The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →There is no single reliable ROI figure for AI: value depends on what a particular use case is meant to change, what it costs to run, and how the organization values that change. The practical starting point is to define the desired outcome and its measures before implementation—not after a pilot—and then track both operational results and the financial or mission value attached to them.
Why AI ROI is difficult to reduce to one number
An AI initiative may save staff time, improve an operational process, enable a new service, or advance an institutional mission. Those outcomes do not convert into revenue or cost reductions in the same way, and some benefits may be meaningful before they appear on a financial statement. A useful ROI discussion therefore starts with the specific application rather than a universal AI productivity figure.
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Measurement also needs to account for the full cost of delivering the outcome: implementation, ongoing operation, and any additional overhead. If a tool saves time but adds costs elsewhere, the net effect matters. Conversely, time saved can be valuable even when it does not immediately reduce payroll or raise revenue.
Define the measures before implementation
Gartner analyst Arun Chandrasekaran advises deciding what return a selected application should produce before building or deploying it: “We don’t want to be implementing use cases and then start thinking about how we’re going to measure value,” he told Esther Shittu of TechTarget in a report published September 17, 2026.
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For each use case, connect three questions:
- What outcome matters? Name the business or mission result the initiative is intended to support.
- What operational measure should move? Choose a measure close to the work being changed, such as delivery speed or time spent on a process.
- How will that movement count as value? Set out the financial or mission significance, expected time to value, and the implementation and ongoing costs.
Chandrasekaran recommends pairing operational measures with value-oriented measures. For software development, for example, delivery velocity—such as new features or capabilities delivered—may better reflect useful change than lines of code. More code is not necessarily more value.
He said the ROI period depends on the type of use case. He also offered the view that “for 80% of enterprise use cases, you want to get to an ROI within a year.” That is his reported guidance, not a universal requirement or an independently validated benchmark. An organization should set a timeframe appropriate to its application and intended outcome.
What reported AI examples show about measuring value
The cases below are accounts reported by TechTarget, not controlled studies or independent audits. They illustrate different kinds of value; their results are not directly comparable.
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Alight: fraud detection and time saved
Alight Solutions, a benefits administrator, became a beta user of HR technology vendor Phenom’s recruiting fraud-detection agent in September 2025. During testing, the tool identified a candidate who had applied twice under different names and email addresses. Julie Eagy, Alight’s talent acquisition operations manager, said catching that one applicant was enough to convince the organization the tool could work for it.
TechTarget reported that avoiding an unnecessary background check was one possible monetary benefit. Eagy described the greater value as saving time. The example shows why the right success measure may include an operational benefit even if the most immediate financial saving is modest or indirect.
OBI Creative: efficiencies and a new offering
OBI Creative, an Omaha advertising agency with fewer than 50 employees, built AI tools for website health monitoring and campaign alignment. CEO and founder Mary Ann O’Brien said the campaign-alignment tool started as an internal prototype for brand strategy and creative teams. Clients then asked to use it, and the agency began selling or licensing it. In her account, a tool intended to support internal work also created a potential revenue opportunity.
O’Brien said this contributed to higher gross margins and that she expected at least 20% year-over-year growth. That figure is her stated expectation, not a verified result. She also reported increased overhead that was quickly balanced by efficiencies—an example of why an ROI calculation should include added costs as well as the gains.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAdoption is another part of the operational picture. O’Brien described employees who were afraid of the tools and the importance of building trust in how they would be used. A measure of technical output alone would not show whether staff are using a system effectively or whether the organization has addressed that human factor.
Cornell: value tied to institutional mission
Cornell University’s reported approach frames AI as a tool to expand human capabilities. Ayham Boucher, head of AI innovations for Cornell Information Technologies, said its outcomes align with the university’s mission, including the number of students served and scientific discoveries made. These measures are not conventional revenue metrics, but they connect technology use to the institution’s purpose.
The TechTarget report says Cornell provides access to models in a secure, private environment and names Microsoft Azure, Microsoft Copilot, Claude Desktop, OpenAI GPT models, and Anthropic Claude models among the technologies discussed. Boucher said Cornell does not measure value by “tokenmaxxing.” The point is to judge results by mission-linked outcomes, not by how much AI activity or usage a system generates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why pilots may not reach production
TechTarget reported that a 2026 Gartner report found around half of generative AI projects were abandoned after proof of concept. Poor data quality, escalating costs, and unclear business value were among the reasons cited. The underlying Gartner report was not independently verified for this article, so the figure should be understood as Gartner’s reported finding, not as an independently confirmed rate.
The cases point to a practical way to reduce the risk of an inconclusive pilot: specify the intended outcome, operational measure, cost assumptions, and time horizon up front. A pilot that produces activity without showing whether the target outcome changed may not provide decision-makers with a sound basis to continue.
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A use-case checklist for an AI ROI decision
- State the business or mission outcome the use case is meant to support.
- Choose an operational measure that reflects the work or result that should change.
- Explain how that operational change connects to financial or mission value.
- Include implementation expense, ongoing costs, and any added overhead in the evaluation.
- Set an expected time to value that fits the use case rather than applying one timeline to every project.
- Consider adoption and whether staff can use the tool effectively.
- Decide what evidence from a pilot would justify expanding, revising, or stopping the initiative.
Chandrasekaran also said vendors should do a better job of selling outcomes and value, not just technology. That expectation complements the organization’s responsibility to define what success means: a compelling demonstration is not a substitute for a measure tied to the work the organization needs done.
Source: Esther Shittu, TechTarget report, September 17, 2026.
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