The Tool Desk
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Measure AI’s effect by comparing similar software work with and without AI, then judge the results by accepted, quality work—not by licenses, suggestions, or lines of code. Track completion time alongside rework, review effort, delivery stability, developer experience, and, where relevant, user or business outcomes. The result applies to the people, tasks, tools, and period you measured; it is not a universal productivity forecast.
What does software development productivity mean?
For an AI coding tool, productivity is not simply how much code appears on screen or how quickly a first draft is produced. A useful measure asks whether a developer or team delivers more accepted, valuable software for the effort invested, without shifting excessive work into review, correction, maintenance, or operations.
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That definition matters because activity can rise while useful output stays flat. Code may be more verbose but functionally equivalent, or a project’s work may be split into smaller tasks without reducing the total work. In their 2025 randomized study, METR’s authors explicitly warn that changes in code volume and task counts can misrepresent productivity for these reasons.
There is no single metric that captures all of this. A practical measurement combines leading indicators of tool exposure with outcomes at the individual, team or service, and organizational levels. Keep those measures visible as separate dimensions rather than compressing them into one score that can hide trade-offs.
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Separate exposure from impact
Access, active use, suggestions generated, and suggestions accepted can show whether the intended group is encountering and using a tool. They cannot, by themselves, show that the tool improved results. DORA’s official guidance makes this distinction directly: adoption metrics alone do not assess the impact of coding assistants.
Use exposure data to explain an outcome, not substitute for one. For example, if a team reports no change in accepted work, usage data may help distinguish a tool that was rarely used from one that was widely used without improving the measured outcome. Neither explanation changes the need to measure the outcome itself.
Which outcomes should I measure?
Use a balanced set of measures: time to accepted completion, accepted work, quality and rework, delivery stability, developer experience, and user or business value when it fits the decision. Each tells you something different, so define the measures before looking at results.
| Measurement layer | Useful measures | Question it helps answer |
|---|---|---|
| Exposure and adoption | Access, active use, suggestions, accepted suggestions | Is the tool reaching the intended group and being used? This layer alone does not establish impact. |
| Individual work | Task time, perceived productivity, focus or flow, valuable work, toil, job satisfaction, burnout | Does the work feel faster or more valuable, and what is the human cost? |
| Service and team delivery | Accepted work, review time, code quality, complexity, technical debt, documentation quality, delivery throughput and stability | Does the team deliver useful software reliably, including the review and maintenance tail? |
| Organization and users | Customer or user outcomes and relevant business-unit measures | Does the change matter to the organization’s mission and the people it serves? |
Measure accepted completion, not just speed
Record how long a defined task takes and whether the work is accepted under a consistent definition. A faster draft is not a faster completed task if it needs substantial correction or fails acceptance. Decide in advance what counts as completion—for instance, the team’s normal acceptance point—and apply that same rule to both comparison conditions.
Pair time with the amount of accepted work so a time reduction is not mistaken for higher output when fewer tasks were completed. Conversely, task counts need context: task size and difficulty can differ, and teams can divide work into smaller units. Where possible, classify task type and complexity and report the distribution of work rather than relying only on a raw total.
Include quality, rework, and stability
Track the downstream effort and consequences of the change. Relevant measures include time spent reviewing AI-assisted work, corrections or rework, code quality, complexity, technical debt, and documentation quality. Add delivery stability or service outcomes if the decision concerns production software. A gain in first-pass speed can be offset by more review, defects, or maintenance work later.
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Use quality definitions that fit the work and can be applied consistently. A measure that changes between the AI and comparison groups—for example, different review expectations—makes the comparison hard to interpret. Establish the criteria and observation window before the experiment begins, and record whether the team’s review policy or delivery process changed during measurement.
Ask developers about the work, too
Developer experience is an outcome, not a decorative survey item. Ask whether the tool helps people focus on valuable work or reduces toil, and consider job satisfaction, flow, and burnout. A perceived productivity measure can provide context for observed delivery changes, but self-reports should not stand in for accepted work or quality.
DORA’s framework treats individual productivity, valuable work, toil, flow, job satisfaction, and burnout as relevant alongside service and organizational measures. That broader view can reveal trade-offs that a narrow time metric misses—for example, work that appears quicker but feels more fragmented or creates extra cognitive overhead.
How do I set up a credible comparison?
Define the decision, choose comparable work, and establish the comparison condition before collecting results. Random assignment is a strong option when it is practical, but any design should document how the work and participants differ between conditions.
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- State the decision and expected mechanism. Specify what work the AI is meant to improve, who is expected to benefit, and over what period. Make the intended change testable: for example, reducing time to resolve a defined class of issue, increasing tested feature throughput, or reducing manual toil without increasing defects.
- Choose the unit of comparison. Decide whether to assign AI access by task, participant, team, or rollout stage. Randomizing tasks or participants can help produce a credible comparison when feasible. The cited trials used randomized designs, but they do not prescribe one universal organizational protocol.
- Make the work comparable. Compare similar task types and levels of difficulty, and account for developer experience. If AI use is voluntary, people who choose to use it may differ from those who do not; do not treat that self-selection as random assignment. In a staged rollout, preserve a comparison group where possible. If you use historical work instead, document how its tasks, staffing, process, and calendar period may differ.
- Record the intervention and context. Capture which tool and model version were available, whether participants had access, whether they actually used it, task type and difficulty, developer experience, review policy, and calendar period. A tool or workflow may change during the measurement window, so report the version and dates rather than treating “AI assistance” as a stable intervention.
- Predefine outcomes and rules. Set the definitions of task completion, acceptance, quality, rework, and the observation window before examining the result. Select task time and accepted work as core measures, then add relevant quality, review, stability, and developer-experience measures. Include user or business outcomes when those outcomes matter to the decision.
- Analyze uncertainty and variation. Report how many people and tasks were included, the estimated effect, an uncertainty interval or standard error, and meaningful subgroup differences. Show distributions where possible. An average can conceal that some work improved while other work slowed down.
- Re-measure after changes. Repeat the measurement when the model, tool, workflow, task mix, or review process changes enough to affect the result. Use what you learn to improve the system and work practices, and watch for sustainable performance rather than assuming an early result will persist.
What to do when a randomized trial is not practical
A staged rollout or comparison with historical work may be more workable in a production organization, but it is less persuasive if the groups or periods are unlike one another. Make the limitation explicit: identify which teams or tasks received access, what comparison was used, and what changed at the same time. Do not report a before-and-after difference as if it isolates the tool’s effect when other changes could explain it.
Keep the outcome definitions and data collection consistent across groups. If a comparison group is not available, the measurement can still describe what happened after adoption, but it cannot establish as clearly what would have happened without the tool. That distinction is important when deciding whether an observed change warrants broader deployment or further evaluation.
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How should I interpret published AI productivity results?
Published randomized studies show that measured effects can differ substantially by task, population, tool, and outcome. Treat each estimate as evidence about its own setting—not as a percentage to apply to your team’s budget or forecast.
| Study and setting | What was measured | Result reported for that study | Important qualification |
|---|---|---|---|
| Microsoft Research, three company field experiments, reported June 2025 | Completed tasks across randomized trials at Microsoft, Accenture, and an anonymous Fortune 100 company | Across 4,867 developers, the combined result was a 26.08% increase in completed tasks; standard error was 10.3%. | The individual experiments were noisy and results varied. This is a task-throughput result in those company settings, not a universal productivity forecast. |
| METR, randomized trial of experienced open-source developers, 2025 | Completion time for 246 tasks in repositories familiar to 16 participants | Allowing early-2025 AI tools increased task completion time by 19% in this study. | The finding applies to the study’s experienced developers, familiar repositories, tasks, and tools. The authors stress the specific context. |
| IEEE conference abstract, randomized Google enterprise task, 2025 | Time on a complex enterprise-grade task among 96 full-time Google software engineers | The best estimate was about 21% less time with AI. | The abstract notes a large confidence interval and cautions that the result may not transfer to other tools, tasks, or periods. |
These results do not conflict in a way that can be resolved by selecting the largest percentage. One study measured completed tasks across company field experiments; another measured completion time on familiar open-source work; the third concerned time on a complex enterprise task. Those are different populations, interventions, and outcomes.
The studies are useful for a more practical reason: they show why local measurement matters. A team should not expect the task-throughput estimate from one company setting, the slowdown in another task environment, or the time reduction in a particular enterprise task to predict its own result. Report your own population, task mix, tools, duration, and uncertainty alongside any external comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I connect productivity to cost and organizational value?
For a finance or investment decision, compare the measured change in useful work with the full costs and consequences of introducing the tool. The evidence summarized here does not establish a universal price, savings figure, or return on investment; those depend on an organization’s own spending, workflow, and results.
Start by defining the cost categories relevant to the decision, such as tool access, implementation and enablement effort, and any additional review or remediation work. Then compare them with the outcomes the organization values: accepted work, delivery stability, reduced toil, or a specific user or business result. Use actual local cost data and clearly state the period and population to which it applies.
A simple decision framework is to ask whether the measured benefit is both valuable and durable enough to justify the costs and trade-offs. Avoid translating suggestion counts or a one-time increase in task activity directly into financial savings. If the intervention appears to speed one task class but increases review effort elsewhere, preserve that difference in the analysis rather than hiding it in a blended average.
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DORA’s 2025 report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. It characterizes AI as an amplifier of organizational strengths and dysfunctions. For measurement, that is a reminder to examine the surrounding delivery system—such as workflow, quality practices, and organizational constraints—as well as the assistant itself.
What can make an AI productivity measurement misleading?
Most misleading conclusions come from measuring the wrong proxy, comparing unlike work, or treating a single estimate as a stable guarantee. Check the design and interpretation before using a result to make a rollout or investment decision.
- Counting adoption as success: access, daily use, or accepted suggestions indicate exposure, not impact. Pair them with outcomes.
- Counting lines of code or tasks without context: code can be more verbose without adding function, and task boundaries can change. Track accepted work and quality with consistent definitions.
- Comparing unlike tasks or people: differences in task difficulty, developer experience, or work type can drive apparent gains or losses. Record these factors and use comparable groups where possible.
- Ignoring the downstream tail: faster drafting can coexist with more review, correction, quality problems, or maintenance work. Include these outcomes in the observation window.
- Reporting only an average: an average may mask uneven results or slowdowns. Include uncertainty and subgroup or distribution detail where the data support it.
- Generalizing beyond the measured context: tool versions and workflows evolve, and results from different studies are not interchangeable. Time-bound the claim and re-measure after consequential changes.
- Assuming immediate gains: DORA cautions that adopting new technology may bring near-term productivity decreases. Evaluate the transition period and sustainable longer-term performance separately when the decision calls for it.
How should I report the result?
A useful report makes it possible for a reader to tell what changed, for whom, under what conditions, and how certain the conclusion is. State the result and its limits together, not as a headline percentage detached from the study design.
- Decision and scope: the work the tool was intended to improve, participating population, task types, and measurement dates.
- Comparison design: how AI access was assigned, what the comparison condition was, and how comparable the groups or periods were.
- Intervention: tool and model version, access, actual use, and relevant workflow or review policy.
- Outcome definitions: what counted as completed and accepted work, how time and quality were measured, and which downstream measures were included.
- Results and uncertainty: sample size, effect estimate, uncertainty interval or standard error, and variation across relevant tasks or groups.
- Trade-offs and limits: changes in review, rework, stability, developer experience, or user value, plus factors that prevent a causal or broad conclusion.
Keep each finding tied to its scope. “In this team, during this period, for this task mix, the measured result was…” is more decision-useful than a claim that AI raises productivity by a fixed percentage. Revisit the result as the tool and work change rather than treating an initial measurement as permanent.
Frequently Asked Questions
Can I use historical data instead of a control group?
Yes, historical work can provide a useful reference when a concurrent comparison is not feasible, but it is vulnerable to differences in task mix, staffing, process, and timing. Describe those differences and treat the result as less conclusive about causation than a well-matched comparison.
Should managers rank individual developers by AI productivity?
A team-level evaluation is generally more appropriate for deciding whether a workflow change helps, because task difficulty and assignment vary between people. Individual usage or output comparisons can confuse those differences with tool impact and may discourage honest reporting.
How often should an organization repeat the measurement?
There is no universal interval established by the evidence cited here. Repeat it when a material change in model, workflow, task mix, review policy, or rollout stage could alter the outcome, and otherwise set a cadence that matches the decision’s time horizon.
What if developers rarely use the tool during the evaluation?
Report low use as an exposure finding and investigate whether access, training, task fit, or workflow friction explains it. A low-use result describes the rollout as implemented; it does not reliably answer what impact wider, sustained use might have.
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