Most AI implementations fail not because the tech is wrong but because the rollout is wrong. This AI implementation guide is the 6-phase playbook we run with teams that want measurable adoption in 90 days.
Phase 1 — Discovery (Week 1)
Audit current usage. Survey the team about pain points. Map the top 5 candidate workflows. Output: a 1-page "AI thesis" naming the bets.
Phase 2 — Pilot design (Week 2)
Pick two workflows. Pick a small team (5–10 people). Set baselines: time-on-task, error rate, satisfaction. Get governance approval.
Phase 3 — Pilot (Weeks 3–6)
Run the pilot. Daily 15-min office hours for the first two weeks. Weekly review. Capture failure modes. Don't scale yet, no matter how exciting it gets.
Phase 4 — Measurement (Week 7)
Compare to baselines. Three numbers must move: time saved, quality maintained or improved, active usage rate. If any slipped, fix the workflow before scaling.
Phase 5 — Scale (Weeks 8–11)
Expand to the next 100 people. Curriculum, not script. Champions program — one trained operator per 10-person team. Shared prompt library.
Phase 6 — Operate (Week 12+)
Quarterly fluency checks. Onboarding integration. Continuous prompt-library improvements. Sunset workflows that aren't being used.
What kills implementations
Three things: (1) Skipping the baseline — no proof anything changed. (2) Mandate without buy-in. (3) Premature scale before measurement.
The governance layer
Even informal teams need a one-page acceptable-use policy. Cover: which models with which data classes, what gets logged, what is reviewed by humans, what's escalated. Update quarterly.
Where to start
The Be Fluent AI portal has a rollout-track curriculum with templates. Pair with our corporate rollout playbook.