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AI Economics Hub

Most organisations cannot prove their AI spend connects to value

This publication exists to close that gap with evidence, frameworks, and governance that would survive an audit.

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Flagship frameworks

Close the value gap

Framework

The AI Value Gap

Why most organisations can track AI cost precisely but cannot prove AI value—and what that means for governance, accountability, and portfolio decisions.

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Framework

AI TCO Framework

A seven-layer cost model for understanding what AI really costs once orchestration, governance, support, and hidden dependencies are included.

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Framework

Value Management Layers

A governance stack for connecting AI usage, output quality, workflow impact, portfolio decisions, and personal accountability across the enterprise.

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Framework

Five Levels of AI Economics

A maturity model for moving from fragmented AI activity to governed, provable, portfolio-level AI value that can be defended in an audit.

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Why the gap matters

Three statistics on the AI value gap: 39 percent report any EBIT impact, 6 percent see AI above 5 percent of EBIT, 44 percent fund AI from unrealised savings
Adoption is near-universal; provable enterprise return is not. McKinsey (39%/6%) and AIMG (79% no impact) studies use different measurement approaches but converge on the same pattern: most AI spend cannot be connected to proven business value.
Three statistics on the AI value gap: 39 percent report any EBIT impact, 6 percent see AI above 5 percent of EBIT, 44 percent fund AI from unrealised savings

Adoption is near-universal; provable enterprise return is not. McKinsey (39%/6%) and AIMG (79% no impact) studies use different measurement approaches but converge on the same pattern: most AI spend cannot be connected to proven business value.

Who this is for

Start with your role

Different roles need different entry points into AI economics. Choose your perspective.

CFO

Prove AI value, manage portfolio risk, and connect AI spend to business outcomes

Lead metric

Attribution coverage

CIO

Orchestrate AI value across the technology estate and operating model

Lead metric

Attribution coverage

CAIO

Build governance that scales from pilots to production AI

Lead metric

Attribution coverage

FinOps Leader

Extend FinOps practice to cover AI cost, usage, and value

Lead metric

Attribution coverage

TBM Practitioner

Integrate AI economics into TBM taxonomy and reporting

Lead metric

Attribution coverage

Strategy Leader

Connect AI investment decisions to portfolio value and risk

Lead metric

Attribution coverage

Site structure

How the site is organised

This is a guide to the argument, not a fixed sequence. Most readers will move back and forth between value, cost, governance, operating model and evidence.