AI & QUALITY LEADERSHIP BASICS
- Why leaders must understand AI: value, limits, accountability.
- Role of AI in customer focus, compliance, and continual improvement.
- Assistive vs. autonomous use; human-in-the-loop expectations.
- Traceability and guardrails: prompt logging, source citation, version control.
PLANNING AI USE IN QUALITY
- Choosing safe starter areas: documentation, reporting, minutes, basic analysis.
- Identifying risks: data privacy, hallucinations, bias, IP, security, model drift.
- Oversight design: approval points, evidence capture, KRIs, audit trails.
- Pilot scoping: objectives, COPQ/cycle-time/right-first-time metrics, success criteria.
LEADING AI-SUPPORTED TEAMS
- Assigning tasks with AI support: roles, RACI, competence matrix.
- Checking outputs for ISO/standards compliance (e.g., ISO 9001 clauses 7–10).
- Evidence for audits: prompt logs, citations, validation notes, decision records.
- Workflow integration: AI-assisted RCA/5-Whys, FMEA support, CAPA drafting with human verification.
BUILDING A CULTURE OF AI-ENABLED QUALITY
- Encouraging safe experimentation: sandboxes, red-team/blue-team reviews, sample data.
- Introducing AI into PDCA: Plan (scoping/prompts), Do (controlled generation), Check (structured validation), Act (SOP updates/standardization).
- Adoption and communication cadence; simple measures for usage and effectiveness.
- Stop/scale decisions for micro-pilots and knowledge capture for reuse.
MEASUREMENT, REPORTING & AUDIT READINESS
- Selecting leading and lagging indicators for AI-enabled quality (including COPQ).
- Packaging evidence for management review and external audits.
- Supplier/customer assurance and external confidence considerations.
- Final pilot consolidation: controls, metrics, responsibilities, and next steps.
