Weeks one to three
We map decisions rather than departments. The output is a short list of repetitive, consequential decisions, each with an owner, a data path, a failure cost and a reversibility rating. Most programmes discover here that the bottleneck is not the model.
We agree one candidate loop to take all the way through, and write down what evidence it must produce before it is allowed to widen.
The build
We build inside your stack with your engineers where they exist, and on our own where they do not. The loop ships with its controls attached: scope limits, refusal conditions, escalation, and a receipt for every consequential action.
Enablement runs alongside rather than afterwards. If your team cannot operate and extend the system without us, we have not finished.
After go-live
Scope expands on evidence, not on optimism. Each proven loop earns the next increment, and the review that grants it uses the system's own records rather than a status report.
We stay for as long as the programme needs and no longer. Handover is a deliverable, not a courtesy.
“We would rather run one decision end to end with evidence than pilot nine things that never leave the sandbox.”
Continue
- The gap between intelligence and action6 min
- What 'autonomy with evidence' looks like in practice7 min
- How to evaluate an AI system you intend to let act5 min
- AI governance in London: what boards are actually asking for5 min
- How to choose an ethical tech consultancy without buying theatre6 min
- The EU AI Act, translated into changes in your codebase7 min
- Running one AI governance model across the Gulf and Europe6 min