When we talk about AI productivity – what do we actually mean?

When we talk about AI productivity, what exactly do we actually mean by it? Or is it just the next buzzword that we keep using until nobody knows anymore what's really behind it?

I keep being bothered by the idea that you just have to give employees a few LLMs, copilots and chat accounts and can then expect a measurable increase in productivity. The simple counter-question "In what way, exactly?" is enough to throw a surprising number of AI strategies off balance.

Because if 100 employees each work with AI on their own, you might end up with 100 faster employees. That's nice. But it's not yet a transformation.

Things only get interesting when these 100 people work together with AI in adapted processes, with shared goals, shared context and shared guardrails. Then you get not just individual acceleration, but real organizational scaling.

That is exactly why orchestration is needed. Between the human layer and the AI/machine layer, a new layer emerges in which tasks are distributed, boundaries are defined, results are checked and decisions are passed back to people. And it is exactly there that operational experience suddenly turns into AI performance.

That's why the real enabler isn't the latest model. It's the knowledge of how an organization actually works. Which role decides what? Which task can the machine take on? What evidence is needed? Where does a human have to step in? It is exactly this operational knowledge that becomes the foundation for real AI scaling.

And only when the time gained can be reliably reinvested in value creation does what I would call real AI productivity emerge. Not simply more output. Not simply faster. But an organization that has learned to scale human experience through machines in a controlled way.
We call this Organizational AI Readiness.