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An AI coding assistant helps your team only if it improves the delivery of code that people accept and can maintain—after prompting, checking, testing, review, and rework are counted. There is no universal speed verdict: results vary by task, developer, repository, tool, and workflow. Test the assistant against comparable work your team actually does, and keep perceived usefulness separate from measured delivery.
What the available evidence says—and what it does not
Published findings point in different directions because they measure different things in different settings. They are useful as reasons to test carefully, not as a forecast of what your team will experience.
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A UK public-sector trial found reported time savings, with important caveats
The UK Government Digital Service (GDS) ran an AI coding assistant trial from November 2024 through February 2025. It made 2,500 licenses available across central government organizations; 1,900 were assigned across more than 50 public-sector organizations. The main analysis included 424 survey responses from users in 31 departments, and 73% of respondents had at least five years of coding experience. Respondents estimated an average saving of 56 minutes per working day. That is a self-reported estimate, not an objectively timed productivity result. The report warns that estimates for different activities may overlap and that optimism may have inflated the total. Read the GDS trial report.
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In that trial, 67% of respondents said they spent less time searching for information or examples, 65% reported faster task completion, and 56% reported more efficient problem-solving. Fifty-eight percent said they would prefer not to return to working without an assistant; average satisfaction was 6.6 out of 10. These are results from this particular supported trial, not general predictions for other teams.
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Acceptance telemetry also illustrates why generated code is not the same as delivered code: for Copilot, the report found a 15.8% average acceptance rate for suggested code lines, while 39% of users said they had committed assistant-suggested code. The report notes missing telemetry for the second month, uneven rollout and support, disruption during the festive period, and that it did not track the same individuals across repeated surveys.
A randomized trial found slower completion in one experienced group
In a July 10, 2025 randomized trial, METR studied 16 experienced developers working on 246 real issues in large repositories they had contributed to for years. The issues included bug fixes, features, and refactors, and averaged about two hours. Participants could choose tools when AI was allowed; they primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet, which were frontier models at the time. Developers took 19% longer on average when AI was allowed. Before the trial, they had forecast a 24% speedup; afterward, they still believed AI had sped them up by 20%. This is one setting where perceived speed and measured task time diverged. Read METR’s study and its limitations.
METR explicitly cautions against applying that result to most developers or kinds of software work. The participants were a small, experienced group working in familiar, mature projects with implicit requirements and high standards. Learning effects, less experienced developers, or unfamiliar codebases could produce different results. The study also distinguishes realistic repository work judged against human review expectations from benchmark tasks that may be tightly scoped and algorithmically scored; the two do not answer the same question.
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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 around the world. It frames AI as an amplifier of an organization’s existing strengths and dysfunctions, and says the largest returns depend on the broader organizational system, not simply on adopting a tool. This is an organizational lens, not a quantified return-on-investment promise for an individual team. Read DORA’s 2025 report.
A workplace study by Jenna Butler, Jina Suh, Sankeerti Haniyur, and Constance Hadley combined surveys, a randomized controlled trial, and a three-week diary study at a large multinational software company. Sustained introduction and use increased perceived usefulness and enjoyment, while views about the trustworthiness of generated code did not change. Eighty-four percent of participants noticed positive changes in daily work practices, and 66% noticed changes in how they felt about their work. These findings describe reported experience and beliefs; they do not establish faster delivery. Read the 2025 workplace study.
Define what “help” means for your team
Start with a specific friction, not a tool demo. Are developers spending too long finding examples, writing repetitive code, debugging, adding tests, documenting changes, or completing another recurring task? Choose an outcome tied to work the team needs to deliver. Faster first drafts, more accepted changes, less review effort, fewer defects, and a better developer experience are different outcomes; decide which matter before the pilot begins.
For each task category, define what counts as done. Use the team’s existing standards for review, tests, documentation, style, and maintainability. A completion-time measure should stop when work is accepted to those standards—not when the assistant first produces code.
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Run a pilot that can show net impact
- Record a baseline. Before enabling the assistant for the pilot work, capture comparable tasks completed without it. Note task type and difficulty, developer experience, elapsed time, review effort, rework, and whether the change met the team’s quality requirements.
- Choose representative work and clear rules. Select bounded tasks that reflect the team’s normal work, and specify which assistant and uses are allowed. Give participants enough onboarding and stable access to use the tool meaningfully; uneven support or rollout can cloud the result.
- Compare like with like. Use comparable tasks and, where practical, a control group or staged rollout. Separate results by task category and developer experience rather than relying on one team-wide average. Record differences in task complexity or repository familiarity that could affect the comparison.
- Measure the whole delivery path. Track time spent prompting, checking, editing, testing, reviewing, and fixing as well as elapsed time to accepted completion. Record reviewer acceptance, defects or regressions found, test and documentation work, and maintenance or follow-up tasks.
- Ask about experience separately. Collect usefulness, frustration, enjoyment, trust, and willingness to continue as distinct measures. Report these alongside delivery results, not as substitutes for them.
- Review by task and decide. Keep the tool in workflows where repeatable improvements outweigh added review, quality, and governance costs. Change the workflow or stop the pilot where it creates more work. Treat the outcome as specific to the tested tasks and conditions.
Compare tools and rollout options on the same work
If the team is evaluating multiple assistants or ways of rolling one out, use the same representative tasks and acceptance criteria for each. The studies cited here do not provide a current feature-by-feature vendor comparison; assess the following dimensions in your own environment.
- Task fit: Evaluate autocomplete, code explanation, search, test generation, refactoring, or multi-step work as separate categories where possible.
- Net time: Compare time to accepted completion, including prompting, checking, editing, and review—not time to first generated code.
- Quality and maintainability: Check whether changes meet review, testing, documentation, style, and maintenance expectations.
- Developer experience: Track usefulness, enjoyment, friction, and willingness to continue separately from delivery measures.
- Workflow fit: Assess integration with repositories, review practices, documentation, and team processes. GDS identifies workflow integration and adaptation as relevant; DORA emphasizes organizational context.
- Governance and cost: Check data handling, permissions, security controls, contract terms, and total subscription cost against your organization’s current requirements. These are procurement checks; the cited studies do not compare current vendor terms.
Make the decision from evidence your team can inspect
A useful evaluation shows where an assistant helps, where it adds overhead, and what the trade-off costs. Keep task-level results visible, include review and follow-up work, and distinguish measured delivery from survey responses about how the tool feels. Product behavior, pricing, privacy terms, and enterprise controls change; verify current details before making a procurement decision. METR notes that it published new data on late-2025 tools in February 2026, which is outside the scope of its July 2025 study described above.
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