Establish Executive Alignment and Intent (Govern)

• Objective: Secure leadership buy-in and define AI risk governance objectives.
• Actions:
  • Identify a responsible AI officer (can be part-time or dual-role).
  • Define a risk tolerance threshold for AI systems in maintenance and supply chain workflows.
  • Align AI risk governance with FAA compliance (e.g., AC 43-210A) and aviation safety culture.

System and Context Mapping (Map)

• Objective: Understand the AI system landscape and operational context.
• Actions:
  • Inventory AI Tools:
    • Predictive maintenance tools using ML (e.g., anomaly detection from sensor data).
    • AI-driven supply chain optimizers or reorder automation systems.
  • Contextual Analysis:
    • Define use-case boundaries (e.g., non-critical decision support vs. direct action).
    • Map stakeholders: maintenance techs, parts suppliers, pilots, regulators.
    • Document input/output data flows and decision-making roles of AI systems.

Risk Identification and Assessment (Measure)

• Objective: Evaluate AI system risks and associated impact in an aviation maintenance context.
• Actions:
  • Conduct AI Risk Assessments for each use case:
    • Accuracy of failure predictions.
    • Data quality (sensor calibration, maintenance records).
    • Model drift or overfitting concerns.
  • Quantify Risk Categories:
    • Safety (e.g., incorrect maintenance flag).
    • Operational (e.g., part ordering delays).
    • Ethical/legal (e.g., bias in vendor selection).

Risk Management and Mitigation (Manage)

• Objective: Apply controls and operational practices to reduce AI risks to acceptable levels.
• Actions:
  • Put AI-in-the-Loop: Ensure a certified A&P mechanic reviews all AI-predicted maintenance recommendations before execution.
  • Validation & Testing:
    • Schedule regular model validation using real-world maintenance outcomes.
    • Perform failure scenario simulations (e.g., false positives/negatives).
  • Data Controls:
    • Implement logging and versioning of all data and model updates.
    • Conduct periodic data integrity checks.

Ongoing Monitoring and Feedback Loops (Manage/Govern)

• Objective: Continuously assess AI performance, risks, and adapt accordingly.
• Actions:
  • Metrics Tracking:
    • False alarm rate, predictive accuracy, parts downtime reduction.
  • User Feedback Integration:
    • Capture technician feedback on AI insights.
    • Enable rapid correction of AI-generated errors.

Governance Structure and Documentation (Govern)

• Objective: Institutionalize accountability, transparency, and lifecycle management.
• Actions:
  • Draft and maintain an AI Governance Charter.
  • Document roles, responsibilities, data provenance, and compliance checkpoints.
  • Ensure readiness for FAA or customer audits involving AI-assisted processes.

Workforce Training and AI Literacy (Govern)

• Objective: Build trust and competence in AI-enabled workflows.
• Actions:
  • Conduct quarterly AI risk awareness and digital tool training for mechanics and operations staff.
  • Provide "explainability" dashboards or simplified model rationales for decision transparency.

Key Success Factors

• Align with FAA safety culture and Part 43/145 compliance.
• Keep the approach lightweight but documented—tailored to shop size.
• Prioritize human review and explainability in any AI-driven action.
• Implement continuous learning cycles and feedback-driven iteration.