judgment.

Toward understanding people.

Toward connecting seemingly unrelated observations.

Toward asking better questions.

In many ways, the role of leaders, product managers, designers, and strategists is evolving from problem solvers to problem definers.

And defining the right problem has always been the essence of Design Thinking.

When answers are abundant, judgment is the differentiator

Technology will continue to evolve.

Models will become more powerful.

Agents will become more autonomous.

Automation will quietly become part of everyday work.

But behind every meaningful innovation, there will still be a human deciding...

"This is the problem that truly matters."

The more I work with AI, the more convinced I become that technology doesn't eliminate the need for human judgment.

It raises the value of it.

Because when answers become abundant, the real differentiator becomes understanding.

Understanding people.

Understanding context.

Understanding which problems are worth solving.

Perhaps that is why Design Thinking feels more relevant today than it did a decade ago.

Not despite AI.

Because of it.