2026: The year AI ROI gets real

AI initiatives by and large have fallen short of expectations.

That’s the conclusion of most research to date, including MIT’s The GenAI Divide: State of AI in Business 2025, which found a staggering 95% failure rate for enterprise generative AI projects, defined as not having shown measurable financial returns within six months.

Moreover, tolerance for poor returns is running out, as CEOs, boards, and investors are making it clear they want to see demonstrable ROI on AI initiatives.

According to Kyndryl’s 2025 Readiness Report, 61% of the 3,700 senior business leaders and decision-makers surveyed feel more pressure to prove ROI on their AI investments now versus a year ago.

And the Vision 2026 CEO and Investor Outlook Survey, from global CEO advisory firm Teneo, noted a similar trend, writing that “as efforts shift from hype to execution, businesses are under pressure to show ROI from rising AI spend,” noting that 53% of investors expect positive ROI in six months or less.

“There is pressure on CEOs and CIOs to deliver returns, and that pressure is going to continue, and with that pressure is the question, ‘How will you use AI to make the company better?’” says Neil Dhar, global managing partner at IBM Consulting.

Laying the foundation for success

Matt Marze, CIO of New York Life Group Benefit Solutions, is confident he can deliver AI ROI in 2026 because he’s been getting positive returns all along. The key? Pursuing and prioritizing AI deployments based on the anticipated value each will produce.

“We started our AI journey with a call to action in December 2023 by the CEO, and from the start we wanted to be a technology, data, and AI company to drive unparalleled experiences for our customers, partners, and employees. So all along the value question, the ROI was very top of mind,” Marze explains.

Marze and his executive colleagues approach AI investments “the same way we think about all our investments” — that is, considering how they’d impact the company’s earnings plan. “We look at operating expense reduction, margin improvement, top-line revenue growth, customer satisfaction, and client retention, but at the end of the day it boils down to our earnings contribution,” he says.

Marze highlights practices that keeps the organization focused on ROI, such as prioritizing AI initiatives for areas that are AI-ready in terms of available data, systems, and skills; using returns from those to fund subsequent initiatives; and designing AI systems in ways that allow for reusability so that subsequent projects can get off the ground more efficiently.

“We’re doing all that very strategically,” Marze says, explaining that this approach enables the organization to select AI projects where there are realistic expectations for ROI rather than merely hopes for vague improvements.

“We want to be nimble and move with urgency, but we also want to do things the right way. And because we fund our investments out of our P&L, we think about spending. We have that P&L mindset. We don’t like to waste money,” he adds.

Marze also credits the company’s ongoing commitment to modernization as helping ensure AI projects can deliver returns. “We built a foundation, and that put us in a good position to capitalize on AI,” he says. “There is a readiness component to leveraging AI effectively and to driving AI ROI. You have to have strategic data management, modernized computing, modernized apps, and cloud-native solutions to take advantage of AI.”

Marze expects those same disciplines and approaches to continue enabling him to pick AI initiatives that deliver measurable value for the organization as his company looks to reimagine work using AI and to bring full agentic solutions into its core processes.

The payback on the various proposals vary, he notes, and the anticipated timeline for payback for some can be a few years out, but he’s confident that the positive returns will be there.

Moving from elusive to realized ROI

Others are not as confident that their AI projects will deliver ROI — or at least ROI as quickly as some would like. Some 84% of CEOs predict that positive returns from new AI initiatives will take longer than six months to achieve, according to the Teneo report.

Their perspective may be colored by the past few years, when ROI has been elusive for many reasons, say researchers, analysts, and IT execs.

Many early AI initiatives were experiments and learning opportunities with little or no relevance to the business, says Bret Greenstein, CAIO at West Monroe. They often didn’t address the organization’s needs or goals and atrophied as a result. And even when the AI projects did address real pain points or business opportunities, they often failed to deliver value because the data or technology needed to scale wasn’t there or cost more to modernize than the anticipated ROI. And while some delivered modest gains or improved experience, they were either difficult to quantify or small enough to not move the needle.

“If you go back to the early days of the web and mobile, the same thing happened, before people learned there are new metrics that mattered. It just takes time to figure those out,” Greenstein says.

Now, three years after the arrival of ChatGPT and generative AI, the enterprise has matured its understanding of AI’s potential.

“We’re clearly in the third wave where more clients understand the transformational value of AI and that it’s about new ways of working,” Greenstein says. “Those who are getting ROIs are the ones who see it as a transformation and work with the business to rethink what they’re doing and to get people to work differently. They know transformation work is required to see an ROI.”

To ensure AI projects deliver ROI, Palo Alto Networks CIO Meerah Rajavel selects initiatives that deliver velocity (“Speed is the name of the game,” she says), efficiency (“Can I do more with less?”), and improved experience. “This forces us to reimagine experiences and processes, and it absolutely changes the game,” she says.

Rajavel assesses each AI initiative’s success on the outcomes it produces in those categories, noting that her company has adopted that focus all along and continues to use it to determine which AI investments to make.

As a case in point, she cites a current project that uses AI to automate 90% of IT operations — a project that is already delivering gains in velocity, efficiency, and experience. Rajavel says automated IT operations jumped from 12% when the project started in early 2024 to 75% as of late 2025 — an improvement that has halved the costs of IT operations.

Metrics and targets

Many organizations haven’t taken a strategic approach when deciding where to implement AI, which helps explain why AI ROI has been so elusive, says IBM’s Dhar. “Some sprayed and prayed rather than systematically asking, ‘How will the technology make my company better?’” he adds.

But top management teams are increasingly looking at AI “as a way to transform — and to transform their businesses dramatically,” he says. “They’re reinventing all their functions, and they’re transforming functions to make them better, stronger, and cheaper, and in some cases they’re also getting top-line growth. Two years ago, there was a lot of experimentation, proofs of concept; now it is transformation, with the most sophisticated management teams looking for returns within 12 months.”

Linh Lam, CIO of Jamf, had been deploying AI to solve pain points but is now using AI “to rethink how we do things.” She sees those as the opportunities to generate the biggest gains.

“I feel like we’re going to see more and more of that, where the technology forces us to rethink how we’re doing things, and that’s where the real value is,” she says.

That’s certainly the case in terms of the AI initiatives Jamf now prioritizes.

“Two years ago, there was more tolerance to say, ‘Let’s try it.’ Now we’ve moved well beyond that, so if someone is bringing something in and they have no semblance of the potential value except it’s going to make life better, we’re going to push back on that. We’re looking at the goals stakeholders have and setting metrics to measure outcomes,” she says. “I feel like the realm of possibility with what you can do with AI and AI agents almost feels limitless. But you’re still running a business, and you want to make decisions in a logical, smart way. So we have to make sure we’re bringing the right value.”

Turning IT challenges into a virtuous cycle for AI transformation

There are challenges, of course, to getting positive returns on AI initiatives — even when they’re carefully selected for their potential, says Jennifer Fernandes, lead of the AI and technology transformation unit at Tata Consultancy Services in North America.

According to Fernandes, many organizations are stymied by legacy technology, process debt, and data debt that keeps them from being able to scale AI projects and see measurable value.

And they won’t be able to scale their AI ambitions and see impactful returns until they pay off that debt, she adds.

Cisco’s AI Readiness Index found that only 32% of organizations rate their IT infrastructure as being fully AI ready, only 34% rated their data preparedness as such, and just 23% considered their governance processes primed for AI.

Fernandes advises CIOs to tackle that debt strategically and use AI to pay it down. Moreover, using AI to modernize IT will bring efficiencies to IT operations while also building IT’s capacity to support more AI use cases and addressing deficits in the organization’s data layer, she says.

The increased efficiency produces returns that can be reinvested in other AI projects, which will be more likely to produce ROI due to the modernization that resulted from the earlier AI project, Fernandes explains.

Moreover, this self-funding model not only helps build the modern tech stack and data program needed to power AI in IT and other business units but also focuses attention on ROI from the start, helping ensure CIOs and their business peers pursue AI initiatives that generate positive returns.

“You’re generating enough savings to pay down your debt, and you’re building incrementally, you’re transforming as you go,” Fernandes says. “And with this [approach], CIOs don’t have to go and say, ‘Give me money to fix these things.’ Instead they can say, ‘I have this model, and if we bring AI in here, we can generate returns, and we can then reinvest to drive these other transformations. Now the CIO can say, ‘I am generating the funding for AI for you.’”