There is a quiet part of enterprise AI that does not get enough credit:

Services.

Not services as an add-on, but services as the place where strategy has to survive contact with reality.

That is where the questions get specific:

- What data can we actually use?

- Which teams need to change how they work?

- What risks are acceptable?

- What breaks when this leaves the demo room?

- What does success look like 90 days after deployment?

The model can produce an answer.

The enterprise still needs someone to turn that answer into architecture, process, controls, training, and measurable value.

The services layer is where AI stops being a promise and starts becoming an actual capability.