The eight-step AI implementation framework

AI in CC · ~7 minute read

AI implementation is not a project; it is a sequence of decisions with explicit go/no-go gates at every step. The eight-step framework that lets the operation decline at any phase without losing face.

The eight steps

1. Define the use-case and value hypothesis; 2. Vendor or build assessment; 3. Pilot design — pre-specified, scoped tightly, comparison-included; 4. Pilot execution — operating-model discipline, observability, honest measurement; 5. Pilot evaluation against pre-specified criteria; 6. Scaling decision — phased, with re-evaluation; 7. Production deployment with operating-model in place; 8. Ongoing operation — maintenance, monitoring, periodic review.

Each step has an explicit go/no-go gate; the operation can decline at any step without losing face or wasting future investment.

Pilot design that produces evidence

A pilot is an evidence-gathering exercise, not a launch. Pre-specify what good looks like; scope tightly; set a defined window (6–12 weeks typically); define success criteria upfront; include comparison; track the full metric set; plan for failure.

A pilot without these is political cover. A pilot with these produces evidence the operation can decide on.

Scaling without the failure pattern

Pilot success rarely survives scaling intact. Pilot users were selected; pilot contact mix was unusual; pilot ran with more support than production. Phased rollout — one team, then three, then ten — with honest investigation of degradation at each phase.

The disciplined leader treats degradation as data, not as teething problems.

The principle

Each phase has explicit go/no-go gates. The operation can decline at any step without losing face. That is the discipline that protects both the pilot budget and the credibility for the next round.

Most AI failure isn’t the model — it’s skipping a gate.

The eight-step implementation framework The eight steps ▸ 1. Define ▸ 2. Vendor / build ▸ 3. Pilot design ▸ 4. Pilot execution ▸ 5. Pilot evaluation ▸ 6. Scaling decision ▸ 7. Production deployment ▸ 8. Ongoing operation Common failures ▸ Big-bang deployment ▸ Over-committed pilots ▸ No exit strategy ▸ Cost under-budgeted ▸ No internal capacity for sustain ▸ Politics over evidence Each step has a gate; decline at any without losing face

The closing principle

The framework is the discipline. Eight steps, eight gates, decline at any without losing face. Most AI failures come from skipping a step rather than from the model itself.

See also