The AI Problem Is Not That We Build Too Slowly. It Is That We Build Too Much.

A CEO sees a problem.

Sales is slowing down. There are leads in Salesforce, tasks in Monday, offers waiting for a response, customer information somewhere else, and probably a few important details living in somebody’s head.

A year ago, the natural response would have been to call a meeting.

Then perhaps write a specification.

Then find someone to build something.

Today, the CEO opens an AI tool.

By the next day, there is a prototype.

By the end of the week, there is a dashboard pulling information from three systems, making recommendations and trying to decide what should happen next.

This feels like progress.

And sometimes it is.

But this is where we need to stop for a second.

Because the problem is no longer that building is too slow.

The problem is that building has become so fast that we are losing one of the few things that used to force us to think.

Friction.

The old process was slow. That was bad.

Let me be clear. I am not romanticising the old way.

Nobody misses the six-week discovery phase for a dashboard that three people will use.

Nobody wants to spend two months explaining a simple automation to a supplier, then wait another month to discover that everyone understood the requirement differently.

The ability to move from an idea to a working prototype in days is a serious advantage.

The intention is good.

The risk starts when every problem immediately becomes a solution.

A sales problem becomes a sales bot.

A reporting problem becomes a dashboard.

A project problem becomes a project control platform.

A communication problem becomes another AI assistant.

One tool for the pipeline.

One tool for the projects.

One tool for management decisions.

One tool to explain what the other tools are doing.

It starts to look organised in the same way a drawer full of cables looks organised.

Everything is technically there.

Good luck finding the charger.

Fast execution removes the natural decision point

In the past, starting a project was expensive enough to create resistance.

Someone had to approve the budget.

Someone had to explain the expected outcome.

Someone had to decide whether the problem was important enough.

The process was often too slow, but at least there was usually a moment when somebody asked:

“Are we sure we should do this?”

AI and vibe coding are removing that moment.

That is understandable.

When an intelligent tool is ready to start immediately, asking for nothing except another prompt, the temptation is obvious.

Why wait?

Why pay someone?

Why explain the full context?

Let’s build something and see.

Again, this is not necessarily wrong.

The problem starts when “let’s see” becomes the operating model for everything.

Then every thought creates a prototype.

Every prototype creates more questions.

Every question creates another feature.

And because the feedback is immediate, the whole thing keeps moving.

This feels like momentum, but in practice it can create a new kind of delivery problem.

Solution sprawl.

Or perhaps more simply:

AI litter.

We are becoming very good at creating things we have not decided we need

The biggest danger is probably not for people who have no ideas.

It is for people who see possibilities everywhere.

They recognise a problem, immediately imagine a system that could solve it, and now have tools capable of starting that system almost instantly.

That combination is powerful.

It is also risky.

Because mental capacity slowly becomes the only limit on how many initiatives can start.

Not how many can be finished.

Not how many create measurable value.

Just how many can start.

That distinction matters.

A prototype can exist without a clear business problem.

A dashboard can exist without a useful decision behind it.

An AI assistant can collect enormous amounts of information without improving anything that the business actually cares about.

This feels like innovation, but in practice it can create more work.

Someone still has to validate the data.

Someone still has to decide what the recommendation means.

Someone still has to check the permissions.

Someone still has to maintain the integrations.

Someone still has to decide whether the tool is helping.

And eventually, someone has to clean up the prototypes that survived long enough to become infrastructure by accident.

The problem is not AI.

The problem is that the cost of starting has collapsed while the cost of choosing badly has not.

The bottleneck has moved

For a long time, companies had more ideas than delivery capacity.

So the obvious problem was execution.

How do we build faster?

How do we automate more?

How do we reduce dependency on developers?

How do we prototype without waiting?

AI is solving part of that problem.

But when one bottleneck disappears, another usually becomes visible.

The new bottleneck is decision quality.

What should we actually build?

Which problem deserves attention?

What result do we expect?

How will we know whether the problem has improved?

Is technology even the next sensible step?

These questions used to sit in front of execution.

Now they are in danger of being dragged behind it.

We build first.

Then we try to explain what the system is for.

That is not faster delivery.

That is unresolved thinking with an interface.

This changes the role of the expert

I think this is also why the traditional project model is starting to feel strange in some situations.

A company identifies a project.

A supplier receives the scope.

The supplier delivers.

This model assumes the project should already exist.

But what happens when the real problem is that there are ten possible projects and nobody knows which one deserves to survive?

Then another delivery team is not necessarily the answer.

More capacity can simply create more activity.

This is where the expert role changes.

The expert is not there to block ideas.

Not to become the corporate police.

Not to say no to everything until innovation quietly dies in a governance meeting.

The role is more practical than that.

Help separate the useful signal from the noise.

Ask what result matters.

Challenge whether the problem has actually been understood.

Find the smallest safe step that creates evidence.

Then, when something is genuinely valuable, help it move faster.

Good structure should not become a brake.

It should become a valve.

It lets the valuable work through.

It prevents every passing thought from becoming a permanent operational commitment.

This is not an employee who needs eight hours of work

There is another mental shift here.

Many leaders still evaluate internal and external experts using two different rules.

An employee must be kept busy.

A supplier must be kept moving.

So the employee receives more tasks because there are hours to fill.

The supplier receives more pressure because there is a project to finish.

Both approaches make sense inside the old model.

They become less useful when the real value is better selection.

A good expert may spend an hour proving that a three-month project should not start.

That can be more valuable than spending three months delivering it efficiently.

A good expert may look at five initiatives and say:

This one matters.

These two need validation.

This one is solving the wrong problem.

And this one can wait.

That does not look as busy as creating twenty tickets.

But activity is not control.

The point is not to fill capacity.

The point is to improve what the company does with its capacity.

Before building, ask four questions

The fix is not complicated, but it does require discipline.

Before another AI tool, automation, dashboard or internal platform starts growing, ask four questions.

What business problem are we actually trying to improve?

Not what system do we want to build.

What is happening in the business that should be different?

What visible result do we expect?

More leads?

Higher conversion?

Faster response time?

Less management effort?

Fewer errors?

A shorter delivery cycle?

“Better visibility” is not enough.

What evidence tells us that this is the real bottleneck?

A weak sales result can come from lead generation, conversion, pricing, follow-up, positioning, capacity or ten other problems.

Adding more data does not automatically tell us which one is true.

What is the smallest step that can test the assumption safely?

Not the smallest feature.

The smallest step that gives us useful evidence.

Sometimes that is software.

Sometimes it is a report.

Sometimes it is a manual experiment.

Sometimes it is one person sitting next to the CEO for two days and understanding what is actually happening.

The ticket should not carry the thinking.

The thinking should happen before the ticket.

And increasingly, the same is true for the prototype.

We do not need to slow down

This is not an argument against AI-speed delivery.

Quite the opposite.

The faster we can build, the more valuable good judgement becomes.

When implementation took six months, companies could only make a few large mistakes.

When implementation takes six hours, we can now create small mistakes at industrial scale.

That is progress of a sort.

The next advantage will not come from being the company that can prototype everything.

Soon, almost everyone will be able to do that.

The advantage will come from knowing what deserves to move from idea to prototype, from prototype to operation, and from operation to something the business genuinely depends on.

The problem is not that we have too many tools.

The problem is that the tools are ready before the thinking is.

Good project hygiene in the AI era will therefore look slightly different.

Less focus on controlling execution.

More focus on controlling what enters execution in the first place.

Because when we can build almost anything, the most important question is no longer:

“How quickly can we make this?”

It is:

“Should this exist at all?”