AI Cost Management - The Value Proof Problem

You can spend a fortune on AI - or any tech enablement project - and still fail the only test that matters. That’s the feedback from last month’s FinOps Summit, where 38% of attendees said their biggest challenge is not cost. It is proving that the hassle of implementation is actually worth it.

The awkward bit is usually answering the question of “why?”. When it comes to AI, most waste isnt down to GPU prices or runaway tokens. It’s misapplied use cases. Teams are pointing large language models at problems a rules engine, classical NLP or a plain API would solve faster, cheaper and with fewer surprises.

If you want proof of value, start where the accountants live. Name one business outcome you will move in this quarter and by how much. Tie it to money or risk. Miss that step and you’re already on dodgy ground. “Why are we doing this?”.

Then play devil’s advocate. Price the boring alternative before you touch a model or new product. If a queue plus a lookup table clears the backlog, say so. If a Python script beats your “agentic swarm”, use it. I like shiny as much as the next magpie, but simplicity compounds, and complexity invoices.

Total cost of ownership is not just the cloud bill, either. Add retraining, prompt iteration, eval harnesses, human review, incident response for hallucinations, vendor lock-in, data movement, and the people required to wrangle stuff out of it all. If those don’t fit on a single slide with five numbers, you’re not ready to ask for money.

In practice, a 90-day proof of concept looks simple. Day 0, baseline the metric you care about. By week two, run a shadow test against real work. By week six, show an A versus B with a credible control. By day 90, publish a before and after with cash impact and a clear call on whether the simple alternative would have beaten it. If you can’t do that, stop.

The teams getting AI right are dull in the best way. They ask “should we?” before “can we?”. They run small, instrumented experiments. They make it routine to say no when the numbers don’t clear the bar.

Deploying is easy. Justification and measuring return is the harder work.

There’s a lot to go at from a pure cloud cost perspective - but that’s irrelevant if you’re just going to spend those savings, plus a lot more, on the next shiny project that delivers no real value.