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AI Integration Playbook

Operationalize AI with outcome-first roles, bias safeguards, and a 4-week launch sprint

Why use this playbook

Use this playbook to launch AI initiatives without overwhelming subject-matter experts. It maps AI responsibilities across the C2O lifecycle so validation, bias mitigation, and adoption become shared outcomes—not hidden toil.

Use-Case Intake Canvas

QuestionGuiding Prompts
Desired outcomeWhat measurable shift will prove value?
Lifecycle bottleneckWhere do decisions currently stall?
Data readinessWhat quality, availability, and governance considerations exist?
Risk lensesBias, hallucination, cyber, regulatory, reputational
Enablement partnersSecurity, Legal, Finance, Architecture, HR
Success economicsCost to serve, time-to-value, productivity mix

Role Mapping Patterns

PhaseDriveContributeEnableAdviseInform
DiscoverProduct or Venture LeadData Science, Domain SMEsLegal, Finance, SecurityEthics CouncilExecutive Sponsor
DecideBusiness SponsorAI Lead, Finance PartnerEnterprise ArchitectureCompliancePMO
BuildEngineering LeadData Science, QA, UXPlatform, DevOpsRisk & ControlsSupport Teams
RunOperations LeadSRE, Customer OpsData GovernanceCISOBusiness Units
AdoptChange LeadChampions, TrainingHR, CommunicationsCustomer SuccessAll Contributors

Bias & Risk Safeguards

  • Define a bias taxonomy covering representation, measurement, interaction, and deployment.
  • Establish validation cadence: daily during prototyping, twice weekly during pilot, weekly post-launch.
  • Red-team critical decisions at each phase gate; log mitigations in the POP.
  • Instrument drift dashboards and incident response runbooks before adoption starts.

4-Week Integration Sprint

WeekOutcomesKey Ceremonies
1POP completed, outcomes + risks agreedDiscover workshop, risk pre-mortem
2Prototype aligned to acceptance testsBuild syncs, bias lab, validation huddles
3Pilot instrumentation readyDecide gate, enablement readiness review
4Launch + adoption plan activatedRunbook rehearsal, change campaign kickoff

Metrics Dashboard

  • Leading: decision latency, validation coverage, data freshness, drift detections.
  • Lagging: value realization, adoption depth, incident rate, satisfaction scores.
  • Behavioral: Collective Psychological Ownership, collaboration density, escalations avoided.