6 strategic trade-offs CIOs can’t afford to get wrong

Tom Armstrong, CIO of Southern Connecticut State University, knows this from experience.

As is the case in many organizations, Southern stores vast amounts of data — with much of it being sensitive, regulated data. And it wants to use that data for all sorts of business cases, including AI initiatives. So Armstrong must make that sensitive, regulated data available while also protecting and securing it. Like the other trade-offs, there’s not a lot of wiggle room here: implementing substandard security and privacy guardrails to enable availability would be problematic, but then again so would be limiting access to data.

Armstrong says this isn’t an either-or decision, it is a both with some tweaks on either side. To help enable data availability while minimizing risk, he turned to creating reusable data products, that is, self-contained, governed data assets that are easily discoverable.

“There’s this outdated idea that when someone requests access to something that the answer is yes or no, but that’s usually not the case. We can fulfill the need and still maintain security,” he says. “So it’s not about answering yes or no but figuring out how.”

AI use vs. its cost

Executives are experiencing sticker shock with their AI costs. For example, a December 2025 survey from research firm IDC found that 96% of organizations deploying generative AI and 92% implementing agentic AI reported costs higher or much higher than expected. The survey also found that 71% have little to no control over where those costs are coming from, resulting in IDC predicting that CIOs will underestimate AI costs by 30%.

“What we’re seeing is the consumption of tokens exceeding budget allocations,” says West Monroe’s Tanowitz. That has led some execs to ask their teams to “throttle down some of the models they’re using to lower levels that are less token heavy.”

Tanowitz says CIOs are still working through the best approach as those AI-related bills come in.

Some are working to mature FinOps for AI practices so they can do better at predicting and optimizing for cost. Others are developing strategies to right-size models to ensure the models deliver needed results but at a price point that doesn’t match or exceed the value delivered. Still others are focused on increasing the use of AI within their organizations and for now have accepted the higher-than-expected bills.

“The tradeoff is favoring the innovation side for now. That might tip toward the other side next year, though,” he says, predicting that 2027 will be “the year of AI cost optimization.”