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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →There is no single reliable number for AI return on investment (ROI). An AI project may save staff time, improve a mission-critical operation, or create a service customers will pay for; those outcomes do not all show up as immediate revenue or cost cuts. The practical approach is to decide what success means for a specific use case before implementation, then track the operational change and the financial or mission value attached to it.
Why AI ROI is difficult to reduce to one number
AI initiatives can affect several kinds of value at once. A recruiting tool might prevent wasted staff effort or an unnecessary background check. A software development assistant might help a team deliver more useful features, even if it does not reduce headcount. An agency tool might become a service clients are willing to buy. These benefits require different measures, and none is automatically equivalent to a verified financial return.
Costs also extend beyond the initial implementation. Organizations may need to account for ongoing model or platform expenses, integration and oversight, and the extra operational overhead of adopting new tools. A useful ROI discussion therefore connects an intended outcome to a measurable operational change, assigns a financial or mission value to that change where possible, and weighs it against the full costs and time to value.
Set the measure before choosing or launching a use case
Gartner analyst Arun Chandrasekaran advises deciding what return an application should produce before implementation: “We don’t want to be implementing use cases and then start thinking about how we’re going to measure value,” TechTarget reported. A pilot can demonstrate that a tool works technically without establishing that it solves a valuable problem.
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For each proposed use case, specify these elements before a pilot begins:
- Intended outcome: State the business or institutional result sought, such as reducing recruiting friction, delivering capabilities faster, serving more students, or creating a sellable offering.
- Operational measure: Identify what should change in the workflow. Use a measure close to the outcome rather than an easy-to-count proxy that may not matter.
- Value of the change: Explain how the operational improvement translates into avoided expense, capacity, revenue, or mission impact. If it cannot yet be credibly priced, say so rather than implying a financial return.
- Costs and time horizon: Include implementation, ongoing use, and added overhead, and identify when the organization expects evidence of value.
Chandrasekaran says the appropriate ROI period depends on the type of use case. He told TechTarget that he would generally expect 80% of enterprise use cases to reach ROI within a year; this is his reported guidance, not a universal requirement or independently validated benchmark.
Choose operational measures that reflect real progress
A metric can move without the organization becoming more effective. Chandrasekaran contrasts counting lines of code with measuring software delivery velocity—for example, the new features or capabilities a team delivers. The latter is closer to a useful outcome, although an organization still needs to check that faster delivery produces capabilities users need and does not compromise quality or reliability.
The same principle applies outside software. Count what represents meaningful change for the particular workflow, then connect it to the value the organization actually cares about. Time saved may create capacity for more valuable work without immediately reducing payroll. That can matter, but it should be described as capacity or productivity unless it produces a demonstrable financial saving.
What the reported examples show
The cases below are accounts reported by TechTarget from company representatives, not controlled studies or independent audits. They illustrate different ways organizations have defined or discovered value; their results are not directly comparable.
| Organization and use case | Outcome or measure described | Value and qualification |
|---|---|---|
| Alight Solutions: recruiting fraud detection | During a test of Phenom’s recruiting fraud-detection agent, Alight identified a candidate who had applied twice under different names and email addresses. | Talent acquisition operations manager Julie Eagy said the detection was enough to demonstrate potential usefulness, with time savings more important to her than avoiding one background-check expense. The example is a reported test incident, not an audited estimate of recurring savings. |
| OBI Creative: campaign alignment and agency operations | The Omaha agency developed AI tools for website health monitoring and campaign alignment. Clients asked to use the campaign-alignment prototype, which the agency then sold or licensed. | Founder and CEO Mary Ann O’Brien said this contributed to higher gross margins and that she expected at least 20% year-over-year growth. The growth figure is her expectation, not a verified result. She also reported higher overhead that was quickly balanced by efficiencies. |
| Cornell University: AI aligned to institutional mission | Cornell’s stated measures include the number of students served and scientific discoveries made. | Head of AI innovations Ayham Boucher described AI as a tool to expand human capabilities. These are mission-oriented measures; the report does not give an audited financial return or quantified change for them. |
Alight: value can be a small event with meaningful operational implications
Alight, a benefits administrator, began testing Phenom’s agent after becoming a beta user in September 2025. The tool identified a candidate who had applied twice using different names and email addresses. Eagy said, “Even by catching that one person that we ultimately didn’t hire, that was enough for us to say, ‘It’s going to work for us,’” as quoted by TechTarget. The avoided background check was a possible monetary benefit; Eagy described the greater value as time saved. The account does not establish how often the tool would detect such cases or the total savings over time.
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OBI Creative: an internal efficiency tool can become a revenue opportunity
OBI Creative, an advertising agency with fewer than 50 employees, initially prototyped its campaign-alignment tool for internal brand strategy and creative teams. O’Brien said clients wanted to use it, creating an opportunity to sell or license it beyond the agency’s internal workflow. She described agentic AI as an efficiency opportunity for scaling offerings across industries. Her report of higher gross margins and expectation of at least 20% year-over-year growth should be read as her account and forecast, not as independently verified performance.
O’Brien also reported increased overhead, which she said efficiencies quickly balanced. That detail matters: a favorable productivity story is incomplete if it omits new costs. She identified employee trust as part of adoption, noting that some staff were afraid of the tools and needed confidence that their expertise remained valuable to clients.
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Cornell: institutional value need not be a revenue measure
Cornell’s approach ties AI outcomes to the university’s mission, including how many students are served and how many scientific discoveries are made. Boucher said, “We focus on this technology as a tool,” and said Cornell does not measure value by “tokenmaxxing.” The report describes 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. It does not provide comparable measured results for those services.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why pilots stall before production
TechTarget reported that a 2026 Gartner report found around half of generative AI projects were abandoned after proof of concept, citing poor data quality, escalating costs, and unclear business value among the reasons. The Gartner report itself was not independently verified for this article, so the figure should be treated as an attributed report rather than a confirmed universal rate.
Unclear value is especially likely when organizations start with a technology demonstration and only later ask what success should mean. A proof of concept may establish feasibility while leaving unanswered whether the use case is worth operating at scale. Defining a target outcome and measure first helps expose that gap earlier; cost growth and data limitations still need their own assessment.
A practical decision frame for AI use cases
Before moving a candidate use case from proposal to pilot, write down the following in terms specific to the organization:
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Outcome: What business or mission result should improve?
- Operational signal: What observable workflow metric would show progress toward that result?
- Value logic: How does a change in that metric create revenue, avoid costs, free capacity, or advance the mission?
- Cost boundary: Which implementation, operating, oversight, and overhead costs belong in the calculation?
- Evidence threshold: What result would justify continuing, revising, or stopping the effort, and by when?
Do not force every benefit into a dollar figure if the organization cannot support the conversion. Separate measured operational change from estimated financial impact and mission value. That distinction makes a positive case more credible—and makes a weak case easier to identify before a pilot becomes a permanent expense.
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