AI your people actually use: from experiments to adoption

Most organisations are no longer short of AI experiments. They are short of AI that anyone uses.

Pilots get built. Demos impress. A proof-of-concept lands in a steering committee deck. And then very little changes in the work itself — because the technology was the easy part, and nobody owned the hard part.

The hard part was never the model

The value of AI is not in the model. It is in the business action it makes possible. A model that produces a good answer no one trusts, inside a workflow no one changed, is not adoption — it is shelf-ware with better marketing.

Three things decide whether AI actually gets used:

• Trust — people need to understand what the system does, where it is reliable, and where a human stays in the loop.
• Workflow — the work has to be redesigned around the capability, not bolted on beside it.
• Governance — clear ownership, guardrails, and measurement, so the organisation can scale it without flying blind.

Get those right and the technology becomes almost incidental. Get them wrong and no amount of model quality saves the project.

Move the goalposts to adoption

A useful discipline: stop measuring AI work by whether it was built and start measuring it by whether it is used — and whether the business is measurably better because of it.

That reframing changes everything. It means identifying the practical use cases where adoption is realistic, redesigning the workflow around them, building governed solutions people can trust, and embedding the result into the work people already do.

The goal is not an impressive pilot. It is AI your people actually use — trusted, adopted, and measured by business impact.

This is the Do stage of The Darb Path — delivered as Darb Praxis. If your AI effort is rich in experiments but thin on adoption, a diagnostic can map where it would actually take hold.