An AI coding tool helps your development team only if it improves the work the team actually ships—not merely how quickly code appears on screen. Measure accepted, maintainable changes after prompting, editing, testing, review, and rework. Results can differ by task, developer, repository, and workflow, so a bounded pilot is more useful than a blanket claim that AI makes developers faster or slower.
What the evidence says—and what it does not
Published findings point in different directions because they measure different people, jobs, tools, and outcomes. The Government Digital Service (GDS) reported perceived benefits in a supported UK public-sector trial; METR measured task completion in a randomized trial with experienced open-source contributors; DORA emphasizes organizational context; and a workplace study examined developer experience and trust. Those results should not be combined into a single forecast for your team.
GDS: perceived time savings in a UK public-sector trial
In a trial conducted from November 2024 through February 2025, GDS made 2,500 AI coding assistant licenses available across central government. It assigned 1,900 licenses across more than 50 public-sector organizations. Its main analysis included 424 survey responses from users in 31 departments; 73% of respondents said they had at least five years of coding experience. The findings describe this particular trial, not a universal effect. Read the GDS trial report.
Respondents estimated average savings of 56 minutes per working day. They attributed 24 minutes a day to code creation or analysis, 21 minutes to reviewing code or analysis, and 10 minutes to learning. These component estimates may overlap, so they should not be added together. GDS cautioned that optimism may have inflated the overall self-reported estimate; it was not an objectively timed result.
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Other respondents reported spending less time searching for information or examples (67%), completing tasks faster (65%), and solving problems more efficiently (56%). Fifty-eight percent said they would prefer not to return to working without an assistant, and average satisfaction was 6.6 out of 10. These are survey responses, not measured delivery gains.
Telemetry for Copilot showed an average acceptance rate of 15.8% for suggested code lines, while 39% of users said they had committed code suggested by an assistant. Acceptance is not the same as useful, reviewed code reaching production. The report also noted missing telemetry for the second month, uneven rollout and support, disruption during the festive period, and that it did not track individuals across repeated surveys.
METR: longer completion times in one randomized trial
In a study published July 10, 2025, METR randomized AI availability across 246 real issues supplied by 16 experienced developers. They worked in large repositories they had contributed to for years; tasks included bug fixes, features, and refactors and averaged about two hours. When AI was available, participants could choose their tools, primarily Cursor Pro with Claude 3.5 or 3.7 Sonnet—frontier models at the time.
Developers took 19% longer on average when AI was allowed. Before the trial they had forecast a 24% speedup, and afterward they still believed AI had sped them up by 20%. The contrast shows why perceived speed and measured completion time need to be tracked separately. Read METR’s study and its limitations.
That result applies to this small group, familiar repositories, and the early-2025 tools studied. METR says it does not establish what happens for most developers or other kinds of work. Learning effects, less experienced developers, unfamiliar codebases, or different tasks could produce different outcomes. The study also explains why benchmark scores may not predict performance on live repository issues: realistic work can include implicit requirements, style, testing, documentation, and human review standards.
DORA: the organization shapes the result
DORA’s 2025 State of AI-assisted Software Development report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. Its central finding is that AI amplifies an organization’s existing strengths and weaknesses; the largest returns depend on the broader organizational system, not just the tools. Treat that as a reason to examine workflow and team conditions, not as a guarantee of return on investment. Read DORA’s 2025 report.
Workplace study: experience is not the same as speed or trust
A mixed-methods workplace study at a large multinational software company combined surveys, a randomized controlled trial, and a three-week diary study. The researchers found that sustained introduction and use increased perceived usefulness and enjoyment, while views about the trustworthiness of AI-generated code remained unchanged. Eighty-four percent of participants noticed positive changes in daily work practices, and 66% noticed changes in how they felt about their work. These are findings about reported experience and beliefs, not proof of faster delivery. Read the study.
Decide what “help” means for your team
Choose a specific work problem before comparing tools. “Developers like it” and “it generates code” are not delivery outcomes. A useful evaluation connects the tool to work the team needs to complete and checks whether the resulting change meets existing standards.
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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 errors- Task completion: Are changes accepted sooner, after all prompting, checking, editing, testing, and review time is counted?
- Quality and maintainability: Does the change pass the team’s review, test, documentation, style, and maintenance requirements?
- Developer experience: Do usefulness, frustration, enjoyment, trust, and willingness to continue change? Track these separately from delivery measures.
- Workflow fit: Does the tool work with the team’s repositories, review practices, documentation, and processes?
- Governance and cost: Do data handling, permissions, security controls, contract terms, and total cost meet current organizational requirements?
Use task categories that match the friction you want to address—such as autocomplete, code explanation, search, test generation, refactoring, or multi-step work—and evaluate them separately where possible. The cited studies do not provide a current feature-by-feature vendor comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a pilot that can answer the question
- Define the outcome and success criteria. Pick the problem to test, such as time spent searching, repetitive code, debugging, test writing, documentation, or slow completion. Specify what an acceptable change looks like before the pilot starts.
- Record a baseline without the assistant. For a period or a set of comparable tasks, log task type and difficulty, developer experience, completion time, review effort, rework, and whether each change meets existing quality requirements.
- Choose representative tasks and a clear tool setup. State which assistant is allowed and how it may be used. Provide stable access and enough onboarding for people to try it meaningfully. GDS reported uneven rollout and support, while METR notes that learning effects and work setting may matter.
- Compare like with like. Compare similar tasks, using a control group or staged rollout where practical. Separate results by task category and developer experience rather than hiding differences in one team average.
- Count the entire delivery path. Track elapsed time to accepted completion, including prompting, checking, editing, testing, review, and fixes. Also record reviewer acceptance, defects or regressions found, tests and documentation, and maintenance or follow-up work.
- Ask about experience separately. Collect usefulness, frustration, enjoyment, trust, and willingness to continue as distinct measures. A tool can feel useful or enjoyable without changing trust or proving faster delivery.
- Make a task-specific decision and revisit it. Keep the tool in workflows where results show a repeatable improvement without unacceptable quality, review, or governance costs. Change or stop its use in workflows where it adds more work. Recheck as tools and team practices change.
Compare tools or rollout choices on the same basis
If you are evaluating more than one assistant or way of rolling it out, use the same representative tasks and acceptance criteria for each. Do not compare one tool’s code-generation rate with another’s time to accepted completion.
| Comparison area | What to evaluate |
|---|---|
| Task fit | Which task categories it supports in your workflow, evaluated separately where possible. |
| Net time | Time to accepted completion, including prompt construction, checking, editing, and review—not time to first generated code. |
| Quality and maintainability | Whether changes satisfy your review, test, documentation, style, and maintenance expectations. |
| Developer experience | Usefulness, enjoyment, friction, trust, and desire to continue, kept separate from delivery results. |
| Team and workflow fit | How the tool works with existing repositories, review practices, documentation, and team processes. |
| Governance and cost | Whether current data handling, permissions, security controls, contract terms, and total subscription costs meet your requirements. The studies cited here do not compare current vendor terms. |
How to interpret your result
A team-wide average can hide where a tool helps and where it hinders. Keep results attached to the task, developer experience, repository, and workflow in which they were observed. If completion time falls but review or rework rises, the apparent speedup may not represent a net improvement. If developers report more enjoyment but accepted delivery does not change, that is still useful evidence—but it answers a different question.
The studies above concern different settings and measures, and the METR experiment used early-2025 tools. AI models, features, pricing, and enterprise controls change quickly. Check current product behavior, privacy, security, and pricing against your organization’s requirements before making a procurement decision.
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