Ben Schein, VP of AI and analytics at AI infrastructure software company Progress Software, adds that CIOs should stop measuring AI activity and start measuring outcomes where possible. “Usage tells you the meter is spinning, not that value came out,” he says. “Instrument the cost side completely, since every AI action can carry its workload, model, and cost. Then pair costs with benefits where the work allows, so cost per workflow run against value per workflow run, never enterprise-wide spend against anecdotal wins.”

However, Dan Priest, PwC US CAIO, recommends getting back to basics, starting with the investment side but adding a few modern updates. “AI ROI has been illusive for many, in part because organizations are still learning how to achieve it, and because AI sometimes gets a pass on the typical disciplines required to measure it,” he says. “For major AI initiatives, starting with investment might include new infrastructure, labor for the build, models for the intelligence layer, and variable expenses like token costs. All of these should be contemplated in ROI calculation, some of which will require new tracking and controls.”

His benchmarking also points to a threshold effect: ROI stays insignificant until AI investment crosses roughly 1.6% of revenue, and above that line, EBITDA is up 9.5%, revenue is up 3.5%, and total shareholder return is up 20.2%.