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Start with a specific workflow and a measurable claim
Choose a bounded workflow rather than measuring “AI productivity” across the whole organization. Possible units include a support ticket, code review, test case, incident, proposal, sprint, project, or client account. Define the observation period and what success means before deployment: faster completion, more accepted work, fewer defects, lower cost, improved client outcomes, or more revenue-generating capacity.
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These outcomes are related but not interchangeable. A team can finish more tasks without shortening elapsed delivery time; reduce drafting effort without reducing total labor; or work faster while creating more review and rework. State which result you are trying to establish.
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Build a baseline and a credible comparison
Before introducing AI, record the workflow’s normal volume, labor time, elapsed time, acceptance rate, defects, rework, escalations, client experience, and delivery economics. Use the same definitions and collection methods after rollout.
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Where practical, compare randomly assigned work, a phased rollout, or matched teams and tasks. If a formal comparison is not feasible, record what changed between the baseline and the AI-assisted period: task mix, seniority, workload, seasonality, staffing, client requirements, and policy. Otherwise, an apparent improvement may reflect a different workload or a broader operational change rather than AI.
Microsoft Research’s review of real-world workplaces says generative AI’s influence varies by role, function, and organization and depends on adoption and utilization. That makes the comparison design and the description of the participating teams essential context, not methodological decoration. Microsoft Research, “Generative AI in Real-World Workplaces” (July 2024)
Measure actual use, not just access
Track who was eligible to use AI, who actively used it, how often, how much work involved it, and where it entered the workflow. Useful fields include active-use days, time spent, task type, workflow stage, and the share of eligible work assisted. Seats purchased, logins, or favorable opinions show availability or sentiment; none by itself establishes meaningful exposure.
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The Federal Reserve Bank of St. Louis emphasizes measuring intensity as well as adoption. Its February 2025 article reports that, in pooled August and November 2024 survey data, 9% of U.S. workers used generative AI every workday. Among U.S. workers who had used it in the previous month, 31.9% said they used it at least an hour per workday. The article estimated that 1.3% to 5.4% of total work hours across all workers were assisted by generative AI. In November 2024, surveyed AI users self-reported time savings equal to 5.4% of their work hours; this is a survey estimate, not a measured gain for IT services firms.
These figures illustrate why access and intensity should be distinguished. They are U.S. workforce survey results, not benchmarks for a particular organization or service line. Federal Reserve Bank of St. Louis, “The Impact of Generative AI on Work Productivity” (February 2025)
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Pair output and speed with quality
Count completed work, but distinguish a draft or suggestion from work accepted by a client, production system, or internal reviewer. Pair volume and elapsed time with evidence of whether the result was correct, useful, safe, and durable.
For software delivery
- Track accepted tasks and lead time alongside defects, incidents, review findings, change failures, rework, and security issues.
- Include maintainability indicators and downstream effects when the workflow can affect future development or support.
- Record review burden: AI output that takes substantial human correction may shift effort rather than remove it.
For service and client delivery
- Measure resolution quality, repeat contacts, SLA attainment, escalations, and customer satisfaction where applicable.
- Separate an answer generated quickly from an issue resolved correctly and durably.
- Track client acceptance and any quality-related cost or commercial consequence.
In a combined analysis of three randomized field experiments involving 4,867 software developers, Microsoft Research estimated a 26.08% increase in completed tasks among users of the AI coding tool, with a standard error of 10.3%. The individual experiments were noisy. This is evidence about selected software-development tasks and study settings, not a forecast for other IT-services workflows. Microsoft Research, “The Effects of Generative AI on High-Skilled Work” (June 2025)
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Translate operational change into financial value
Build an explicit bridge from the measured workflow result to the organization’s finances. For each benefit, state how it is expected to materialize and whether it has actually been realized. Potential paths include:
- Lower expenditure: less paid labor, external spend, or other cost, without moving the work elsewhere.
- Avoided cost: capacity that prevents an otherwise necessary hire or outsourcing commitment.
- Additional valuable capacity: more accepted work delivered with existing resources, if there is demand and the organization can staff, schedule, and bill it.
- Revenue or cash-flow effects: new work won, increased billable delivery, or faster revenue realization that can be attributed to the workflow change.
- Quality-related value: reduced cost of defects, rework, service failures, or client harm, when the avoided cost can be substantiated.
Then subtract the full cost of implementation and operation. Include licenses or inference, integration, data preparation, security and governance, training, human review, change management, and rework. Report gross benefit separately from net result; state the time horizon, assumptions, and which amounts are realized versus forecast. This is a practical measurement framework, not a formula prescribed by any one study.
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Professional-services survey evidence shows that organizations use multiple indicators rather than a single productivity number. In Thomson Reuters’ 2025 report, 21% of respondents said their organization measured generative AI ROI. Among those measuring ROI—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 are survey responses about measurement practices, not verified financial outcomes. Thomson Reuters, “2025 Generative AI in Professional Services Report”
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsPayback can also take longer than a pilot suggests. In Deloitte’s 2025 survey of 1,854 executives in Europe and the Middle East, supported by 24 interviews, only 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 a broad survey finding, not an IT-services-specific benchmark. Deloitte, “AI ROI: The paradox of rising investment and elusive returns” (2025)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Segment results and report uncertainty
Do not let a team-wide average conceal where AI helps, has no effect, or makes work worse. Break results down by task, service line, role and experience, client context, and usage intensity. Include neutral and negative outcomes as well as gains. State the sample size, observation period, baseline, comparison method, adoption level, quality measures, and uncertainty alongside headline results.
Re-measure when workflows, models, or policies change; an earlier estimate may no longer describe current work. The OECD’s 2025 review notes potential harm when generative AI is applied to tasks for which it is not effective, so task fit and risk belong in any comparison of use cases. The International Labour Organization’s review, published 1 June 2026, describes gains as real but often unverified and uneven. OECD, “The effects of generative AI on productivity, innovation and entrepreneurship” (2025); International Labour Organization, “The impact of GenAI on jobs, productivity and work organization” (1 June 2026)
Keep productivity evidence separate from margin claims
Evidence that a workflow is faster or that an organization reports an ROI measure does not, on its own, show that AI caused a higher profit margin. 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. Those are correlations between delivery practices and margins, not proof that AI raised margins. Use such findings to frame operational questions, not as a causal estimate for an AI business case. McKinsey, “How to optimize IT productivity for revenue growth”
When evaluating alternatives, compare task fit and risk; usage among the eligible population; throughput and cycle time; quality, rework, security, and client outcomes; implementation and ongoing costs; the ability to monetize or otherwise use released capacity; and the strength of the evidence and time to payback. Productivity percentages from different studies should not be ranked as if equivalent when tasks, populations, outcome definitions, or study designs differ. The DORA 2025 publication is also a relevant source for software-development measurement context. Google Research / DORA, “DORA Impact of Generative AI in Software Development” (2025)
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