Continual improvement requires mechanisms for learning from incidents, assessment findings, and operational experience, then feeding those lessons back into the system’s design and governance. Post-incident reviews and periodic risk register reviews are the primary vehicles.

The improvement cycle connects assessment findings to systemic change. A major non-conformity in one system’s fairness testing might lead to a revision of the organisation’s fairness testing standard that applies to all systems. A serious incident might reveal a gap in the human oversight design that triggers a review of the oversight framework across the portfolio.

The AI Governance Lead tracks improvement actions arising from assessments, incidents, and governance reviews, ensuring that lessons learned are translated into concrete changes rather than archived in reports. The improvement record (actions identified, actions taken, outcomes observed) is retained as QMS evidence.

Key outputs

• Incident, assessment, and operational learning feeding back into governance
• Cross-system lesson propagation
• Improvement action tracking with outcome measurement
• QMS evidence