How an Internal AI Lab Emerges

This week, a client wrote about our collaboration. Publicly. On LinkedIn and Instagram.

That made me happy. Not because of the visibility. But because it shows: It works.

First understand. Then build.

I’ve been working with this company for four and a half months. The first weeks? Just conversations. Analyzing. Understanding how they work. Where potential lies. What’s really needed.

No quick solutions. No pre-packaged concepts. No “Here’s your AI strategy, good luck.”

Now we’re building. Concretely. An internal AI lab.

More than just a team. A new way of working.

What’s special about this AI lab: We’re not just changing what the team does. We’re changing how it works.

I enable the team. Through a new way of working based on agile methods. Through new tools that support this way of working. Through self-organized teams with a new leadership structure.

Why all this?

Because an effective AI lab needs speed. It must be able to act as an autonomous research and development team. Decide quickly. Test quickly. Learn quickly.

That doesn’t work in existing, rigid organizational structures.

An AI lab isn’t a project you build into the old hierarchy. It needs space. Autonomy. And a way of working that fits its purpose.

The team does the work

The team tests. Structures. Sharpens approaches. I’m there, asking questions, showing patterns, introducing new methods. But they do the work themselves.

That’s exactly how it works.

Not: “Here’s your strategy, good luck.”

But: “Let’s figure out together what works for you.”

Courage instead of hype

What particularly impresses me about this collaboration: The courage.

They experiment. They speak openly about limitations. No hype. No loud drumming. Just good work.

In their post was a sentence that stuck with me:

“BE BRAVE. SAY WHAT YOU KNOW.”

And that’s exactly what they live.

One puzzle piece. An important one.

The AI lab is one puzzle piece. An important one. But it’s not about the lab alone.

It’s about the company finding its own direction. With support. But not dependent.

That’s the difference between consulting and mentoring.

Consulting delivers answers. Mentoring helps ask the right questions.

What this means for you

If you’re considering integrating AI into your company, these are the most important insights:

There are no shortcuts.

You can get external expertise. You should. But the work, the engagement, the understanding – that must come from within.

An AI lab isn’t a department. It’s a transformation.

It’s not enough to assign a few people and say: “Do AI.” A real AI lab needs:

• A new way of working (agile, iterative, experimental)
• New tools that support this way of working
• Self-organized teams with decision-making freedom
• A leadership structure that enables autonomy

This is more than a project. This is organizational change.

Old structures slow down innovation.

If your AI lab is trapped in existing hierarchies and processes, it won’t develop the speed it needs. Innovation needs room to breathe.

My role in this? I’m the sparring partner. I ask the uncomfortable questions. I show patterns. I introduce new methods. I hold up the mirror.

But the decisions? The team makes them.

This brings me joy

Such collaborations bring me joy.

Not because they’re fast. But because they’re sustainable.

Not because I deliver the solution. But because the team develops it themselves.

And when a post appears saying: “We’re building an AI lab” – then I know: It works.

Want to find out how AI can work concretely in your company? Not as a hype project, but as real integration? Let’s talk. Book your 30-minute clarity conversation here.