Enterprises are pouring cash into AI sooner than they’ll show it is working. Gartner’s 2026 1H CIO Report discovered 83% of CEOs are growing AI funding — but 59% of AI initiatives by no means attain manufacturing. And 71% of the 11,000 CIOs surveyed mentioned they wrestle to prioritize AI use circumstances that can ship measurable outcomes.
Now the payments are coming due. Token-based pricing means prices scale with use, and distributors have taken discover. In early July, Anthropic added value controls to Claude Enterprise — spend alerts, mannequin defaults and analytics that pair consumption with output — one of many first strikes to tame runaway “tokenmaxxing.”
However these instruments present solely what an organization spent, not enterprise outcomes. Tying AI adoption to “true worth” is essential, mentioned Mohan Sankararaman, govt vice chairman and CIO of First Horizon, a regional monetary companies firm based mostly in Memphis. “Each AI use case ought to have a transparent function and make sure that function has ROI.”
To ship ROI for AI deployments, CIOs might want to work carefully with CFOs and different members of the C-suite to find out the proper metrics for measuring the enterprise outcomes of AI adoption of their organizations.
Why it is exhausting to measure AI ROI
The confusion begins with treating AI like bizarre software program, based on Ashwin Rangan, a veteran expertise govt and board advisor who has held senior roles at Walmart and ICANN. It is higher understood as a general-purpose expertise, extra akin to electrical energy. “Simply making electrical energy out there to each employee with a wall outlet is a measure of profitable deployment,” he mentioned. “However it isn’t a measure of efficient utilization.”
By that logic, the brand new value dashboards are electrical energy meters: helpful however restricted. They will relay how a lot energy was utilized from the grid, however what’s lacking is a “fit-for-purpose companion meter to measure enterprise outcomes,” he added.
Most organizations nonetheless report solely deployment statistics. A uncommon few measure precise AI outcomes, and a handful observe ROI, Rangan mentioned.
The pull towards the incorrect measures is powerful as a result of the helpful one is tough to implement, mentioned Ara Kharazian, lead economist at spend-management platform Ramp. “It is very troublesome to measure AI’s affect on a enterprise, so leaders are compelled to make use of metrics which might be out there however not essentially acceptable,” he mentioned. Tokens used, workers utilizing AI, traces of code written — “these metrics say nothing in regards to the high quality of labor being produced,” he defined.
The deeper subject happens even earlier, Sankararaman mentioned. “The most important danger for expertise leaders is shifting with out function,” he mentioned. “Doing extra AI experiments does not equate to extra AI worth.”
And the goal retains shifting, based on Kevin Rooney, CIO of consulting agency West Monroe. “The error has shifted during the last six months,” from hype to adoption to value administration, and now to “counting on delicate productiveness justification with out connecting it to actual enterprise outcomes,” he mentioned.
Monitor AI outcomes, not simply token prices
Specialists say measurement work ought to begin earlier than any AI is switched on.
Earlier than deciding on an AI instrument, executives must determine the enterprise workflow, defined Yogesh Joshi, senior vice chairman of worldwide AI platforms at TransUnion. “We determine the job to be executed, perceive the folks, processes and artifacts concerned, and set up baseline efficiency metrics. Then we measure how AI modifications these metrics over time.” None of that requires reinventing the self-discipline as expertise leaders have been measuring the worth of tech investments for many years, he mentioned.
At First Horizon, the proper stakeholders fluctuate by challenge, however the expertise chief and the enterprise proprietor accountable for the operate all the time align on the yardstick earlier than the work begins — concrete measures like time to e-book a mortgage, time to launch a product or buyer expertise scores. “We concentrate on delivering significant progress in opposition to these metrics earlier than increasing our [AI] investments,” Sankararaman mentioned.
Rangan lays out a parallel sequence: benchmark the present state of a workflow, deliver the CFO in early so each value ingredient is captured, run take a look at circumstances proving the AI-assisted model is measurably higher, and translate inputs and outputs into financial phrases.
The place the CFO and board stand on AI
That early CFO involvement issues, as a result of finance leaders have a tendency to border AI in another way than technologists do. To Hemant Kapadia, CFO at planning-software maker Anaplan, measuring AI’s worth means treating it much less like a software program buy than a capital-allocation determination.
“Too usually, enterprises consider AI the identical means they’d consider any new software program buy: ‘What did this particular instrument save us?,’ and ‘Can I draw a straight line to a line merchandise?'” Kapadia mentioned. “You find yourself measuring a pilot as a substitute of measuring a functionality.” The capital lens asks an even bigger query: not whether or not a pilot saved time, however whether or not it delivered enterprise outcomes like altering value buildings, pace of determination making, or a company’s aggressive place, , Kapadia mentioned.
That makes the CIO-CFO relationship strategic somewhat than transactional. The CIO permits — maintaining AI usable, safe and ruled — whereas the CFO evaluates whether or not AI is affecting the economics of the enterprise, Kapadia mentioned. The catch is that the 2 executives should align on what a deployment is supposed to vary earlier than it goes stay, not after.
The folks to whom expertise leaders reply have already made this shift. “Boards are already there,” Rangan mentioned, crediting rooms filled with seasoned CEOs, CFOs and administrators. “The dialog has shifted materially towards ROI. Administration groups are nonetheless not there.” The result’s that board members have gotten more and more pissed off that ROI is just not being demonstrated as AI accelerates, he warned.
Contained in the enterprise, the strain is concrete. In 2026, boards and c-suite executives need proof that AI is shifting from pilots to delivering ROI, Sankararaman mentioned. They need outcomes that display that AI is delivering enterprise worth and that expertise groups are utilizing the proper governance for danger administration.
The place to begin measuring AI ROI
For leaders dealing with a climbing AI token invoice and a skeptical CFO, the recommendation converges on a disciplined begin. Sankararaman suggested choosing two or three “daring use circumstances,” implementing them and making certain they align with enterprise outcomes.
Joshi’s recommendation: “Begin small and go deep.” As soon as worth is confirmed in a single high-impact workflow, it is a lot simpler to scale measurement throughout the group, he mentioned. And know when to stroll away. “AI cannot be a hammer on the lookout for a nail,” he added. “In sure circumstances, the choice to not use AI might very effectively be probably the most acceptable one.”
The token prices, in the meantime, will hold climbing. The IT leaders who can present what these tokens purchase will hold incomes the funding. Those that can solely report what they spent might discover their AI budgets dealing with the identical scrutiny cloud spending as soon as did.
How is your group measuring whether or not AI investments are paying off — and what’s working? Share your ideas with us at [email protected].
