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Measure AI’s impact on IT services by connecting three things: how intensively people use it, what happens to the speed and quality of their work, and whether those operational changes produce a realized financial benefit. Compare like-for-like work against a credible baseline, then account for implementation and operating costs. Faster work alone does not prove higher profit.
Start with a specific claim and unit of work
Choose a bounded workflow before measuring—for example, code review, incident triage, test generation, service-desk responses, proposal preparation, or a defined client-delivery task. State what AI is expected to change: elapsed time, labor time, accepted output, quality, cost, client outcomes, or revenue. Those are related but distinct outcomes.
Set the unit of analysis and observation period in advance. Depending on the workflow, the unit might be a ticket, task, sprint, project, account, or team. Define which work is eligible for AI assistance and what counts as completed and accepted. A draft generated by a tool is not necessarily completed work.
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Before rollout, record the selected workflow’s normal performance. At a minimum, capture work volume, elapsed and labor time, acceptance, defects, rework, escalations, client experience, and delivery economics when those measures apply. Keep task definitions consistent before and after AI is introduced.
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Use random assignment, a phased rollout, or matched tasks or teams when practical. If a controlled comparison is not feasible, document factors that could explain a change independently of AI, such as task mix, employee experience, workload, seasonality, and policy changes. Microsoft Research’s workplace analysis emphasizes that generative AI’s influence varies by role, function, and organization and depends on adoption and utilization (Microsoft Research, July 2024); comparison design should account for that variation.
Measure actual use, not just availability
Track who is eligible and has access, who actively uses AI, how often they use it, how much time they spend with it, where it appears in the workflow, and which tasks it supports. Seats purchased, logins, or enthusiasm scores show reach or interest—not how intensively AI contributes to work. The Federal Reserve Bank of St. Louis explicitly recommends measuring intensity as well as adoption in its February 2025 analysis of generative AI and work productivity.
Separate exposure measures from outcome measures. For example, days of use can help explain variation in throughput, but do not by themselves establish that throughput improved. Record usage at a level that can be linked to the same workflow units used in the baseline comparison, while following applicable privacy and governance rules.
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No single productivity measure captures speed, usefulness, quality, and service performance at once. Choose a small set of measures that reflects the workflow and interpret them together.
| Measurement area | Examples to track | What it helps answer |
|---|---|---|
| Use and exposure | Eligible population, active users, days of use, time spent, workflow location, tasks assisted | How much of the work was actually exposed to AI? |
| Throughput and speed | Completed or accepted tasks, work volume, elapsed time, labor time, lead time | Did the team finish more work or finish comparable work sooner? |
| Quality and rework | Defects, review findings, rework, security issues, maintainability indicators, repeat contacts | Did faster or higher-volume work remain usable and safe? |
| Service and client outcomes | Resolution quality, SLA attainment, escalations, customer satisfaction, client acceptance | Did the change improve the service received, not only internal activity? |
| Economics | Delivery cost, avoided expenditure, billable capacity used, revenue realized, AI-related costs | Did operational change create a financial result after costs? |
For software delivery, pair output or lead time with defects, incidents, review findings, change failures, rework, security issues, and maintainability indicators. For service operations, pair response or resolution speed with resolution quality, repeat contacts, SLA performance, satisfaction, and escalation rates where applicable. Use software delivery measures in the context of the workflow rather than treating any one engineering metric as a universal AI score; Google Research’s DORA impact publication is a relevant reference for software-development measurement.
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Translate operational changes into financial results
Build an explicit bridge from measured work effects to money. Freed time has economic value only if the organization converts it into something realized, such as lower expenditure, avoided hiring or outsourcing, additional billable capacity that is actually used, faster revenue realization, reduced quality-related costs, or a client outcome with commercial value. Distinguish realized benefits from forecasts.
A transparent calculation can be expressed as:
Net financial impact over the stated period = realized financial benefits − AI-related costs.
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Professional-services surveys illustrate why ROI should not be reduced to a single metric. In Thomson Reuters’ 2025 Generative AI in Professional Services Report, 21% of respondents said their organization measured GenAI ROI. Among that subset—not all surveyed firms—the reported measures included internal cost savings (79%), employee usage (64%), employee satisfaction (51%), projected external revenue generation (31%), new business won (24%), and client satisfaction (38%). These survey measures span activity, experience, and financial outcomes; they are not interchangeable proof of profit.
Interpret published productivity figures in context
Published figures can inform expectations, but they are not transferable promises. Studies and surveys differ in work type, participants, outcome definitions, and evidence design.
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| Evidence | Reported result | How to interpret it |
|---|---|---|
| Federal Reserve Bank of St. Louis, 2025; pooled August and November 2024 U.S. worker survey data | 9% of U.S. workers reported using generative AI every workday. Among workers who had used it in the previous month, 31.9% reported using it at least an hour per workday. Estimated assistance ranged from 1.3% to 5.4% of total work hours across all workers. | These figures describe reported adoption and estimated exposure, not measured productivity gains for an IT services firm. |
| Federal Reserve Bank of St. Louis, November 2024 survey | AI users reported average time savings equal to 5.4% of their work hours. | This is self-reported time saved, not an independently verified increase in output or profit. |
| Microsoft Research, 2025; combined analysis of three randomized field experiments with 4,867 software developers | Users of the AI coding tool completed an estimated 26.08% more tasks; the standard error was 10.3%, and the individual experiments were noisy. | This is a study-specific estimate for selected software-development settings. It should not be assumed to predict results in other IT services workflows. |
| Capgemini Research Institute, April 2024 executive survey | Organizations with active generative AI initiatives reported 7–18% improvement in total productivity across the software development lifecycle. | The organizations represented had initiatives in pilot or scaling stages. This is survey evidence, not an independent causal estimate. |
The Federal Reserve Bank of St. Louis frames the counterfactual question usefully: how much additional time would workers have needed to complete the same amount of work without generative AI? That helps define a time comparison, but it does not establish whether the work was equally good or financially valuable. Pair it with quality and economics measures rather than treating time saved as the final result.
Segment results and report uncertainty
Break out findings by task, service line, role and experience, client context, and level of AI use. An overall average can hide a workflow where AI helps and another where review burden or defects offset the speed gain. Include neutral and negative findings, sample size, period, baseline, comparison method, adoption level, quality measures, and uncertainty. Re-measure when workflows or models change.
The International Labour Organization’s June 1, 2026 review of empirical evidence describes gains as real but often unverified and uneven. The OECD’s 2025 review also notes potential harm when AI is applied to tasks for which it is not effective. For this reason, test task fit and risk alongside use intensity and productivity measures.
Keep productivity evidence separate from margin claims
Productivity improvement and profit-margin improvement are different claims. Even when a team completes work faster, the organization still needs to establish whether capacity was monetized, costs fell, quality-related costs changed, or client value improved—and deduct the cost of achieving that result.
McKinsey reports that cross-functionality, lower vendor dependency, and public-cloud use correlate most strongly with high profit margins in its study of technology-delivery capabilities (McKinsey, “How to optimize IT productivity for revenue growth”). That is an association involving delivery practices, not evidence that AI caused higher margins. Deloitte’s 2025 AI ROI survey found that only 6% of surveyed organizations reported AI payback in under a year; most respondents reported satisfactory ROI on a typical AI use case within two to four years. Its respondents were 1,854 executives in Europe and the Middle East, supported by 24 interviews, so the result is not an IT-services-specific benchmark. Use a defined payback horizon and distinguish a pilot’s operational result from scaled financial impact.
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