How CIOs can get a better handle on budgets as AI spend soars

Gartner predicts global AI spending will hit $2 trillion in 2026, up from $1.5 trillion this year. And In a survey of over 300 executives at large companies by management consulting firm West Monroe Partners, 85% said they plan to increase IT budgets next year, with a big chunk going to AI. For 42% of executives, scaling AI and data capabilities is the top priority for technology investment, and 91% said AI is causing their tech spend to increase while nearly three quarters plan to spend more on contractors as a result of AI.

In the previous couple of years, many companies were doing POCs, just figuring out what AI can do, says Bret Greenstein, CAIO at West Monroe. But that’s all changing now. “I see a lot less discussion of use cases and POCs, and more about phase one, phase two projects,” he says.

It’s not as hard to assess whether or not AI can do something anymore, he adds. “I can look at something and say this is highly addressable by AI.” But that doesn’t mean CIOs get carte blanche to spend all they want.

At the Principal Financial Group, a global investment and insurance company, the focus is now on delivering measurable business value, says Rajesh Arora, the company’s chief data and analytics officer.

“We’re reallocating budgets toward scalable platforms and high-impact use cases,” he says. Plus, the firm is implementing rigorous ROI tracking and cost governance. That’s because the firm is moving past experimental pilots, he says. In addition to looking for platforms that can scale, the company is also looking at lifecycle management tools, data foundations, and operational AI capabilities.

“These are solutions that’ll automate processes, enhance customer experience, build new capabilities, and strengthen risk management,” he says. “Our goal is to make every dollar work harder.” And that means some things have to go.

The company is pausing low-impact investments, for example, in favor of high-value use cases. And they’re tightening up their contract governance and renegotiating terms. There’s also automation. “We’re deploying cost-alerting for LLM ops and feature story versioning to flag anomalies and prevent overruns,” he says.

LLMs can produce different results for the same output, and different versions of the model can have very different performance metrics and costs. And feature story versioning tracks software changes, as well as the model, data, and prompts used. So it all comes down to AI becoming a strategic focus to manage costs.

Arora’s experience isn’t unique. Enterprises of all sizes and in all verticals are grappling with their AI spending as they move on from POCs to actual deployment at scale, which often means facing new demands for ROI, shifting money from legacy to AI projects, and struggling to get a handle on technical debt.

The push for proof

To prove its AI investments are worth the dough, Principal tracks efficiency gains, reductions in risk, improved customer satisfaction, and better employee experience. This creates a holistic view of the value that AI creates, says Arora.

“Our approach is to maintain a balanced portfolio,” he says. That means both short-term wins that build momentum, and long-term innovation to drive strategic advantage and growth. “As AI capabilities mature, we must be more intentional about how we define success and ensure long-term sustainability,” he adds.

Tech executives at smaller companies are also having to show results from their AI projects. JBGoodwin Realtors, with four offices in Austin and San Antonio, has 800 agents, partners, and employees, and everyone is all-in on AI, says Edward Tull, the company’s VP of technology and operations.

“The CEO uses it every day,” he says. “All the agents use it, too, and we have approval to spend more.” But he has to show ROI. “I have to prove it,” he adds. “I spend a little, prove the use case, and then I get a little more and spend a little more.” So for example, AI might result in better efficiency so to demonstrate this, he might run two processes in parallel, one the old-fashioned way, and the other with AI.

Focusing on AI projects that result in cost savings is a good way to show results and build momentum, agrees Gartner analyst Melanie Freeze. “We know that can lead to other non-cost considerations and long-term value.” For example, in infrastructure and operations, likely wins include cloud cost management, IT service support, and general employee productivity, she says.

“You can get cost optimization, but also all that other value like innovation, efficiency, optimizing talent management,” she says.

A shift in priorities

Another way to pay for AI projects, especially experimental ones that don’t yet have clear ROI, is to take money from other areas. JBGoodwin’s Tull says he does that. “I’ll get rid of other things we spend on, to offset what I spend on AI,” he says.

Everyone wants to become AI-centric or AI-native, says West Monroe’s Greenstein. “But nobody has extra buckets of money to do this unless it’s existential to their company,” he says. So moving money from legacy projects to AI is a popular strategy.

“It’s a shift of priorities within companies,” he says. “They look at their investments and ask how many are no longer needed because of AI, or how many can be done with AI. Plus, they’re putting pressure on vendors to drive down costs. They’re definitely squeezing existing suppliers.”

Even large, tech-forward companies might have to do this kind of juggling.

“We didn’t create a whole new allocation for AI,” says one senior tech executive at a Fortune 500 insurance company. “We’re still working through the mechanics of budgeting for AI.”

Instead, the firm is carving out funds from other areas.

“AI is in a self-funding model at the moment,” he says. “We’re shifting investment from legacy technologies to AI.” For example, he says, if the company was spending a million dollars on a particular technology and used automation to get it down to $900,000 a year, the $100,000 savings could go toward AI.

And sometimes the company can get new AI for free, he says, as vendors add AI functionality or agentic capabilities to existing products. But other platforms charge extra for the new features. “Some of it is inherent in the solution, though, and doesn’t really change the cost,” he says. That might evolve to new funding in 2026 to 2027, he adds. But as the company’s use of AI continues to mature, the funding model will evolve as well, he says.

“We’ll see that change as we demonstrate capabilities that either deliver high business value or efficiency gains,” he says. “Then we’ll shift to additional infusions of investment to accelerate things.”

Planning for the unexpected

Budgeting for IT projects has never been simple, but AI adds its own challenges. The unprecedented pace of change is one of them.

“Whatever modeling I do now is not going to be valid in six months,” says Sheldon Monteiro, chief product officer at Publicis Sapient. This isn’t always a bad thing. For example, the per token prices of some models have dropped dramatically over the past two years, he says. But on the flipside, there are always newer and better models, growing usage, and unpredictable performance.

“With traditional software economics, you have upfront costs like development, engineering, or infrastructure, but once you have those fixed costs, operating costs are relatively predictable and manageable,” he says. With AI, though, the inference costs are variable, and the guardrail and compliance checks might have additional costs, he says. Scaling is also non-linear and the tech itself is in constant flux.

“You need to be able to flex,” says Monteiro, “And to recognize that now, winners and losers are hard to call.”

Another challenge to budgeting is the demands that AI places on people, systems, and data. One of the most significant challenges to managing AI costs is talent, says Principal’s Arora. “Skill gaps and cross-team dependencies can slow deliveries and drive up costs,” he says.

Then there’s the problem of evolving regulations, and the need to continuously adapt governance frameworks to stay resilient in the face of these changes. Organizations also often underestimate how much money will be needed to train employees, and to bring data and other foundational systems in line with what’s needed for AI.

“Legacy environments add complexity and expense,” he adds. “These one-time costs are heavy but essential to avoid long-term inefficiencies.”

Finally, when AI technology is actually moved out of POCs into production, it often turns out very different to what companies expected.

“There are so many unknowns right now,” says Karen Panetta, IEEE fellow and dean of graduate engineering at Tufts University. “People think of it as a replacement for a human, and it’s not. And you get new areas you haven’t had to worry about before.” For example, many companies look to use AI agents to replace customer service or support teams.

“It’s really appealing,” she says. “You’ve got 10 people answering phone calls now, and it feels like AI is going to do the job of those ten people. But I’ve designed it for normal process flows, so what about all the exceptions? Now you have angry customers, or it breaks and is unavailable. And what about security? Before, we had humans to detect these things.”

CIOs have to be thoughtful about what they’re doing with the AI and why, she says.

Many CIOs have already transitioned from managing costs and risks, to managing data and becoming enablers of insight, and getting closer to the business units. Now they’re in a position to become enablers of AI, while doing it safely and at cost.

“There are some CIOs that blocked and firewalled every AI tool the day it came out,” says West Monroe’s Greenstein. “That blocked companies from adoption. The ones who are progressive are being thoughtful, deliberate, are building governance models, and creating a new enterprise architecture around AI. The CIOs who are embracing that are enabling the enterprises of tomorrow.”