Strategy
Beyond experimentation: how to industrialise enterprise AI
The distance between a working demo and a running system is where most AI budgets disappear. A field guide to closing it.
Every enterprise now has an AI demo. Far fewer have an AI system. The difference is industrialisation — the unglamorous engineering that turns a capability into something that runs unattended, holds up under audit, and improves as the business changes.
Start where the work is expensive
The temptation is to start where the technology is exciting. Resist it. Start where a workflow is repetitive, rule-bound, and costly — the queue that never clears, the reconciliation that eats a team's week. Value there is measurable, and a measurable win funds the next one.
Make "done" a definition, not a vibe
An autonomous system needs to know when it has succeeded. Encoding the definition of done — the check the agent measures itself against — is the single most important design decision in the build. It is what lets an agent verify its own work instead of hoping a human notices a mistake.
An agent that can't check its own work isn't autonomous. It's unsupervised.
Widen autonomy on evidence
Trust is earned in the audit trail. Run agents with a human gate first, let the record accumulate, and move the gate up only as reliability is proven. Autonomy is a dial, not a switch — and the dial should turn on evidence, not optimism.