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Measure AI coding tool adoption and engineering impact separately. Usage telemetry can show who has access, which features engineers use, and how often; it cannot establish on its own that teams deliver more value. Pair adoption data with delivery, quality, operational, and developer-experience measures, then interpret changes against a clear baseline and a credible comparison.
Start by defining what you want to learn
Before collecting metrics, specify the evaluation’s unit and scope. A task-level study, a team workflow review, and an organization-wide rollout answer different questions. Name the tools and features in scope, what counts as adoption, and which engineering outcomes matter. If several tools or features are available, record exposure to each rather than treating all AI use as one uniform intervention.
Keep those definitions stable across baseline and follow-up periods. Decide in advance what time window you will analyze and how you will compare results. Without these choices, a change in a dashboard may reflect a different mix of users, work, or features rather than a change in how well engineering is going.
Measure adoption without mistaking it for impact
Track reach and activity
Begin with access and activation: licenses allocated as a share of purchased licenses, and unique daily, weekly, or monthly active users. Add usage frequency and movement between adoption cohorts to see whether use is expanding, steady, or fading. These measures help identify access, onboarding, or workflow friction; they do not show whether engineering outcomes improved.
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Look at feature-level use
Where the tool reports them, examine suggestions shown and accepted, chat interactions, agent use, and usage by language or mode. Interpret each measure in the context of the feature: high activity may show engagement, but raw acceptance rates or lines of accepted code are not a score of engineer effectiveness.
DORA’s 2025 report lists allocated licenses, daily active users, suggestions generated, chat exposures, suggestions accepted, and accepted lines of code as possible early-adoption signals. It cautions that “On their own, these metrics do not assess the impact of using coding assistants.” The report also says metrics should drive conversations, support decisions, and help teams prioritize improvements, rather than serve as a complete verdict.
Understand what your reporting includes
GitHub’s Copilot usage metrics documentation distinguishes daily and weekly active users, active licensed users, suggestion acceptance, feature engagement, adoption-cohort distribution, and an adoption multiplier. The multiplier connects engaged and passive users with pull requests merged per user and time to merge. These dashboard measures can guide investigation, but an association between engagement and those outcomes does not establish that engagement caused the difference.
GitHub also documents important reporting boundaries: dashboard charts do not include Copilot CLI usage, and its user-team report must be joined with per-user usage metrics to construct team-level measures. Team metrics are therefore not simply pre-aggregated values in every report. Check the dashboard’s scope before comparing teams or interpreting a missing activity signal.
Choose outcomes that reflect engineering value
Use a small set of outcomes tied to the intended benefit, and pair speed or volume with measures that can reveal costs. The right measures depend on the organization’s work and on what it can collect consistently.
- Delivery: completed and merged work, throughput, time to merge, and end-to-end lead or cycle time, using consistent definitions of the work counted.
- Quality: review rework, defects, escaped defects, and maintainability or test outcomes the organization already measures reliably.
- Operational performance: service reliability, change-related incidents, recovery time, and deployment outcomes.
- Developer experience: perceived usefulness, cognitive load, satisfaction, flow, and time available for valuable versus repetitive work.
- Business outcomes: customer or mission measures where a plausible connection to the engineering work can be established.
More code or more pull requests may mean more activity without proving more value. GitHub’s impact dashboard connects adoption cohorts to pull-request output and merge time; use those measures as prompts to examine the work and workflow, not as causal proof by themselves. Keep work type and team composition visible: a complex maintenance task is not directly comparable to a small routine change.
DORA’s 2025 report frames metrics as decision and feedback aids, gathered through conversations, surveys, and system telemetry, each with different precision. Choose measures suited to the organization’s situation and supplement them with relevant internal measures rather than treating a standard set as universally sufficient.
Compare results in a way that supports a fair conclusion
Establish the baseline and comparison
Record baseline definitions and periods before rollout. When practical, randomly assign access or use a comparable group that has not yet received it. If neither is feasible, compare similar teams or tasks over time and document other changes that could affect the result, such as staffing, project mix, release policy, incidents, seasonality, or parallel process improvements.
Report the limits with the result
State the sample size, period, exposure, comparison method, and uncertainty. Self-reported time savings can explain how engineers experience a tool, but should not stand alone as measured productivity. Observational cohorts and dashboards show associations; randomized studies can support stronger causal claims within their tested setting, but do not automatically generalize to every organization.
Two studies illustrate why the design and setting belong beside any headline figure:
| Study | What it reported | How to interpret it |
|---|---|---|
| GitHub and Accenture, 2024 | In this study, 67% of participants reported using GitHub Copilot at least five days per week; average reported use was 3.4 days per week. The study reported an 8.69% increase in pull requests per developer. | The usage figures are participant reports, not an adoption target. Attribute the pull-request result to the study’s setting and methodology; it is not a forecast for other teams. The authors used randomized assignment for a trial and separately analyzed company-wide adoption, combining DevOps telemetry with survey responses. |
| METR, 2025 | A randomized trial involving 16 experienced open-source developers and 246 tasks found that allowing the tested early-2025 AI tools increased task completion time by 19% in that setting. | The participants worked on their own mature open-source projects. This bounded result is not a prediction for all developers or tasks. Participants estimated a time reduction despite the measured slowdown, illustrating why perception and observed outcomes should be reported separately. |
Read modeled estimates as estimates
DORA’s 2025 report also presents modeled estimates for a 25% increase in AI adoption, with 89% uncertainty intervals. Its plotted estimates are a 2.2% increase in productivity, 2.1% increase in job satisfaction, and 0.4% increase in flow; a 2.6% decrease in time spent on toilsome work and a 2.6% decrease in time spent on valuable work; and a 0.6% decrease in software delivery performance. These are report estimates with uncertainty, not guaranteed effects or a universal expected lift.
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Review adoption and outcome measures together at a team cadence, and invite engineers to explain what the numbers may miss. Ask which tasks benefit, where generated changes add review or testing effort, and whether tool use is changing the work in the intended way.
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- Low adoption: investigate license access, onboarding, training, and workflow fit; do not treat low usage as an individual performance problem.
- High adoption without better outcomes: examine task mix, quality, bottlenecks, review burden, and whether the tool fits the work.
- Higher throughput with weaker guardrails: check defects, reliability, and rework before calling the change an improvement.
Use what teams report and what the outcome measures show to adjust enablement or workflow, then continue tracking the same definitions. There is no universally established adoption target or productivity lift that makes sense for every engineering team.
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