In April 2026, IBM reported that Blue Pearl, a cloud solutions and consulting company, used an AI development system to complete a Java software upgrade in three days. The same project would normally have taken about 30 days. According to IBM, the company saved more than 160 engineering hours.
That’s the kind of AI story every business leader wants to hear. The work was defined. The improvement was measured. The value was tied to an actual business process. Blue Pearl didn’t claim that AI had transformed the entire company. It applied AI to a specific problem and calculated what changed. While this was a vendor-reported result, it provides a useful example of what measurable AI value looks like.
When executives ask me about the ROI of AI, my answer is that there isn’t one universal number. AI doesn’t generate a return because a company bought licenses, launched a chatbot, or gave employees access to a collection of tools. It generates a return when it improves a measurable business outcome.
When AI Activity Is Mistaken for Value
PwC’s 2026 Global CEO Survey, based on responses from 4,454 CEOs across 95 countries and territories, found that only 12 percent said AI had produced both cost and revenue benefits. Fifty-six percent reported no significant financial benefit. Companies that had embedded AI extensively across products, services, demand generation, and decision-making were far more likely to report returns. Those with strong AI foundations were three times more likely to see meaningful financial benefits.
To me, that doesn’t mean AI is failing. It means many companies are measuring deployment instead of value.
They count users, prompts, licenses, agents, or generated documents. Those numbers tell us whether people are touching the technology. They don’t tell us whether costs declined, revenue increased, customers stayed longer, errors fell, or work reached completion faster.
Productivity is especially easy to misunderstand. Workday’s 2026 research, conducted with 3,200 employees using AI at companies with more than $100 million in annual revenue, found that 85 percent saved between one and seven hours per week. That sounds like a significant return. However, nearly 40 percent of those savings were lost to correcting errors, rewriting material, and checking AI-generated output. Only 14 percent of employees consistently experienced clear positive results.
This is why I don’t consider time saved to be ROI by itself. If an employee saves five hours with AI but spends two hours checking its work, the gross benefit isn’t five hours. It’s three. If the remaining time disappears into more meetings, unnecessary output, or a larger workload, the company may never convert that capacity into financial value.
Turning AI Into a Measurable Return
The basic calculation hasn’t changed. What has changed is what belongs in that calculation. The investment side should include software, computing, integration, training, process redesign, security, governance, maintenance, and human review. The benefit side should include validated labor savings, reduced operating expenses, additional gross profit, fewer costly errors, faster cycle times, improved retention, and lower risk exposure.
Google Cloud’s 2026 survey of more than 2,400 executives found that 84 percent were seeing increasing financial returns from AI, while 26 percent reported that returns were accelerating year over year. The leaders shared three characteristics: clear ownership, AI embedded into core workflows, and continuing employee training. Nearly half had integrated AI into major business processes, revenue streams, or new business models.
That finding reinforces my view that the greatest returns won’t come from giving everyone an AI assistant and hoping productivity improves. They’ll come from identifying high-volume, expensive, error-prone workflows and redesigning them around measurable outcomes.
I’d begin with one question: What business result should change?
From there, I’d establish the current cost, speed, accuracy, and outcome rate. Then I’d introduce AI, measure the same factors, subtract every associated cost, and validate that the improvement is real. Usage can support the analysis, but it can’t substitute for it.
The ROI of AI can be substantial. It can also be negative, invisible, or imaginary. The difference isn’t determined by the model alone. It’s determined by whether the company connects AI to completed work, financial performance, and business outcomes that someone is accountable for delivering.
