How this layer operates
Optimization is the layer that maintains the deployed AI portfolio — addressing drift, refining configurations, replacing tooling that has been outpaced, and retiring capabilities that no longer warrant their continuing cost. Its quarterly cadence aligns with the rhythm at which Measurement evidence stabilises enough to support interventional decisions, and at which vendor change cycles produce material new configurations to evaluate.
Optimization is distinct from Execution because Execution introduces capability; Optimization refines, replaces, or retires it. Optimization is distinct from Governance because Optimization is forward-looking change management; Governance is the standing posture that survives Optimization cycles. The interface between Optimization and Sunset — the final stage of the AI Lifecycle — is the layer's most under-discussed responsibility: capabilities accumulate gravity, and Optimization is where retirement decisions are made deliberately rather than by erosion.
Optimization artefacts include the Continuous Optimization Tracker (COT-01), the quarterly model drift assessments, vendor performance reviews (SUS-01), the AI Market Scan Radar (SUS-03) when used to inform replace-versus-retain decisions, and the Technology Sunsetting Plan (SUS-06). Optimization decisions are documented decisions; the layer's outputs become inputs to the next Governance cycle.
A function operating without Optimization watches its AI portfolio decay invisibly — vendors slip, models drift, configurations rot, capabilities ossify. The function may continue running the same dashboard and reporting the same numbers, but the underlying capability is no longer the one Measurement initially captured. Optimization is the layer that ensures the portfolio the function defends today is the portfolio the function actually operates today.