Getting AI past the demo: what production adoption actually requires

The technology desk's finding this summer is blunt: most AI pilots stall not on model quality but on operations, no owner, no data boundary, no definition of done.

What production adoption actually requires: a named business owner for each workflow, an explicit list of the systems and documents the assistant may touch, an error path when it is wrong, and a measurement that finance accepts, hours, cycle time or error rate, not enthusiasm.

For owner-led businesses the advice is to pick one revenue-adjacent workflow, run it to production discipline, and only then scale. One workflow in production is worth ten demos, and it changes the valuation conversation at exit.