Teams should measure AI coding tools across adoption, usefulness, delivery, code quality, developer experience, and business impact—not rely on accepted suggestions or lines of code as proof of productivity. Set a baseline and success criteria before a pilot, then assess representative work against the team’s usual standards. Published results vary by task and developer population, so no single study can predict what another team will gain.
Build a scorecard around the outcome you need
Start by naming the decision the evaluation should support: whether to expand a pilot, change how a tool is used, compare candidates, or stop using it. Then choose a small set of measures that can answer that question. Google Cloud’s framework separates evaluation into adoption, trust, acceleration, and impact; these are stages in a diagnostic chain, not interchangeable definitions of success.
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A useful scorecard connects early signals—who uses the tool and whether its output is useful—to delivery and quality outcomes, then to the organizational goal the pilot is meant to support. Google Cloud summarizes the distinction this way: “AI-assistance metrics measure Adoption and Trust of AI-assistance but development productivity metrics express the impact.” (Google Cloud, March 19, 2025.)
Keep the scorecard compact enough to inform an actual choice, but retain the component measures behind any roll-up. For example, a favorable average across delivery and adoption should not hide a decline in reliability or a rise in review effort. The right measures depend on the pilot’s purpose; a tool intended mainly for code completion may call for different task examples than an agent expected to handle larger development tasks.
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Measure adoption and usefulness without mistaking them for impact
Adoption: Is the intended group using the tool?
Track active use and relevant interactions, such as suggestions or chat requests, for the eligible population and observation period. Define the denominator: for example, distinguish people who were offered access from people whose roles and tasks made use appropriate. Otherwise, a usage rate can change simply because the counted group changed.
Adoption indicates exposure and whether a tool has entered the workflow. It does not show that developers are completing work faster or that the resulting code is good. Low use can prompt questions about access, fit, training, or workflow friction; high use is not itself a reason to declare a pilot successful.
Trust and usefulness: Are developers keeping helpful output?
Suggestion acceptance rate and accepted lines can help show whether output is being retained. Interpret them as diagnostic measures: an accepted suggestion may still need edits, fail tests, complicate maintenance, or create review work. Rejection and editing patterns, paired with developer feedback, can help explain whether output fits the task and why it does or does not help.
Be precise about what an “accepted” suggestion means in the data you collect. Acceptance can indicate a developer kept an output, but it does not establish whether the final change passed tests, was easy to review, or reduced total effort. If possible, read acceptance alongside the corrections and review needed to complete the task rather than treating the acceptance rate as a score in isolation.
Do not use generated or accepted lines of code as a stand-alone productivity measure. A tool can produce or place more code without improving the time, quality, or effort needed to deliver a change.
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Test whether representative work moves through delivery more effectively
Assess acceleration on work resembling the team’s ordinary tasks, not just a short exercise designed around a tool’s strengths. Compare measures such as coding iteration time and existing software delivery measures over a defined period. Where useful, track completed tickets or story points over time, but interpret them cautiously: teams may define them differently, and task mix can shift the totals.
Include the full workflow that matters to the team. If a change takes less time to draft but more time to correct, test, or review, coding time alone will overstate the benefit. Look at how work moves through implementation and review, and record the human corrections needed to reach the team’s normal completion standard.
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For a team asking whether an assistant improves productivity, look for a consistent change in the work the team actually delivers—not just faster completion of a single benchmark task. Compare the baseline and pilot using the same definitions and explain material differences in task mix or conditions. Existing delivery indicators can be useful, but measures such as ticket counts and story points need context because what is counted and how difficult the work is can vary.
Keep quality and risk guardrails beside speed measures
Assess whether completed work meets the team’s standards for functional correctness, reliability, readability, maintainability, conciseness, and review approval. GitHub’s controlled code-quality study used these as quality dimensions; in a team pilot, evaluate them with suitable automated tests and human review in the team’s own context (GitHub’s study).
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Use existing quality checks wherever possible, and apply the same criteria to tool-assisted and comparison work. A pull-request merge rate can be one signal about whether maintainers or coworkers accept changes, but it is not a complete quality verdict. GitHub described merge rate as one code-quality measure in an enterprise study with Accenture on May 13, 2024; read it alongside review criteria and defects, not by itself (GitHub’s account of the study).
Set quality guardrails before interpreting a speed gain. The relevant question is not only whether work finishes sooner, but whether it does so without unacceptable effects on correctness, reliability, maintainability, or the team’s review standards.
Measure developer experience and workflow costs
Ask developers whether the tool changes their focus, flow, collaboration, or time spent verifying and correcting generated output. Pair direct feedback with workflow data where appropriate: telemetry can show that a tool was used, while developers can explain whether it reduced friction, interrupted concentration, or shifted effort into review.
These outcomes matter because a productivity measure that ignores review burden or collaboration can miss costs elsewhere in the workflow. In a GitHub survey of 500 U.S. developers at companies with more than 1,000 employees, respondents said collaboration and code quality should count more than output quantity and efficiency in performance measurement. That finding describes this particular survey sample, not a universal ranking (GitHub’s survey summary).
Use those experience questions as evidence about how work is changing, not as a substitute for delivery and quality measures. A survey can help identify whether developers feel more focused or whether verification has become burdensome; workflow data and review outcomes can help investigate those explanations. The point is to measure human experience and collaboration alongside output, rather than dismissing them as anecdotes or assuming that more output means a better workflow.
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Connect measured changes to a business goal carefully
Choose the organizational outcome before the pilot and allow an observation period appropriate to that outcome. Google Cloud names time to market, revenue, and market share as possible organization-specific impact measures. Which one matters depends on why the team is evaluating a coding tool.
Do not assume that a change in a delivery measure caused a change in a business KPI. State the expected chain—for example, a workflow improvement that could contribute to faster delivery—and account for other changes during the same period. The available framework does not establish a universal formula for attributing business results to an AI coding assistant.
What published studies do—and do not—tell your team
Published findings demonstrate why local measurement matters. Their results describe specific participants, tasks, and study conditions; they are not forecasts that can be averaged into one expected productivity effect.
- GitHub, 2022: In a randomized experiment, 95 professional developers writing a JavaScript HTTP server finished 55% faster on average with Copilot. Reported task-completion rates were 78% with the tool and 70% without it. This was one bounded exercise and a vendor-authored study, not a prediction for other teams (GitHub’s report).
- METR, 2025: In a randomized trial involving 16 experienced open-source developers completing 246 tasks in mature projects, allowing early-2025 AI tools increased completion time by 19%. The result applies to those developers, tasks, and tools; it does not establish that AI tools generally slow development (METR’s study).
- GitHub and Accenture, May 13, 2024: An enterprise study included DevOps telemetry and treated pull-request merge rate as one measure of code quality from maintainers’ or coworkers’ perspective. Merge rate is one proxy to interpret with review criteria and defects, not a substitute for them (GitHub’s account).
- GitHub, 2023: A survey of 500 U.S.-based developers at companies with more than 1,000 employees asked about developer experience, collaboration, and AI coding tools. Respondents favored collaboration and code quality over output quantity and efficiency as performance measures. The finding is bounded by the survey’s sample and sponsor context (GitHub’s survey summary).
The settings differ in task type, developer experience, tool period, and quality expectations. Use these studies as evidence that outcomes can vary, not as competing estimates from which to select a promised gain. The 55% faster GitHub result and the 19% longer METR result should not be blended into a forecast: they concern different tasks, people, and study conditions.
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When evaluating more than one candidate, define each measure once and apply it consistently to the same kinds of work and participants where feasible. A benchmark can help characterize an agent’s task capability, but benchmark performance is not the same as a productivity gain in the team’s full workflow. GitHub describes combining benchmark evaluations with real-world measures and online experiments for its agentic harness (GitHub’s evaluation account).
| Evaluation axis | What to examine |
|---|---|
| Task performance | Completion on representative tasks, task success, and human corrections required. |
| Delivery | Time or iteration needed to complete and review work, plus the organization’s established delivery indicators. |
| Code quality | Correctness, reliability, readability, maintainability, conciseness, and review outcome. |
| Adoption and trust | Use among eligible users and whether outputs are useful enough to retain; treat both as diagnostic signals. |
| Developer and workflow experience | Focus, flow, collaboration, and time spent reviewing or correcting generated work. |
| Business relevance | Whether observed delivery changes plausibly support the organizational goal selected for the pilot. |
There is no established universal weighting or single composite score. Weight measures according to the work the team expects the tool to support and the risks it must control. Preserve the underlying results so a favorable average cannot conceal a serious quality or workflow problem.
Run a pilot that can answer a real decision
- Choose the decision and success criteria. Agree in advance what result would justify expanding, revising, or ending the pilot. Include delivery outcomes and quality guardrails rather than choosing a single proxy after seeing the data.
- Record the baseline. Capture the relevant existing delivery, quality, and workflow measures before the pilot. Keep definitions consistent so later differences are interpretable.
- Specify the evaluation conditions. Record the intended users, tool and version, eligible tasks, and observation period. Define who is included in adoption measures and what counts as completion, correction, review, and quality failure.
- Use representative work. Include tasks that reflect the work the team actually expects to support. If comparing tool-assisted work with other work, document how the tasks and conditions are similar or different.
- Collect complementary evidence. Combine relevant workflow data with developer feedback, review outcomes, and appropriate quality checks. Each view answers a different question; none should be treated as a complete account on its own.
- Interpret results against the decision. Look for a coherent pattern across adoption, usefulness, delivery, quality, experience, and the selected business goal. Explain meaningful differences in task mix or conditions, and do not claim causation from a proxy alone.
The result should be a decision-ready scorecard, not a headline percentage detached from its conditions. Adoption and acceptance explain whether the tool is entering the workflow; delivery, quality, and developer experience show whether that use is helping the team do its work; the chosen business measure indicates whether the change matters beyond the development process.
Frequently Asked Questions
How do we know if an AI coding assistant improves developer productivity?
Set a baseline before the pilot, then compare representative work using delivery measures and quality guardrails, alongside developer feedback. Look for a consistent pattern across the workflow rather than treating usage or one task result as proof.
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Should we measure accepted AI suggestions or lines of code?
They can help diagnose adoption and usefulness, but neither is a stand-alone productivity measure. Accepted code may still require edits, review, or correction; compare these signals with delivery time, quality, and developer experience.
How should we compare two AI coding tools?
Use the same task types, definitions, observation period, and evaluation axes where feasible: task performance, delivery, quality, adoption and trust, developer experience, and business relevance. Benchmarks describe capability, not necessarily productivity in your full workflow.
How do we run an AI coding tool pilot?
Choose the decision and success criteria first, record the baseline, define participants, tool version, eligible tasks and observation period, then use representative work. Combine workflow data, quality checks, review outcomes, and developer feedback before interpreting results.
The Bottom Line
Evaluate AI coding tools with a pre-agreed scorecard: who uses them, whether outputs are useful, whether representative work moves through delivery more effectively, whether quality and review standards hold, how the workflow feels, and whether any change supports the team’s business goal. Treat adoption and accepted code as diagnostic signals, not proof of productivity.
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