Operating Model
How enterprise teams turn AI from scattered experimentation into a governed operating system of demand, service ownership, and portfolio discipline.
This area is for readers focused on the mechanics of running AI well: service transparency, consumption discipline, accountability, and the management systems that make scale sustainable.
Core operating disciplines
These pages explain where TBM, FinOps, and related disciplines fit in the economics of enterprise AI.
Signature framework
The Layers of AI Value Management
How usage transparency, output quality, workflow value, delivery alignment, and portfolio strategy fit together.
Domain overview
TBM & AI
How service and portfolio management disciplines adapt to shared AI capability economics.
Domain overview
FinOps & AI
How demand visibility, optimisation, and unit-cost governance evolve for AI workloads.
Domain overview
ITFM & AI
How budgeting, planning, forecasting, allocation, and reporting disciplines adapt to AI.
Domain overview
SPM & AI
How strategic portfolio management shapes which AI initiatives to fund, scale, or stop.
Signature framework
AI Economics Maturity Model
A maturity model for judging whether the organisation is becoming more governable as AI scales.
Applied reading
Use these pieces when the operating question is specifically about inference demand, service cost, or broader economic governance.
Article
FinOps for Inference-Era Workloads
Why inference-heavy AI services require FinOps practices that extend beyond cloud billing.
Framework
AI ROI Models
Use ROI timing, proof standards, and portfolio logic to govern AI operating decisions.
Framework
AI TCO Framework
Understand the full cost stack that operating teams are actually being asked to govern.