ai-rollout-five-phase-playbook.md

AI Rollout: Five-Phase Playbook

On-demand reference for the revops-change-management skill.

This is your operational blueprint. Use it to sequence your AI adoption.

Phase structure: Discover → Pilot → Scale → Embed → Govern

Each phase has gates (go/no-go decisions), specific activities, and success metrics.

Phase 1: Discover (Weeks 1–3)

Goal: Identify which processes, decisions, and teams benefit most from AI. Build business case.

Activities:

Audit current processes: where's manual work, where do humans add low value, where's error-prone work?
Prioritize by impact and ease (impact/effort matrix)
Identify high-risk processes first (compliance-sensitive, data-heavy, error-prone)
Build financial case: time savings, quality improvements, cost avoidance
Check regulatory landscape (Works council notification if EU-based)

Duration: 2–3 weeks

Success metrics:

3–5 priority processes identified
Business case quantified (conservative estimate OK here)
Regulatory blockers identified (early)
Executive alignment on first pilot

Gate: Proceed to Pilot when executive sponsor approves business case and pilot scope.

Phase 2: Pilot (Weeks 4–8)

Goal: Test one AI application in a controlled environment with a small, engaged team. Prove concept. Learn what doesn't work.

Two-week sprint structure (repeat as needed within pilot phase):

SPRINT TEMPLATE: Scope → Baseline → Build → Run → Evaluate → Document

Week 1: Scope & Baseline
Day 1-2: Scope
  - Define the specific decision/task: "Qualification scoring for inbound leads"
  - Define AI tool: vendor, model, integration point
  - Define success: "AI scores match human qualification 80%+ of the time"
  - Define constraints: data available, team capacity, regulatory requirements

Day 3-4: Baseline
  - Measure current state: How long does qualification take? Accuracy? Who decides?
  - Document decision logic: What criteria does a human use to qualify?
  - Establish comparison: Can we run manual and AI in parallel for a week?

Week 2: Build & Run
Day 5-7: Build
  - Configure tool: connect data, set parameters, define rules
  - QA: test on historical data, validate outputs match expectations
  - Safety check: confirm no data leakage, no PII exposure, no bias amplification

Day 8-10: Run
  - Live pilot: 100–200 records through AI + human decision
  - Log: inputs, AI output, human decision, time taken
  - Weekly standups: team feedback, quick fixes to parameters
  - Incident log: any errors, any weird outputs, any governance issues

Week 3: Evaluate & Document
Day 11-12: Evaluate
  - Compare: AI vs human accuracy, speed, consistency
  - Sentiment: team feedback, adoption friction, confidence in tool
  - Financial: hours saved, quality improvements, cost-per-decision

Day 13-14: Document
  - Pilot report: what worked, what didn't, learnings
  - Process map: AI's new place in the workflow
  - Team playbook: how to use tool for next phase
  - Recommendation: scale, refine, or kill

Pilot metrics (measure all):

Accuracy: AI decision vs. ground truth (human expert review). Target: 75%+ agreement.
Consistency: Same input yields same output. Target: 95%+ (identify edge cases).
Speed: Time per decision (AI vs. human). Target: 60%+ faster.
Adoption: Team using tool without prompting. Target: 80%+ of available opportunities used.
Confidence: Team trusts AI outputs (survey 1–5). Target: 3.5+.

Kill criteria (when to stop an initiative):

Accuracy <65% (tool not reliable enough)
Adoption <40% after 2 weeks (team doesn't want it)
Data quality issues discovered (garbage in, garbage out)
Governance blockers identified (regulatory issue, data sensitivity)
Cost-per-use higher than manual process
Team confidence score <2.5 (distrust too high)

Gate: Proceed to Scale when:

Accuracy ≥75% OR team confident in outputs + clear remediation plan
Adoption ≥60%
No unresolved governance issues
Financial case holds (ROI neutral or positive within 6 months)

Phase 3: Scale (Weeks 9–16)

Goal: Expand from one team to 3–4 teams. Roll out with clear playbooks. Build confidence.

Activities:

Expand to adjacent teams (similar roles, similar processes)
Recruit change champions from pilot team (they become trainers)
Weekly operational cadence: usage standup, issue triage, quick wins celebration
Build skill: structured training, hands-on practice, certification (optional but effective)
Gather feedback: monthly pulse survey, open suggestion channel

Duration: 6–8 weeks

Success metrics:

3–4 teams active, 60%+ usage rate
Accuracy sustained (75%+) or improving
Incident rate <1 per 100 active users
Team confidence increasing (sentiment survey)
Time savings realized and quantified

Gate: Proceed to Embed when:

60%+ of expanded user base actively using tool
Business case metrics met (time saved, quality improved)
No major governance incidents
Process standardization documented

Phase 4: Embed (Weeks 17–26)

Goal: Make AI usage standard operating procedure. Integrate into workflows, performance metrics, hiring.

Activities:

Integrate AI into formal workflows: job descriptions updated, training mandatory, metrics tracked
Add to onboarding: new hires trained on AI tools day 1
Performance management: AI productivity (time saved, quality) becomes KPI
Feedback loops: monthly review of outputs, retraining as needed
Build organizational muscle memory: "this is how we work now"

Duration: 8–10 weeks

Success metrics:

80%+ usage rate sustained
New hires up to speed within 2 weeks
Accuracy stable or improving
Productivity metrics show sustained gains
Shadow AI usage declining (people using approved tools)

Gate: Move to Govern when:

AI-enabled workflow is default, not optional
Team competency normalized
Business value clearly realized
Regulatory compliance proven over time

Phase 5: Govern (Ongoing, starting week 26+)

Goal: Ensure sustained use, manage risk, optimize continuously.

Ongoing activities:

Monthly metrics review: usage, accuracy, incidents, cost-per-use
Quarterly business review: is AI still delivering ROI? Should we expand?
Continuous retraining: skill decay monitoring, refresher cadence
Incident management: protocol for errors, data breaches, user issues
Tool refresh: evaluate new models, update parameters, retire if outdated
Works council reporting (EU): quarterly updates on usage, incidents, any changes

Metrics (track forever):

Active user rate, engagement rate, output acceptance rate
Incident rate, data governance compliance, audit pass rate
Time saved per user, quality improvements (reduced rework)
ROI (realized + trending + capability)

Weekly Cadence During Implementation

Once you're in Pilot, Scale, or Embed, this is your rhythm:

WEEKLY STANDUP (30 min, Tuesdays 9am)
Attendees: Project lead, tool owner, team representatives, change champion

Agenda:
1. Usage: Are people using it? Adoption rate vs. target?
2. Issues: Any errors, data problems, user confusion?
3. Quick wins: What's working well? Celebrate it.
4. Blockers: What's slowing adoption? What needs fixing?
5. Forecast: What's coming next week?

Decision rights:
- Quick fix (parameter tweak, training gap): PM decides, implements by Thursday
- Larger issue (tool limitation, design change): escalate to steering, decide within 1 week
- Kill decision: steering committee vote (Weeks 1–4 of pilot)

OUTPUT:
- 1-pager: adoption %, issues, next week's focus
- Shared with sponsors, works council (EU), exec team

This framework takes 6–9 months from Discover to full Embed. That's realistic. GenAI adoption is not a 6-week sprint. It's a quarterly narrative.

Your biggest risk isn't the technology. It's abandoning the change process too early when adoption looks slow (weeks 4–6 is always slow), or trying to move faster than your organization can absorb.

Move at the speed of trust-building, not the speed of the technology.