Why AI Economics Needs To Be Included In The Equation

Alec Scott is a Principal at CDW for Intelligent Platforms.

The FinOps Foundation’s 2026 “State of FinOps” report shows that 98% of FinOps practitioners now include managing AI spend among their responsibilities. But cost visibility, allocation and determining value are common challenges when it comes to AI usage.

The operating model I use for enterprise AI starts with assigning the CFO three responsibilities: cost attribution, return expectations and scale economics. That changes the focus from how much the platform cost to what each capability cost, who benefited and whether it’s worth scaling.

Give Every AI Capability A Financial Identity

Best practice already calls for AI agents to have identities so that roles, permissions, ownership and accountability can be assigned and governed. I believe the same discipline should extend to financial management.

Before an agent or AI service reaches production, require a record that identifies its owner, business workflow, funding source and the business unit or cost center responsible for its use. If one capability supports several functions, define how its shared cost will be allocated before deployment.

Start by establishing an enterprise-wide financial taxonomy for AI spend. Finance and technology should agree on categories before creating tags, including model consumption, agent or application services, infrastructure and compute, data and retrieval services, platforms, software licenses and implementation or operating expenses. The taxonomy should also include attributions such as AI capability, business unit, cost center, workflow, funding owner and provider.

Tags then apply that taxonomy to financial records. Carry them through purchasing, cloud billing, vendor usage, invoices, accounts payable, internal allocations and financial reporting. Include the agent or capability ID so finance can trace spend to what consumed it.