Measure the entire delivery system, not the amount of code an assistant generates. Compare AI-assisted work with a credible baseline across four layers: throughput, delivery stability, product or user outcomes, and developer experience. Include the time spent prompting, reviewing, testing, verifying, integrating, and reworking generated code. Current evidence is mixed: controlled studies report different task-level effects, while DORA’s 2025 organizational research found AI adoption associated with higher throughput and product performance but lower delivery stability. Those relationships do not prove that AI caused the results.
What counts as an improvement in software delivery?
A useful test starts with an outcome that matters to the organization. “The tool produced more code” is an activity measure. “More completed, accepted changes reached users without unacceptable defects, in less total effort” is a delivery outcome.
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Define completion before the pilot begins. For example, a task might count only when its change is merged, passes the team’s required checks, is deployed, and remains within agreed reliability and defect limits for a stated observation window. The exact definition should fit your product and release process, but it must be applied consistently to AI-assisted and comparison work.
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Delivery has several dimensions that can move in opposite directions:
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- Throughput: how much valuable work reaches users and how long it takes to move through the system.
- Stability: whether releases create failures, rollbacks, incidents, or recovery work.
- Quality and rework: defects, rejected changes, reopened tasks, review corrections, and maintenance burden.
- Product or user impact: adoption, task success, performance, reliability, revenue, or another outcome tied to the product’s purpose.
- Developer experience: perceived speed, ease, quality, satisfaction, friction, and cognitive load.
A rollout is an improvement only when the gains you value survive these checks. Faster drafting that creates more review and incident work is not faster delivery.
Use a balanced measurement model
Delivery metrics show what happened, but they rarely explain why. DORA’s 2026 guidance says, “Simple software delivery metrics alone aren’t sufficient. They tell you what is happening but not why it’s happening.” Pair operational measures with experience and outcome measures. DORA points to speed, ease, and quality (SEQ), SPACE, and HEART as complementary ways to structure that evidence.
| Layer | Questions to answer | Possible measures | Interpretation risk |
|---|---|---|---|
| Throughput | Are valuable changes moving through the system faster? | Completed changes reaching production, lead time, deployment frequency, and queue or waiting time | A larger change count can reflect smaller or lower-value work. |
| Stability | Did speed come with more operational harm? | Failed changes, rollbacks, incidents, recovery time, and emergency fixes | A short observation window can miss delayed failures. |
| Quality and rework | How much additional correction did the output require? | Defects, review revisions, reopened work, test failures, and post-release maintenance | Teams differ in how they record defects and rework. |
| Total effort | What happened to the whole work cycle? | Time spent drafting, prompting, reviewing, verifying, testing, integrating, and repairing | Calendar time alone hides parallel work and interruptions. |
| Developer experience | Did work feel easier and more effective? | Reported speed, ease, quality, trust, satisfaction, and friction | Perception can diverge from observed task results. |
| Product or user outcome | Did users receive a better result? | Task success, adoption, latency, reliability, retention, or another product-specific goal | Many factors besides the coding tool affect user outcomes. |
Lines of code accepted can indicate tool usage, but they should not be your success metric. DORA warns that AI can inflate generated volume and recommends measures tied to organizational goals.
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Choose the unit of comparison
Decide whether you are comparing tasks, pull requests, releases, teams, or products. Record the task type and complexity so routine maintenance is not compared with unfamiliar architecture work. Define which developers are included and whether AI use is optional, required, or prohibited in the comparison condition.
Prefer randomization or a staggered rollout
The strongest practical design randomly assigns comparable tasks or developers to AI-assisted and comparison conditions. If that is not feasible, use a staggered rollout with teams or services that adopt at different times and preserve a credible group that has not yet adopted the tool.
A simple before-and-after chart is easy to produce but easy to misread. Task mix, seasonal demand, staffing, incidents, process changes, and a new test system can all change delivery at the same time as the AI rollout. Record those events and avoid attributing every movement to the tool.
Define the success rule in advance
Write down the minimum improvement that would justify adoption and the deterioration that would stop or pause the pilot. For example, you might require faster completed delivery while allowing no material increase in escaped defects or recovery work. Specify the observation period long enough to include integration and production effects, not only coding sessions.
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Capture uncertainty and subgroup differences
Report ranges, confidence intervals, and the number of observations. Separate results by task type, developer experience, repository familiarity, and tool version. A single average can hide a benefit for one group and a penalty for another.
Instrument the whole workflow, not just code generation
For every sampled task, record the time and outcome of each stage:
- Preparation: understanding the requirement, locating relevant code, and forming a plan.
- Drafting and prompting: writing code, tests, documentation, and prompts or context supplied to the tool.
- Review: reading the generated output, comparing alternatives, and requesting revisions.
- Verification: checking assumptions, security implications, dependencies, edge cases, and factual claims.
- Testing: running existing tests, writing new tests, diagnosing failures, and validating behavior outside the happy path.
- Integration: resolving conflicts, updating interfaces, completing approvals, and deploying.
- Rework and maintenance: fixing defects, reverting changes, answering follow-up questions, and repairing code after release.
DORA’s 2026 qualitative analysis found that code generation and information seeking were visible AI use cases, while auditing, verification, code review, testing, hallucinations, and knowledge limits created friction. A tool can shorten the drafting stage while lengthening the total task. Your instrumentation must make that trade-off visible.
Track throughput and stability together
Throughput measures should be paired with the failure and recovery measures that qualify them. A team that merges twice as many changes but spends more time on rollbacks has not demonstrated a net delivery gain.
| Question | Record | Useful slice |
|---|---|---|
| Did more work reach users? | Accepted changes that completed the team’s release path | By service, task type, and AI exposure |
| Did work move faster? | Elapsed time from an agreed starting event to production completion | Median and distribution, not only an average |
| Did releases become less stable? | Failed changes, rollbacks, incidents, and recovery time | By release and severity |
| Did quality hold? | Defects, escaped bugs, test failures, and reopened tasks | Before and after release |
| Did total effort fall? | Drafting plus review, verification, testing, integration, and rework time | Per completed accepted change |
Use the same event definitions throughout the pilot. If a team changes what counts as a deployment or incident halfway through, annotate the break in the series rather than treating it as a performance change.
What current evidence says—and what it does not say
DORA’s 2025 organizational findings
DORA’s 2025 report combined more than 100 hours of qualitative work with survey responses from nearly 5,000 technology professionals worldwide. It describes AI as an amplifier of existing organizational strengths and weaknesses.
The report found a positive relationship between AI adoption and software delivery throughput and product performance, alongside a continuing negative relationship with delivery stability. Google Cloud’s announcement reported that 90% of respondents used AI at work, more than 80% believed it increased their productivity, and 30% reported little or no trust in AI-generated code. These are survey responses and reported relationships, not measurements proving that AI caused the outcomes.
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Nathen Harvey, DORA Lead, and Derek DeBellis, Researcher, summarized the implication as: “AI doesn’t fix a team; it amplifies what’s already there.” Platform quality, workflow clarity, automated testing, version control, and fast feedback therefore belong in your measurement plan.
DORA’s 2026 qualitative analysis
A 2026 DORA insight analyzed 1,110 open-ended responses from Google software engineers collected in the third quarter of 2025. Respondents described perceived velocity benefits but also verification overhead, hallucinations, knowledge limitations, and code-review friction. This evidence helps identify mechanisms and questions to measure; it is not a representative causal estimate for every organization.
Google’s controlled enterprise study
A Google-authored randomized controlled trial involved 96 full-time Google software engineers performing one complex enterprise-grade task. The authors’ best estimate was about 21% less time on task with AI, with a large confidence interval. They cautioned that the result may not transfer to other tools, tasks, populations, or periods.
METR’s randomized study
METR randomly assigned 246 tasks among 16 experienced developers working on mature open-source projects. In its early-2025 tool setting, allowing AI increased observed completion time by 19%. Participants estimated a 20% reduction after the study, while their pre-study forecast was a 24% reduction. The authors noted that experimental artifacts cannot be entirely ruled out.
These studies differ in participants, repository familiarity, task design, tools, and measurement. Do not average the estimates into a universal productivity number. Treat each as evidence about a particular setting and reproduce the relevant context when designing your own comparison.
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A practical pilot plan
Step 1: Write the outcome contract
Name the product or business outcome, the delivery event that counts, the quality limits, and the observation window. State who owns each data source and how missing events will be handled.
Step 2: Freeze the measurement definitions
Document task categories, complexity labels, start and end timestamps, accepted-change criteria, defect severity, recovery events, and what constitutes rework. Keep these definitions stable for the pilot.
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Step 3: Record the intervention precisely
Log the tool and model version, enabled features, usage frequency, whether use was optional, and the developer’s familiarity with the repository. “AI-assisted” is too broad to reproduce without these details.
Step 4: Collect baseline observations
Use historical data only when definitions are comparable. Otherwise, collect a prospective comparison period or concurrent comparison group. Note staffing, platform changes, release freezes, incidents, and workflow changes.
Step 5: Run the rollout
Randomize comparable work where possible; otherwise stagger adoption. Do not silently change review requirements for the AI group. Keep testing, approval, and deployment policies equivalent unless the policy change is itself an intervention you intend to evaluate.
Step 6: Review weekly for safety, not for a final verdict
Monitor failed changes, incidents, security findings, and severe defects continuously. A safety stop can be triggered before the full observation window if predefined limits are exceeded.
Step 7: Analyze total effort and outcomes
Compare completed accepted delivery per unit of total effort, then inspect throughput, stability, quality, developer experience, and product outcomes separately. Show distributions and subgroup results, not only a single percentage.
Step 8: Decide, document, and repeat
Record which outcomes improved, which worsened, where uncertainty remains, and what organizational conditions affected the result. Re-run the evaluation after a major tool or model change; results from an early-2025 tool setting should not be assumed to describe a later system.
How to compare tools or rollout approaches
Use the same comparison axes for every option:
- Task and population: routine or novel work; novice or experienced developers; familiar or unfamiliar repositories.
- Tool and exposure: exact tool, model, version, permitted features, usage frequency, and optional versus required use.
- Delivery: throughput, lead time, deployment frequency, and accepted work that reaches users.
- Stability and quality: failed changes, recovery time, defects, rework, test failures, and review revisions.
- Total effort: drafting, prompting, review, verification, testing, integration, and maintenance.
- Developer and user outcomes: perceived speed, ease, quality, satisfaction, friction, and product impact.
- Organization: platform quality, workflow clarity, testing, version control, and feedback speed.
This structure prevents a tool with impressive drafting speed from winning simply because its costs appear in another team’s review or operations queue.
Common measurement mistakes
Using lines of code as productivity
Generated volume can rise because the tool is verbose, duplicates logic, or creates code that later gets deleted. Treat accepted lines as an adoption signal at most, never as proof of useful delivery.
Reporting developer sentiment as the result
Perceived speed and ease are valuable experience measures, but the METR study illustrates why they must be reported separately from observed completion time.
Ignoring verification and review
If engineers spend less time typing but more time checking assumptions, tests, security, and generated explanations, the net task cost may be unchanged or higher. Capture those activities directly.
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Pooling unlike work
Mixing a familiar, repetitive task with a novel architectural change can produce a misleading average. Stratify or match tasks before calculating an overall effect.
Attributing correlation to causation
AI adoption may coincide with platform investment, hiring changes, process improvements, or leadership initiatives. A comparison design and a context log are more informative than a chart with an adoption date.
Stopping at merge
A merged change is not necessarily a delivered outcome. Include deployment, production behavior, defects, and recovery so delayed costs are counted.
A decision rule for leadership
Adopt or expand an AI coding tool when the evidence shows a repeatable improvement in the outcome contract, total effort, and user value without exceeding the predefined stability and quality limits. Keep the rollout limited when gains appear only in drafting or sentiment. Pause it when failures, security issues, or rework exceed the safety thresholds, even if throughput rises.
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