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

Like all execs, CIOs must make tough choices.

Brian Fruh, CIO at CTI Meeting Technology, has encountered many of them over the course of his career: Should he go all-in with innovation or favor a stable environment? Should his team favor speed over security in its work? What’s the right investment split between transformation, modernization, and cost optimization?

And Fruh has had to determine at various times the right trade-off in each case, deciding based on factors such as the company objectives, priorities, and obligations.

But making such calls has become more fraught today with higher stakes involved, he says.

“These are longstanding CIO challenges, but AI and cybersecurity have intensified them,” he explains. “Businesses today expect technology to move at unprecedented speed. At the same time regulatory requirements, cyber threats, and operational complexity continue to grow. The rise of AI has added another dimension, creating pressure to innovate quickly while ensuring governance, security, and responsible deployment.”

Making the right call isn’t easy.

“The challenge is that every stakeholder is typically optimizing for a legitimate business objective,” Fruh notes. “Business leaders often see opportunities to improve growth, client experience, or operational efficiency and want to move quickly. Technology and security teams understand the underlying complexity, integration requirements, support implications, and risks that come with those decisions.”

The trade-offs that Fruh highlights are among many that tech execs now face. Here, IT leaders delve into six of the most pressing trade-offs in their trade today.

Investments in the IT foundation vs. investments in growth

CIOs who have enough money to pay for everything they want to pursue are hard to find, which leaves most — if not all — CIOs making trade-offs on where their funds will go.

Kathy Kay, executive vice president and CIO of Principal Financial Group, is one such CIO, saying she contends with “the ongoing need to balance foundational investments with investments that drive growth.”

Kay acknowledges that this isn’t a new challenge for IT leaders but notes that “AI and the evolving risk landscape have raised the stakes.”

“What we consider table stakes, particularly around resilience, security, data, and governance, continues to expand,” she says. “At the same time we’re seeing meaningful productivity gains from AI across engineering, operations, and employee workflows. The focus now is on making deliberate decisions about how we prioritize our time, talent, and investments so we’re strengthening the foundation while accelerating value creation and differentiation.”

Similarly, Marc Tanowitz, managing partner for advisory and transformation at consultancy West Monroe, says CIOs are constantly trying to get the optimal balance between lights-on spending and spending on transformation projects, which today mostly means cutting corners elsewhere in favor of AI initiatives.

Skimping too much on either side can put an organization at risk. Underfunding IT operations could ding IT and, thus, organizational resilience, while underfunding transformation could hurt organizational competitiveness and survival.

“That’s the big challenge that CIOs are dealing with right now,” Tanowitz says.

Innovation vs. operational resilience

CIOs also say they’re making trade-offs when it comes to innovation and operational resilience.

“This is probably the most visible trade-off facing CIOs today,” says Joshua Bellendir, a longtime technology executive who most recently served as CIO at WHSmith North America.

“Every business wants to innovate. Executives want new digital capabilities, better customer experiences, more automation, and faster access to data. At the same time customers and employees expect technology to work flawlessly every day,” Bellendir says.

Sometimes, though, advances in one area can impact the speed, scale, or quality of the other side.

“The challenge is that innovation inherently introduces change, and change introduces risk,” Bellendir observes. “If your systems are unstable, innovation slows because the business loses confidence. Conversely, if you avoid modernization entirely, resilience eventually suffers because aging platforms become harder to support, secure, and scale.”

To contend with that, he treats both sides as interdependent rather than competing priorities.

“My approach has always been to create room for innovation while maintaining disciplined production standards. Pilot quickly, learn quickly, but be deliberate about what moves into enterprise-scale operations,” he explains. “During my time as CIO at WHSmith North America, we modernized several core retail platforms, including migrating major merchandising and retail systems to cloud-based platforms while also upgrading our store technology environment. These initiatives were critical to positioning the company for future growth, but they also required balancing modernization efforts against the need to maintain reliable day-to-day operations across a large retail footprint, including stores located in some of the busiest transportation hubs in North America.”

Innovation vs. risk management

Similarly, CIOs say they often must balance innovation and risk management, a task that also can mean trade-offs on one side or the other, or both.

Kay says “balancing business impact through innovation with risk management as AI becomes embedded across more parts of the business” is one of the most pressing issues she and other CIOs face right now.

“Organizations want to move quickly to capture value, while also maintaining the transparency, accountability, and oversight that customers, regulators, and stakeholders expect,” she explains.

It’s not a new dynamic, but it has become more acute due to AI, according to Kay.

“Technology leaders have always balanced speed with control, but AI has increased both the pace and the scale of that challenge,” she says. “At Principal, we have more than 100 AI use cases deployed or in development across the enterprise, which creates tremendous opportunity alongside growing expectations around privacy, security, explainability, and accountability. The efficiency-versus-transformation trade-off has also become more relevant as organizations move beyond experimentation and focus on value realization.”

Similar to Bellendir’s approach, Kay’s solution isn’t to lean all-in on one side or the other but to “hold multiple priorities at once.”

“The same work that improves customer experience and efficiency also needs to be built with transparency, accountability, and discipline from the start,” she explains. “It’s not about one group moving faster and another slowing things down. It’s about integrating those perspectives so we can move with both speed and discipline.”

Sean Searby, executive vice president and chief information and operations officer at Amalgamated Bank, shares a similar take.

“There is a delicate balance between building innovative capabilities and keeping the institution safe,” he says. “CIOs are thinking about keeping up with where their industry is going but doing it prudently.”

Searby believes every CIO must balance those two sides based on their own organization’s circumstances.

“You’re seeing a variety of positions and points of view, because so much of that depends on what you do, what information you have, regulations, your value statement, and your mission,” he explains. “I don’t think anyone should look at this as binary. It’s use case by use case, and everyone should make the decision based on what’s right for their business.”

Speed vs. organizational readiness

The desire for speed can be tempered by an organization’s readiness to move fast.

Kim Basile, CIO of IT infrastructure services provider Kyndryl, says that’s a common consideration today given rapidly evolving technology. “Every day we wake up and something has changed. So you need to be able to pivot and move at the same pace that the technology is moving at,” she says.

That, though, can be challenging for employees and the overall organization — something that may leave CIOs having to adjust the pace enough to allow teams to catch up.

Basile says she’s cognizant of worker skill levels and where education and training are needed to keep the gap between speed and readiness to a minimum. She also sometimes opts for controlled rollouts of innovation, bringing new technologies first to those teams that are ready to use them right away and then upskilling others to ready them for later deployments. And she tries AI tools in the company’s new “garage lab,” where experimenting with them can happen quickly while allowing more time to ensure they have the security and guardrails needed for companywide use.

Basile credits these options for helping IT “make sure we’re not holding things up.”

Data accessibility vs. data protection

Here’s another tightrope that CIOs must walk, more so in the data-hungry AI era than ever before.

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.”