The company spent two years bringing the AI pilot into production. Now, the deadline for the bill is approaching, and for many CIOs, it’s not enough.
Poorly managed co-pilots, redundant SaaS AI capabilities built into every tool in the stack, semi-secure chatbots and barely moving automation are quietly ballooning cloud, licensing, and labor costs far beyond budget. What is the ROI that is supposed to justify everything? It’s mostly missing in the action.
The time has passed for the growing number of IT leaders to be weeded out. Not to retreat from AI, but to reduce unnecessary burdens, free up budgets for the AI that actually makes a living, and save those savings to build a blend of smarter, more profitable tools in the future. The trick is to reduce AI waste without draining your business. It requires a wise and well-executed exit strategy.
“The AI exit strategy is not a retreat from AI, but rather a maturation stage that will separate companies that increase the value of AI over the next decade from those that continue to pour money into sprawl.” Dr. Kaushal Kulkarniassociate part-time surgeon at Mount Sinai’s New York Eye and Ear Infirmary, and co-founder and chief medical officer of Predoc, a company dedicated to connecting and organizing medical data across the United States.
How to judge the waste of AI
which one to decide AI tools, models, and projects to cut It’s an inaccurate exercise at best. Most companies “don’t have any evaluation criteria in place,” Kulkarni said. Instead, they “bought AI on faith and are now trying to grade work they never defined.”
However, all is not lost, as there are now ways to develop criteria for culling decisions. Pragati AwasthiAssistant Professor of AI and Data Science at Drexel University, a global R1-level research university, suggests that CIOs ask three questions about each AI tool, model, or project they are evaluating.
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Is it in production or is it still a pilot?
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Does it have measurable business metrics associated with it?
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Has anyone actually changed the way they work because of this?
“If you can’t answer yes to all three, you’re on the withdrawal list,” Awasthi said.
But don’t stop there. Let’s dig into the details.
“Technically, look at inference costs per completed task, model error rates for production and integrated debt. On the business side, compare the actual time savings and revenue impact to licensing and cloud spend,” Awasthi said.
Once you have evaluated these closely, look diligently for any hidden costs involved.
the biggest The hidden costs of enterprise AI The tool itself is rarely used. Jackie SwansonManaging Partner of Gartner Consulting. “Each new AI surface adds security reviews, integration efforts, and governance overhead to an already expanded stack,” she said.
Costs you probably haven’t calculated
Once you find them, take another look. There are almost certainly AI costs that have not yet been identified and are not properly accounted for in expenditures. Most companies are “paying for AI in places that don’t count as AI spending,” he said. Frank MeltkeCEO of Contraco, a global digital transformation consulting firm
“All SaaS products with co-pilot or assistant functionality have AI costs added to the per-seat license. When CIOs take stock of their AI spending, they typically find that the inclusion of AI capabilities built into existing software subscriptions typically increases by 40% to 60% over the original numbers,” Meltke said.
The AI exit problem most companies face is “fundamentally not a project problem,” Swanson said, so be careful when starting to cull based on use case.
Rather, she said the problem goes back to department-level procurement and operating model decisions, AI in SaaS vendor bundles shoehorned into existing contracts, and cumulative spending without clear ownership.
“Exit strategies that start at the use case level miss most of the real cost drivers,” Swanson said.
As a final cost check in deciding to cut back on a particular AI tool, model, or project, compare the cost of AI to the cost of reasonable and available alternatives, such as other forms of analytics, automation, or employees.
“Costs beyond replacement labor are a math problem disguised as transformation.” Diptamay SanyalCrowdStrike’s chief engineer.
AI costs are outweighing employee costs, a hard fact that several companies, including Microsoft, Nvidia, and Uber, have recently faced.
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Nvidia acknowledged the following: cost of computing Because AI far outweighs the cost of employees.
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Uber provided most notable example: The company has used its entire 2026 AI budget by April. We are currently testing additional coding models for agent-driven development.
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Microsoft is The most direct corrective actionreportedly canceled most of its direct-to-clod code licenses just six months after deploying the tool and directed its engineers to the GitHub Copilot CLI instead.
What does a successful AI exit strategy look like?
The important thing to remember is that Reduce the number of AI tools Usage is not the end goal.
Swanson said the pattern across large companies is “consolidation, not exit,” and cited two industry examples.
retail. Starting with 14 AI initiatives spread across business units, the retailer has three platform-level capabilities tied to measurable P&L impact. As a result, free budget was redirected to a single AI platform team that runs AI Survivor with true discipline.
Banking business. Another successful example of an AI exit strategy she provided was a similarly situated bank that cut six out of nine first officers and retained three due to documented productivity gains. The savings were used to fund governance and security efforts that were omitted in the first wave.
“In most of these departures, the clarity of ownership on the other side is more important than the total amount saved,” Swanson said.
Examples of successful AI exit strategies have also emerged from other sources.
Meltke gave the example of a mid-sized financial services company that conducted a structured AI portfolio review over the course of one quarter. In this review, employees cataloged all AI-enabled features, SaaS tools with AI components, and internal automation around customer data.
Of the 34 AI items identified in the portfolio, he said:
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Eleven had no owners recorded.
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8 had never been formally assessed for data processing compliance.
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6 had features that overlapped with tools the company was already paying for.
“We didn’t cancel everything; we designated ownership, defined success metrics, and documented and integrated data flows into 19 tools,” Meltke said. “Annual spending is down about 35%, and security teams finally have full visibility into what is actually being done.”
He said the key elements to make it work are:
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Executive sponsorship makes the team irresistible to the inventory process.
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Two-step termination sequence (pause and evaluate before termination)
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A commitment to documenting learnings rather than simply reducing costs. “That document served as the basis for a more prudent procurement next time,” Meltke added.
Ultimately, whether the AI exit will be successful or not is clear in both observations and numbers.
“Dependencies were documented, data was inventoried and deleted, and users were migrated without loss of productivity. Costs were noticeably lower and the team learned lessons for their next investment. The success of the exit was not dramatic; it was non-disruptive,” Sanyal explained.
