Is Your FinOps Strategy Ready for the Cost of AI?

As AI adoption grows, organisations face new challenges around cost visibility, consumption and accountability. Explore how FinOps can help manage AI spend and support better financial decisions.

AI is adding another layer of complexity to cloud financial management. Alongside compute, storage and other cloud services, organisations increasingly need to understand the cost of AI models, token consumption, applications and agents.

The technology may be different, but many of the financial management challenges are familiar. Organisations still need visibility into what they are consuming, clear ownership of that consumption and the ability to connect technology costs with business value. As AI adoption grows, these FinOps fundamentals will become even more important.

AI Introduces New Cost Decisions

One emerging challenge is AI model selection. The FinOps Foundation has highlighted the risk of “over-modeling”, where teams default to highly capable and more expensive models even when a workload may not require them.

There are similarities with cloud over-provisioning. FinOps has long helped organisations identify oversized or inefficient cloud resources. AI introduces a similar question: are organisations using the right level of capability for the workload?

The answer is not simply to choose the cheapest model. Performance, quality, risk and technical requirements all matter. However, FinOps can provide the cost visibility needed to make those trade-offs more informed. Understanding which models are being used, what is driving consumption and what individual workloads cost gives organisations a better foundation for optimisation.

Visibility and Ownership Matter

This is where AI fits into a wider FinOps strategy. As environments become more complex, fragmented ownership and limited cost visibility can make it difficult to understand where spend is going and who is responsible for it.

AI could make that challenge greater. Consumption may come from developers using APIs, employees accessing AI capabilities through applications or agents carrying out automated tasks. Without effective allocation and governance, costs can quickly become disconnected from the teams and business activities creating them.

The industry is already responding. The FOCUS 1.5 working draft includes greater support for AI cost data, including identifying which AI model was billed, while further work is progressing around token pricing and attribution. Better data is an important step, but organisations still need the governance and accountability to act on it.

FinOps Is About More Than Reducing Cost

The goal should not be to minimise AI consumption simply because it creates additional spend. As with cloud more broadly, higher consumption may be justified where it is delivering measurable business value.

Effective FinOps helps organisations understand that relationship. It brings together cost, usage and accountability so finance, technology and business teams can make better-informed decisions about where investment should continue, where optimisation is needed and where greater control should be introduced.

For AI, this could mean understanding whether a different model could deliver the required outcome more efficiently, identifying which teams own growing consumption or improving forecasting as successful AI workloads scale.

Building AI Into Your FinOps Approach

Organisations do not need a completely separate financial management model for AI. Instead, AI consumption should become part of the wider visibility, governance and accountability already being established across cloud environments.

At ITAA, our FinOps specialists help organisations understand where cloud spend is occurring, what is driving it and who owns it. By combining FinOps expertise with cloud, software licensing and ITAM knowledge, we help organisations identify optimisation opportunities, strengthen financial accountability and improve commercial decision-making.

About the Author

Stephen Narey is a veteran Software Asset Management (SAM) specialist and Service Director at ITAA, bringing over 20 years of multidisciplinary leadership to corporate technology strategy. As a trusted advisor to C-suite executives and procurement leaders across highly regulated sectors like banking, insurance, and energy, he transforms complex, opaque software licensing liabilities into predictable, optimised business assets. Steve possesses deep, expert-level technical knowledge across major software publishers, including Oracle, IBM, SAP, FinOps, and Microsoft and has a proven track record of safeguarding global enterprises from predatory vendor audits while unlocking millions in hidden operational savings.

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