A certification focused on the value of AI investments
I have earned the FinOps Foundation’s FinOps Certified: AI Value certification. Its scope covers the full lifecycle of an AI investment, from strategy to value realization. The certification verification link is available with the references at the end of the page.
The investment question is the one I want to retain in discussions with teams: what use are we funding, what outcome do we expect and how will we know it justifies the spending? An invoice tracks the amount consumed. It cannot answer these questions alone.
Cost per token remains useful for understanding rates and volumes. It needs to be connected to what the service produces. A lower unit rate can be offset by more calls or by work that fails to produce an accepted outcome.
Define a useful outcome for each use
A useful outcome may be a case resolved, a transaction completed or a task successfully performed. The unit needs to match the work expected by the business. Its definition should distinguish an interaction with a model from a validated outcome.
Calculating the cost of that outcome requires linking consumption data to business events and specifying the scope. Different uses do not necessarily produce the same value, even when they use the same model. Combining them into an average cost may hide those differences.
Some exploratory work does not immediately produce a measurable outcome. Its maturity and experimental objective then need to be explicit. This allows a discussion of what you want to learn without presenting a value hypothesis as a benefit already realized.
Set spending boundaries during experimentation
Spending guardrails need to help teams experiment within an understood scope. An overly rigid control may delay a useful trial. A lack of tracking makes it difficult to explain the bill and decisions made during that trial.
Possible approaches include setting a budget per team or use case, providing alerts and scheduling review points. The rules should specify who can decide to continue, adjust the scope or stop the experiment.
These suggestions need adapting to the context. The level of control depends on committed spending, service maturity and funder expectations. Consumption needs to remain visible during trials to avoid discovering its scale only at the end.
Document quality, latency and cost trade-offs
Expected quality, acceptable latency and cost need to be examined together. A quickly delivered result will not be useful if it fails to meet the need. Better quality may also require more time or resources: its value needs assessing on the relevant use.
Teams need shared criteria for choosing. A transactional process and a creative assistant may have different requirements. Applying the same trade-off to every service risks making some too expensive or insufficient for their task.
Documenting choices and measurements preserves team autonomy while making decisions understandable. Stakeholders can then discuss a change in model, timing or quality level together with its consequences for the expected outcome.
Review the investment after deployment
To start, I suggest selecting a use case, identifying its useful outcome and specifying the expected value. Then check whether available measurements can connect spending to that outcome. Gaps show which data to collect before drawing conclusions about returns.
Schedule reviews with technical teams, FinOps and business owners. Compare observed outcomes with the initial assumptions, including quality, time and cost. The discussion needs to support deciding whether to expand, adjust or stop the service.
The certification supports this lifecycle view; it does not replace analysis specific to each organization. The investment case needs to evolve with usage and observed outcomes, including when they fail to confirm the expected value.
The post behind this insight
Expanded from the LinkedIn post. The links below come from the original post; listing them does not imply independent verification.
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