AIGN Runtime Economic Governance is a vendor-neutral governance reference model for controlling not only whether AI is compliant, but whether AI usage is economically defensible during live operation. As enterprise AI moves from isolated prompts to copilots, multi-step workflows and autonomous agents, every model call, context window, agent loop, tool execution and generated output creates variable cost, operational dependency and accountability exposure. The model defines seven interlocking governance layers: AI Usage Classification, Total Cost of Inference Exposure Mapping, Model Right-Sizing Governance, Runtime Budget Governance, Value-per-Token Assessment, Runtime Monitoring & Escalation, and Evidence & Board Reporting. These layers connect AI usage to cost, risk, value, accountability and evidence. The model is positioned above tooling and measurement disciplines such as FinOps and emerging tokenomics practices. It consumes their data as input and converts it into governance decisions, accountability structures and board-oriented evidence that can be defended in front of the CFO, the board, auditors and regulators. AIGN Runtime Economic Governance is published as a formal reference model extension to AIGN OS 4.0 — The Operating System for AI Governance — and is designed for direct anchoring in regulatory and normative contexts including the EU AI Act, DORA and ISO/IEC 42001. The central question the model answers is: Which AI usage is worth running, scaling and defending — and can the organization prove it?
Patrick Upmann (Thu,) studied this question.