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Start with a measurable claim, not an AI adoption target
Define the workflow and the result you want to evaluate before introducing AI. A useful test might ask whether AI-assisted incident triage reduces time to resolution without increasing repeat contacts, or whether AI-assisted code review helps a team deliver more accepted work without creating more defects.
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Choose a unit that matches the work: a ticket, task, sprint, project, account, or team. Set an observation period and specify the primary outcome—such as cycle time, accepted output, quality, cost, client experience, or revenue. Adoption is exposure to the tool, not evidence of impact. Microsoft Research notes that generative AI’s influence varies by role, function, organization, adoption, and utilization in its July 2024 workplace report.
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Record what comparable work looked like before AI use: work volume, elapsed and labor time, acceptance rates, defects, rework, escalations, client outcomes, and delivery economics. Then compare AI-assisted work with a control or with a carefully matched baseline.
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- Best when practical: randomly assign comparable work or teams to AI-assisted and non-AI-assisted workflows.
- Useful for staged adoption: roll out the tool in phases and compare teams or periods while accounting for changes over time.
- When a controlled comparison is not feasible: match tasks or teams and record differences in task mix, experience, workload, seasonality, and policy.
A before-and-after comparison alone can be misleading if the work changed, the team gained experience, or demand shifted. Report the comparison design and its limitations rather than presenting every observed improvement as caused by AI. Microsoft Research’s three software-developer field experiments illustrate why context matters: the combined estimate is informative for those settings, but is not a universal result for IT services.
Measure actual AI use, not seats or enthusiasm
Track who could use AI, who actually used it, how often, for which tasks, and where in the workflow it was used. Useful measures include eligible users, active users, days of use, time spent, and the share of relevant work performed with AI assistance. Seats purchased, logins, and self-reported enthusiasm do not establish how much work was exposed.
The Federal Reserve Bank of St. Louis argues that productivity measurement should look beyond adoption to usage intensity in its February 2025 analysis. Its survey-based figures offer context about U.S. workers, not an IT-services-company benchmark:
| Measure | Reported finding |
|---|---|
| Use every workday | 9% of U.S. workers reported using generative AI every workday in pooled August and November 2024 survey data. |
| At least an hour per workday | Among U.S. workers who used generative AI in the previous month, 31.9% reported using it at least an hour per workday. |
| Work hours assisted | The study estimated that generative AI assisted between 1.3% and 5.4% of total work hours across all workers. |
| Self-reported time savings | Surveyed users reported average time savings equal to 5.4% of their work hours in November 2024. |
The time-savings figure is self-reported. It is not a measure of verified hours removed from payroll or of profit created.
Pair speed and throughput with quality
Productivity has more than one dimension. A workflow that produces drafts faster but requires extra review or causes more rework may not improve overall performance. Distinguish output generated from work accepted by a client, production system, or responsible reviewer.
| Workflow | Output and speed | Quality and service checks |
|---|---|---|
| Software delivery | Completed or accepted tasks; lead time; delivery volume | Defects, incidents, review findings, change failures, rework, security issues, and maintainability indicators |
| Service desk or incident response | Resolution time; tickets handled; SLA attainment | Resolution quality, repeat contacts, escalations, and customer satisfaction |
| Client delivery or proposals | Accepted deliverables; turnaround time; work completed | Client acceptance, correction effort, review burden, and commercial outcome |
Choose measures appropriate to the workflow; the reviewed sources do not define a single universal scorecard. For example, a software task completed more quickly is not necessarily a gain if it later produces a defect or requires substantial correction.
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Published results should be interpreted in their original settings. A combined analysis of three randomized field experiments covering 4,867 software developers found a 26.08% increase in completed tasks among users of the AI coding tool, with a standard error of 10.3%; Microsoft Research reported that individual experiments were noisy. This is a study-specific estimate, not a forecast for other firms or IT-services workflows. See the June 2025 study.
Separately, Capgemini Research Institute reported a 7–18% improvement in total productivity across the software development lifecycle among organizations with active generative AI initiatives in pilot or scaling stages. This figure comes from its executive survey, not an independent causal estimate; see its April 2024 infographic.
Translate verified operational change into financial results
Time saved becomes a financial benefit only if the organization realizes value from it. Map measured changes to a specific economic outcome: lower expenditure, avoided hiring or outsourcing, additional billable capacity, faster revenue realization, reduced quality-related costs, or another demonstrable client or business benefit.
A practical measurement framework is to report gross benefits and net results separately over a defined period. The framework is a way to organize the calculation, not a formula prescribed by any one source.
- Gross benefit: the value of realized cost reductions, avoided costs, additional valuable output, or revenue effects supported by the measurement.
- Full cost: licenses or inference, integration, data preparation, security and governance, training, human review, change management, and rework.
- Net result: gross benefit minus full cost over the stated time horizon, with assumptions and forecast benefits distinguished from realized results.
Do not count the same benefit twice. For instance, time released and the revenue from selling that same time are not automatically separate gains. State whether increased capacity was actually used, billed, or otherwise converted into value.
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Professional-services surveys show that organizations assess ROI in multiple ways, but their results are not a substitute for a firm’s own measurement. Thomson Reuters reported that 21% of respondents said their organization measured generative-AI ROI. Among those measuring ROI—not all firms—the report listed the following measures:
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| Measure reported | Share among respondents measuring ROI |
|---|---|
| Internal cost savings | 79% |
| Employee usage | 64% |
| Employee satisfaction | 51% |
| Client satisfaction | 38% |
| Projected external revenue generation | 31% |
| New business won | 24% |
These are survey findings from the 2025 Thomson Reuters Generative AI in Professional Services Report; the listed percentages describe measures used by the subset that said it measured ROI.
Deloitte’s 2025 survey of 1,854 executives in Europe and the Middle East, supported by 24 interviews, found that 6% of organizations reported AI payback in under a year; most respondents reported satisfactory ROI on a typical AI use case within two to four years. This is not an IT-services-specific benchmark. Use it as a reason to make the payback horizon explicit, not as a promised timetable for your organization. See Deloitte’s 2025 analysis.
Segment results and disclose uncertainty
Average results can hide gains in one workflow and losses in another. Break results down by task, service line, role and experience, client context, and usage intensity. Include neutral or negative outcomes, sample size, measurement period, baseline, comparison method, and quality results. Reassess when workflows or models change.
Task fit matters: evidence summarized by the OECD notes potential harm when AI is used on tasks for which it is not effective. The International Labour Organization’s June 1, 2026 review describes productivity gains as real but often unverified and uneven. Together with Microsoft’s findings on variation across roles and organizations, this is a reason to measure locally rather than assume one result applies everywhere.
Keep productivity findings separate from margin claims
Operational improvement and company profitability are related but not interchangeable. More completed work or lower cycle time may create capacity, but the effect on profit depends on whether that capacity reduces expense, supports revenue, or improves another valued outcome—and whether those benefits exceed the costs of implementation and operation.
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. The analysis describes correlations between capabilities and company margins; it does not establish that AI caused higher margins. Use those findings to frame operational questions, not as proof of AI’s financial effect. See McKinsey’s analysis.
Evidence from selected software experiments, executive or professional-services surveys, and margin correlations answers different questions. None establishes a universal causal increase in IT-services profit margins. A defensible conclusion should therefore say what changed in the measured workflow, for whom, under what comparison, and whether a financial benefit was actually realized.
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