This interview is one of the more provocative ones in the series.

And I mean that in a good way.

Jan’s core message is simple, but uncomfortable for many clubs:

Having data is not the same as using data intelligently.

That sounds obvious. But in football, it is still one of the biggest gaps.

Over the last decade, clubs have invested heavily into data providers, dashboards, tracking data, scouting platforms, performance reports, recruitment tools, and internal analytics. That investment matters. It professionalized a lot of decision-making.

But the next step is different.

The question is no longer:
“Do we have enough data?”

The better question is:
“Are we letting the data challenge our assumptions?”

That is where Jan’s answer really stood out.

He points to three reasons why clubs still struggle:

1. The amount of data is too large to handle properly without AI
2. Clubs often tell the data what they want to see
3. Confirmation bias still drives too many decisions

That second point is important.

A lot of analytics work in football still starts with an opinion.

A scout likes a player.
A sporting director has a hypothesis.
A club has a preferred profile.
Then the data gets used to support that view.

This is not real intelligence.

It is decorated intuition.

The strongest data systems should not simply confirm what the club already believes. They should surface what the club did not know to ask.

This is why PLAIER’s positioning is interesting to me.

They are not framing themselves as another reporting layer. They are not just saying: “Here is what happened.” Their ambition is to use AI-based data science to answer deeper football questions.

What makes a good footballer?
How should a footballer be measured?
How do you measure squad strength?
What is the assumption of winning?
How important is the coach?

These are not dashboard questions.

These are model-building questions.

And that is exactly where the future of football analytics, predictive football intelligence, AI scouting, recruitment analytics, and squad planning is likely heading.

Not more tables.
Not more charts.
Not more data subscriptions.

Better questions.
Better models.
Better prediction.

The most surprising answer in the interview was probably Jan saying that it is “not important at all” for clubs to first define what kind of football organization they want to be before implementing AI-driven systems.

I expect many people in football strategy would disagree with that.

My own instinct is usually that clubs need clarity before building systems.

But I understand the point behind his answer.

If your AI model is genuinely strong, maybe the first task is not to force your existing philosophy onto the data. Maybe the first task is to let the system reveal what actually drives outcomes.

That is a different mindset.

It moves from:
“We know what we are looking for.”

To:
“Show us what we are missing.”

And that is where predictive AI can become valuable.

Not because it replaces human decision-making, but because it can expose blind spots at a scale humans cannot match.

PLAIER’s claim is bold. They want to help decode football. They analyze huge volumes of players and football data, and their belief is that the industry still underestimates AI because the results can look almost too simple.

That point is fascinating.

In many industries, good technology often feels obvious after the fact.

The output looks simple.
The interface looks easy.
The recommendation looks clear.

But underneath, there is a lot of complex data science, model design, validation, and domain interpretation.

That is probably where many clubs will struggle to evaluate AI vendors.

The best systems may not feel complicated on the surface. But the quality of the prediction, the underlying assumptions, the training data, and the ability to generalize across football contexts are what matter.

For me, this fits perfectly into the broader topic I keep coming back to:

Football does not need more data for the sake of data.

It needs systems that turn data into better decisions.

In commercial operations, that means better CRM segmentation, fan engagement, campaign targeting, sponsorship reporting, and revenue prediction.

In sporting operations, it means better player evaluation, squad planning, recruitment strategy, performance forecasting, and risk management.

Different departments. Same principle.

Data collection is now the baseline.

The edge sits in interpretation, prediction, and execution.

And that is why this conversation with Jan is so relevant.

It challenges clubs to move beyond retrospective analytics and ask whether their data setup is actually helping them see the future, or merely explaining the past more elegantly.

The bonus answer made me laugh, but it also says a lot about the confidence behind PLAIER:

If Jan had full authority over a club’s data strategy, he would cancel all data subscriptions, sign with PLAIER, and watch the team win.

A little tongue-in-cheek, of course.

But the underlying conviction is clear.

PLAIER believes the future of football intelligence belongs to predictive AI, not backward-looking statistics.

And whether you agree with every part of that or not, it is exactly the kind of thinking football needs more of.

Less comfort.

More challenge.

Less confirmation bias.

More predictive intelligence.