What Cloud Taught Us About the Real Cost of AI Inference
Why enterprise inference bills land 30-50% above forecasts, the four cost mechanics that headline rates miss, and how CFOs and FinOps leaders should estimate fully loaded inference economics.
A Proof of Concept That Proves the Technology Has Proved Almost Nothing
Technical feasibility is not evidence of business value. An AI pilot must prove cost, workflow change, adoption, risk and value capture.
AI Value Management Is Not FinOps for AI
Why true AI Value Management sits closer to enterprise strategy, finance and operations than to technology cost management, and why its purpose is to turn AI capability into growth, operating leverage, protected earnings and strategic advantage.
Rent, Reserve or Own Intelligence?
A decision framework for API, committed capacity, neocloud, private and owned AI infrastructure.
The Behavioural P&L of AI
AI adoption changes trust, review, learning, workarounds and decision behaviour. Those effects create real economic assets and liabilities.
The CIO Can Orchestrate AI Value, but Cannot Own It
AI value needs a federated operating model. Technology leaders can orchestrate evidence and platforms, but business executives must own outcomes.
The End of the Software Seat
Agentic AI breaks the human-seat denominator. Enterprise software pricing is moving toward actions, workflows, capacity and outcomes.
The Token Is the Meter, Not the Value
Tokens make AI consumption measurable and priceable. They do not tell an organisation whether anything valuable happened.
Who Owns the Means of Intelligence?
AI economic power is distributed across energy, chips, data centres, models, orchestration, applications and enterprise demand.
What CFOs Should Ask of AI ROI Claims
Most AI ROI cases are structured to survive scrutiny rather than invite it. A genuinely credible AI business case looks different from what most organisations currently produce — and CFOs are the right people to demand the difference.
When AI Usage Outruns the Budget: What the Uber Story Teaches About AI Value Management
Uber spent its 2026 AI coding budget in four months. The COO couldn't prove the value. This case file analysis examines what went wrong and what AI Value Management would have done differently.
The First 30 Days of AI Value Management
A field sequence for starting AI Value Management in a mid-to-large organisation. Everything here is doable with finance extracts, vendor portals and a spreadsheet - no tooling purchase required in month one.
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.
The Inference Cost Crisis: What Every Enterprise AI Buyer Should Know
Why inference has become the primary economic bottleneck in enterprise AI, what could happen when pricing normalises, and how buyers should prepare now.
Why Cheaper AI Will Cost More
Falling token prices do not guarantee falling AI budgets. Cheaper intelligence expands demand, reasoning depth and agent activity.
Embedded AI, Hidden Tokens: Why SaaS Pricing Obscures AI Economics
How bundled AI features in SaaS subscriptions hide token consumption, create procurement blind spots, and prevent effective AI cost management. A guide for CFOs and procurement leaders.
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.
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.
Programmatic Access to AI Costs: A FinOps Practitioner's Billing API Guide
A practical reference for FinOps and platform teams on how to access AI cost data programmatically across the major model providers, hyperscalers, and coding tools — including what is available, what is not, and how to build a minimal cost aggregation pipeline.
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.
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.
AI TCO Worksheet: The Seven-Sheet Model
A working spreadsheet structure for pricing AI use cases from pilot through production. Designed so a finance analyst and platform engineer can fill it in together in an afternoon, with the pilot-to-production bridge most business cases skip.
Building an AI Business Case That Survives Board Scrutiny
A practical guide for CFOs and finance leaders on what a credible AI business case requires — and the most common ways they fail under scrutiny.
The Cost of AI Governance: When the Operating Model Consumes the Portfolio
AI governance frameworks are genuinely necessary. They are also genuinely expensive. The question of how much governance is proportionate — and at what point the operating model costs more than it saves — is one of the least examined in enterprise AI.
What AI Actually Costs: Reference Cost Ranges for Enterprise AI
Reference cost ranges across the main layers of enterprise AI spend — from inference and platform fees to governance overhead and integration engineering. A practical starting point for internal planning and benchmarking.
The Economics of AI Vendor Selection: Lock-in, Pricing Risk, and Exit Costs
How to evaluate AI vendors on economic and risk grounds — not just capability. Covers pricing model risk, lock-in dimensions, consolidation exposure, and what a vendor exit actually costs.
AI Economics for Mid-Market Companies: Why the Enterprise Playbook Doesn't Scale Down
The standard AI economics frameworks were built for enterprise conditions that mid-market companies do not share. This piece examines what changes — and what a more practical approach looks like.
AI Economics for the Engineering Leader
A practical guide for Heads of Engineering, platform leads, and CPOs on model selection economics, shift-left cost awareness, and build-versus-buy decisions in enterprise AI.
What Consulting Partners Need From AI Economics
How consulting partners and directors can use AI economics frameworks to improve client conversations, shape transformation programmes, and build more credible AI value-realisation work.
The CAIO's First 100 Days: An Economic Governance Playbook
A practical guide for newly appointed Chief AI Officers and VPs of AI on building visibility, proof standards, and cross-functional governance in the first hundred days.
Where AI TCO Models Fail
Why enterprise AI cost models often break down once shared platforms, governance overhead, and operating complexity move beyond the model invoice.
FinOps for Inference-Era Workloads
Why inference-heavy AI services require FinOps practices that extend beyond cloud billing into model behaviour, workflow design, and unit economics.
Enterprise AI Cost Basics
A practical primer on where enterprise AI costs accumulate and how leaders should think about them.