CFO lens
Start with proof, capital discipline, and economic accountability.
Use the value thesis, maturity model, and ROI framework to test whether AI spend is becoming economically legible.
Vendor-neutral editorial
Most organisations cannot prove their AI is paying for itself.
AI Economics Hub is a vendor-neutral publication for senior leaders who need clearer language for AI economics — from value proof and TCO to FinOps, TBM, ITFM, and portfolio governance.
The question is no longer whether AI is capable. It is whether organisations can explain what they are spending, who owns the economics, and what value is being proved.
Core reading
The essential frameworks for enterprise AI economics
Flagship essay
The AI Value Gap
The central thesis: most organisations still cannot prove whether AI is delivering economic value, because visibility, accountability, and proof remain fragmented.
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Signature framework
AI Value Management Layers
A governance stack for connecting AI usage, output quality, workflow impact, portfolio decisions, and personal accountability.
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Signature framework
AI Economics Maturity Model
A maturity model for moving from fragmented AI activity to governed, provable, portfolio-level AI value.
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Core framework
AI TCO Framework
A seven-layer cost model for understanding what AI really costs once orchestration, governance, and support are included.
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Flagship reference
AI Economics KPIs
A filterable library of 30 KPIs for governing AI cost, value, accountability, portfolio performance, and financial planning.
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Industry perspectives
The AI economics debate: no consensus yet
Optimist·McKinsey Global Institute
“$2.6–4.4 trillion in annual value from generative AI across 63 use cases.”
2023 analysis of generative AI economic potential
Skeptic·Goldman Sachs Research
“AI is best at doing cheap tasks better. The economic return from cheap tasks is inherently limited.”
Jim Covello, 2024 — questioning whether ROI justifies $1T+ investment
Nuanced·FinOps Foundation
“AI spend is following the same arc as cloud — and organisations are just as unprepared.”
Drawing explicit parallels to the 2014–2018 cloud cost governance crisis
This debate matters for practitioners now. The absence of consensus is not a reason to wait — it is a reason to build governance before the market decides for you.
Latest articles
Recent analysis for finance, technology, and operating-model leaders
28 May 2026
Agentic AI Economics: Why Your Existing Frameworks Are Already Obsolete
Every AI economics framework in use today was built on a shared assumption — a human initiates a task, an AI model assists. Agentic systems break that assumption. The economic implications are not incremental. They are structural.
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28 May 2026
AI Economics in Regulated Industries: Why the Standard Framework Breaks
The AI economics frameworks used in most enterprise conversations were built for tech-company conditions. In financial services, healthcare, and regulated government, the economics are structurally different — and the standard approach systematically underestimates cost and overestimates return.
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28 May 2026
The Spell-Checker for Thinking: Personal AI Accountability for Knowledge Workers
We accepted the spell-checker trade-off without much debate. AI is now proposing the same deal for reasoning, judgment, and original thought. Whether to accept it — and on what terms — is a question worth being deliberate about.
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28 May 2026
What Boards and Audit Committees Should Actually Ask About AI
Most board conversations about AI are either too strategic or too tactical. The economic governance layer — capital discipline, value proof, and investment accountability — is largely missing. This article sets out what responsible oversight actually requires.
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28 May 2026
When to Stop: The AI Initiative Autopsy
Most organisations have detailed criteria for starting AI investments and almost none for stopping them. This is the most expensive gap in enterprise AI governance.
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Find your entry point
Different questions need different starting points
CIO lens
Treat AI as an operating model, not a collection of pilots.
Move from scattered pilots to a clearer view of shared platforms, governance burden, and portfolio consequence.
FinOps lens
Bring AI demand inside a stronger cost-governance loop.
Connect model usage, orchestration choices, and optimisation decisions back to allocation and unit economics.
Strategy & portfolio
Compare AI initiatives as a governed portfolio, not a queue of exceptions.
Use return timing, strategic fit, and scarce resource demand to decide what to fund, scale, redesign, or stop.
Editorial briefing
A quieter way to keep up with AI economics
The briefing focuses on durable frameworks, market structure, cost and value signals, and governance questions that are useful beyond the current news cycle.
Editorial briefing coming soon
The briefing will open once delivery infrastructure is connected. Until then, the articles library is the best way to follow new analysis.
Browse latest essays