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Measure whether your team ships more accepted, useful work per unit of developer time—not how often people use an AI tool or how much code it generates. Compare tool-assisted work with a credible baseline, track review and rework as well as completion time, and set quality and developer-experience guardrails before rollout. Published results range from faster task completion to slower work, so your team’s own evaluation matters more than a headline percentage.
Define what “better productivity” means for your team
Choose the decision you want the evaluation to support: whether to expand access, change how a tool is used, or stop using it for certain work. Then define improvement in terms of outcomes your organization values. A useful starting hypothesis is: “AI access increases accepted work completed per developer-hour without worsening defects, rework, or developer experience.”
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Pick one primary outcome and a small number of guardrails before collecting results. This prevents a large scorecard from making it easy to spotlight whichever metric happened to improve.
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- Primary outcome: accepted tasks, changes, or other meaningful work completed per developer-hour, or time to complete work that meets your acceptance criteria.
- Quality guardrails: review findings, rework, escaped defects, and security issues where your team can measure them consistently.
- Team guardrails: developer satisfaction and well-being, collaboration, and whether work is becoming harder to review or maintain.
Code volume, prompts, suggestions accepted, and lines changed can describe tool activity, but none establishes that the team delivered more value. Software productivity has several dimensions. GitHub’s research uses the SPACE framework: satisfaction and well-being; performance; activity; communication and collaboration; and efficiency and flow. GitHub’s explanation of its Copilot research and productivity measures discusses why activity and speed alone are insufficient.
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Choose a comparison that can support a decision
A before-and-after comparison is easy to run, but it cannot by itself show that AI caused a change. Task mix, staffing, deadlines, codebase changes, or a new release process may also shift during a rollout. Choose the strongest practical comparison and document what could confound it.
Randomize when feasible
For eligible developers or comparable tasks, randomly assign access to the AI tool or current practice. Random assignment helps separate the tool’s effect from differences between people or work, though the result still applies most directly to the population, tasks, and workflow studied. Set rules for access and task assignment in advance, and avoid withholding a tool where doing so would be inappropriate.
Use a phased or matched rollout when randomization is impractical
Roll out access in stages, or compare teams or tasks that are as similar as possible. Capture a baseline before access and record why the groups may differ—for example, experience, repository familiarity, project phase, or task complexity. Treat this as less conclusive than a well-run randomized comparison rather than implying that a simple before-and-after change proves causation.
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Keep the evaluation conditions visible
Record the observation dates, tool and model versions, training, task mix, and workflow changes. Track who was eligible, who participated, and any exclusions. Without that context, a result can be hard to interpret or reproduce, and later changes in tools or practices may make it a poor guide to current work.
Measure the whole path from task to accepted work
Start and stop the clock at meaningful points: define when work begins, what counts as completion, and what makes a result accepted. Measure more than the time needed to produce an initial draft. An apparent speed gain can disappear if code takes longer to review, repair, test, or maintain.
- Completion: count work that meets a consistent acceptance standard, not drafts or pull requests that have not been accepted.
- Time: measure task completion time and, where possible, the developer effort spent on implementation, review, rework, and testing.
- Flow: observe review latency and where work waits between development, testing, security review, and deployment.
- Quality and maintenance: track review changes, reopened work, escaped defects, or other existing indicators that can be compared fairly across groups.
Do not assume that every team can attribute all effort or defects to an individual task. Use measures your current systems support consistently, explain what they omit, and pair them with qualitative feedback rather than manufacturing precision.
Use a balanced scorecard, not a single productivity number
Combine delivery measures with recurring, brief developer surveys or interviews. Ask whether the tool helped with the work, where it added review or correction effort, and whether it affected focus, confidence, or collaboration. Telemetry can show what happened in a workflow; developers can help explain why. Neither gives a complete account on its own.
Organize measures around the SPACE dimensions so an apparent improvement in one area does not conceal a cost in another:
- Satisfaction and well-being: perceived usefulness, frustration, cognitive load, or confidence.
- Performance: whether delivered work meets user, product, or engineering goals.
- Activity: observable work such as changes or reviews; interpret it as context, not as value by itself.
- Communication and collaboration: handoffs, review interactions, and coordination with teammates.
- Efficiency and flow: time and effort from starting work to accepted completion, including bottlenecks.
Segment results to find who benefits and where
A team-wide average can hide meaningful differences. Compare results, where sample sizes allow, across routine and unfamiliar tasks, repository familiarity, experience levels, and tool usage. Note whether developers received training or used AI at different stages of work. These segments can reveal where to refine access or workflow, but a small subgroup result is easy to overinterpret.
Report the number of people and tasks in each comparison, uncertainty around estimates, exclusions, and the evaluation period. If results vary widely or the sample is small, say so. A point estimate is not a guarantee of what will happen on the next project.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published studies can—and cannot—tell you
Published estimates differ because studies measure different outcomes with different participants, tasks, tools, and settings. They are useful evidence that effects can vary, not interchangeable forecasts for your team.
| Study | What was measured and found | How to interpret it |
|---|---|---|
| Microsoft Research, 2025 | Three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company covered 4,867 developers. The combined estimate was a 26.08% increase in completed tasks, with a standard error of 10.3%. The assistant offered intelligent code completions. Study details | The researchers note that each experiment is noisy. This is an estimate across those experiments and settings, not a universal expected gain. |
| GitHub Copilot task experiment, 2022; post updated 2024 | In a randomized experiment, 95 professional developers wrote a JavaScript HTTP server. The Copilot group averaged 1 hour 11 minutes, compared with 2 hours 41 minutes without Copilot; the study reported a 55% faster completion result (P=.0017; 95% confidence interval for speed gain: 21% to 89%). Study details | This was one bounded coding exercise, not a measurement of sustained team-wide productivity. |
| METR, July 2025 preprint | A randomized trial involving 16 experienced open-source developers and 246 tasks in mature repositories found that access to early-2025 AI tools increased task completion time by 19%. Participants estimated a 20% time reduction after completing the tasks. Preprint | This small, specialized study is not a verdict on all tools, developers, or teams; it shows that perceived savings and measured time can diverge. |
These figures are not directly comparable: the studies differ in task realism, participant experience, assignment, outcome definition, tool, observation period, and organizational setting. Compare those conditions before using any result to set expectations for your own team.
Quality needs its own evidence. In a separate randomized GitHub study of 202 valid submissions from experienced developers working on web-server API endpoints, unit tests and blind developer review found that the Copilot-access group had a 53.2% higher likelihood of passing all 10 tests, along with several modest differences on review criteria. The result describes that task and does not establish lower production defect rates across organizations. GitHub’s code-quality study
Organizational conditions matter, too. DORA’s 2025 report draws on survey responses from nearly 5,000 technology professionals and more than 100 hours of qualitative data. It characterizes AI as an amplifier of organizational strengths and dysfunctions, a reason to measure team and delivery-system conditions alongside tool adoption. DORA 2025 State of AI-assisted Software Development Report
Check whether saved time becomes useful capacity
If developers finish a task sooner, find out what happened next. They may complete other planned work, improve tests, help colleagues, or have time absorbed by review, product clarification, security checks, and deployment queues. A tool can make one step faster without increasing the organization’s total useful output if the bottleneck moves elsewhere.
Use the evaluation to identify those constraints. If implementation speeds up but review becomes the queue, a review-process change may matter more than wider AI access. If results improve only on familiar, routine tasks, target those tasks rather than assuming the same effect on unfamiliar or high-risk work.
Turn the result into a clear decision
At the end of the evaluation, state whether the primary outcome improved, whether guardrails held, and how confident you are in the comparison. Include sample sizes and important differences between groups. Then decide whether to expand, narrow, or stop the rollout—or run a better-targeted evaluation if the evidence is inconclusive.
Keep the decision tied to the conditions measured: which tasks, developers, tool version, and workflow were included. Revisit it when those conditions materially change. That produces a more useful answer than treating tool usage or a published percentage as proof of productivity.
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