KPMG report finds enterprise disconnect between AI and its ROI

Enterprise CIOs need no convincing that return on investment (ROI) for genAI and agentic AI is elusive, but consulting giant KPMG is reporting that some companies are plowing ahead with the technology anyway.

In fact, beyond the lack of quantifiable ROI, executives are not even letting a weak economy slow their AI investment plans. “Three out of four global leaders will prioritize AI investment despite economic uncertainty,” KPMG found.

“A clear gap is present between organizations still in the experimentation phase and those that have moved beyond pilots to fully scaling AI agents and capturing real business value outcomes,” the company said in its Global AI Pulse Survey report. “Although AI adoption is accelerating worldwide, only a small group of AI leaders are seeing clear returns. These leaders consistently outperform others, including 82% saying that AI is already delivering meaningful business value, compared to 62% of their peers. This is not simply an AI maturity gap; it is a widening performance gap between organizations that treat AI as an enterprise-wide transformation and those that are trying to bolster AI onto existing models and seeing incremental gains.”

In the subset of its report focusing on the UK, KPMG reported: “AI no longer needs traditional return on investment to be justified. 65% of UK respondents say their organization would continue to invest in AI regardless of tangible ROI. Despite a lot of money being spent by businesses on artificial intelligence, traditional return on investment isn’t necessarily needed for them to see value in the technology.”

Mindset shift

Leanne Allen, a KPMG head of AI, said the extreme focus on enterprise AI has forced a new approach to the financial aspects of the technology. 

“This shift in mindset by business leaders from viewing AI as something that must deliver an immediate return to one that sees AI as a long-term investment, recognizing it as a strategic enabler for enterprise‑wide transformation, is an important milestone,” Allen said. “But that shouldn’t translate into investing in AI blindly, without a clear strategy. AI is reshaping how organizations operate, how decisions are made, and how human and AI agents work together day‑to‑day.”

This shift in thinking is partly pragmatic, with many CIOs being told by their boards that AI investments are not optional. But the ROI challenge with AI has many forms.

The many problems with AI ROI

Given the urgent pace of AI experimentation and deployment, many AI proofs of concept (PoCs) are launched by executives setting unrealistic ROI goals. If the project is being measured against a standard that it technologically can’t achieve, it’s not an indictment of the LLM when the inappropriate metrics were not delivered. 

Some enterprises are also discovering unexpected costs from AI rollouts, such as when they use AI in customer chatbots and then discover that people are abusing them as “free” genAI tools, with the enterprise having to pay for the additional tokens. 

What to measure

What some analysts and investors argue is that the kinds of intellectual effort that AI is replacing have never been measured well, if at all. This means that financial departments will need to figure out different ways of measuring AI ROI.

Ben Grant, managing partner at Lambton Capital Partners, said, “I believe the problem is how we measure it. Traditional ROI wants clean input-output. AI doesn’t do that yet in most businesses. The value shows up in time reclaimed, decisions made faster and gaps being plugged before they become problems. Try putting that in a spreadsheet.”

But, he added, “I definitely don’t think companies investing in AI without traditional ROI are being reckless. They’re being practical. They’ve seen enough to know it works. They just can’t quantify it in the language finance teams want.”

Manish Jain, a principal research director at Info-Tech Research Group, said that he believes this disconnect exists “because enterprises are simultaneously operating in two modes: exploratory, where learning velocity matters more than ROI, and industrialized, where value realization is expected, but maturity is still evolving.”

Companies have adjusted their expectations, he noted. “It is not that companies don’t care for returns,” he said. “It’s that they’ve learned that before focusing on ROI, they need to focus on maturing AI capabilities. When a new engine comes along, wise operators don’t ask first what it earns. They ask what happens if they’re the only ones without it.”

Is AI becoming mundane?

Gartner VP Analyst Nader Henein isn’t going so far as to call AI deliverables trivial, but the technology has started to integrate into mundane everyday functions, which can challenge a traditional ROI spreadsheet.

“Some AI investments like AI assistants are becoming standard office tools, like the office suite. No one calculates ROI by counting the number of Word documents or presentations produced,” Henein said. “But ROI calculations on AI projects are not going anywhere. If it burns cash and fails to produce any tangible ROI, it will be retired. P&L reports and the expectations of investors from publicly traded companies are not changing.”

Spending and hoping

Michael Leone, VP/principal analyst at Moor Insights & Strategy, said the differentiated nature of AI deployments can also frustrate typical ROI mechanisms. 

“The old ROI playbook from ERP or cloud migrations doesn’t fit AI, and every CIO I talk to knows it. They can likely tell you exactly what productivity benefits they’re getting on a specific workflow, but ask them what the three-year enterprise payoff looks like and you get a shrug. That’s where the ‘regardless of ROI’ line is really coming from and, frankly, I think leaders are right to keep funding through it,” Leone said. “Budget fell off the list of things killing AI programs a while ago. The money’s there and the mandate’s there. The real blockers now are security, privacy, and the fact that almost nobody has the people to run this at scale. I look at it as all of the orgs making an informed bet. They’ve done the math on what falling behind costs, and they don’t like the answer.”

He noted that perhaps one in ten enterprises he’s spoken to has the talent, governance, and operating discipline to actually get compounding returns from its AI spend. “Everyone else is spending and hoping. That’s the real story,” he said.

Carmi Levy, an independent technology analyst, said he sees it as “sheer fiscal suicide to spend on any bleeding edge technology without at least a modicum of ROI to justify it. Yet the speed and scope of AI advancement means traditional means of calculating ROI have become woefully obsolete. AI now compels organizations to dive in more out of fear of being left behind.”

This means, Levy argued, that finance may simply need to back off rigid ROI calculations for the moment. 

“The need to remain competitive in AI, or at least stay within sight of the competition while everyone struggles to figure AI out, means decisions may not be based on the same depth of fiscal rigor that might have been used in years past,” Levy said. “Increasingly turbulent economic conditions often compel organizations to hit the brakes on technology investments, but that logic is being tested as AI deepens its hold on the technological roadmap. Organizations will seek savings elsewhere to avoid the risk of falling behind competitors who refuse to back off their own AI-centric spending amid economic uncertainty. Indeed, many leaders use AI as a catch-all driver of unspecified future cost savings, which in the frenzied rush to remain AI-relevant is often enough to secure sign-in from the C-Suite.”