As enterprises move from AI copilots to embedded workflows and autonomous agents, the cost model is changing underneath them. A single user request can now trigger dozens — sometimes hundreds — of model calls, retrievals, retries, and orchestration steps. A tool that looked affordable in a pilot can behave very differently in production. In a new feature on CIO.com, ModelOp CTO Jim Olsen joins other enterprise technology leaders to explain how CIOs can get ahead of runaway AI spend — and why the harder problem isn't the bill at all.
Use the right model for the job
One of the fastest ways enterprises overspend, Jim explains, is defaulting every task to the most powerful model available. Many workloads don't need frontier-level reasoning, and paying for it anyway is pure waste.
"It's like hiring the most expensive engineer to change a few colors in a website's CSS, or visual styling. You wouldn't do that. You use the appropriate tools for the task."
— Jim Olsen, CTO, ModelOp
The discipline of routing work to the least expensive model that can still accomplish the business goal is one of the clearest levers CIOs have to bring costs down without sacrificing outcomes.
The deeper problem: value shock, not bill shock
For Jim, the bigger issue facing enterprises isn't the size of the AI bill — it's whether that spend is actually returning value. Spending $200,000 in a quarter is easy to justify if it produces $2 million in business value. The danger is pouring money into use cases that don't deliver a meaningful return.
"Are you actually getting that return on investment, or are you just blowing tokens for something that's not delivering the value to your business?"
— Jim Olsen, CTO, ModelOp
As Jim puts it in the article, the question isn't whether someone used a million tokens. It's what they used them for.
You can't connect cost to value without an AI inventory
Tracking token usage by user or department shows who consumed AI — but not whether that consumption mattered. The same underlying models and agents might power HR document search, customer support, and code review, each with very different business value.
That's why Jim argues enterprises need an AI inventory: a record of which business workflows use which models, agents, providers, and systems. Without that system of record, organizations simply can't tie consumption back to the business use cases it serves — and can't tell which AI spend deserves to scale.
Why this matters
Jim's perspective in CIO.com reflects the core of what ModelOp delivers for enterprise AI leaders. Managing AI cost, value, and risk at scale isn't a dashboard problem or an accounting exercise after the money's already gone — it requires a system of record that connects every model, agent, and workflow to the business outcome it's meant to produce. That's how CIOs move from reacting to AI bills to actively governing AI value.
