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Set AI productivity targets as a balanced measurement system, not a quota for AI-generated code. Start with the service or business outcome you want, compare it with a local baseline, and pair any delivery-speed goal with quality, stability, developer-experience, and user-impact measures. AI adoption and suggestion acceptance can show whether people use or trust a tool; neither proves that the team is more productive.
What should an AI productivity target measure?
A useful target measures whether a team can deliver a valuable change more effectively and safely—not how many lines, suggestions, or AI-generated changes it produces. Make the intended outcome explicit first: for example, shorten the time from a validated user need to a safe release, improve a user outcome, or reduce operational burden.
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Then distinguish three layers of measurement. DORA describes developer-experience measures, service-level measures, and organizational measures; each helps answer a different question. A target system should not collapse those levels into a single score. DORA’s 2025 guidance on choosing measurement frameworks emphasizes fitting measures to organizational goals and being able to act on what they reveal.
| Measurement layer | What it helps answer | Examples supported by DORA |
|---|---|---|
| Developer experience | How does work feel for the people doing it, and what might explain a delivery result? | Perceived productivity; burnout; trust in generated code |
| Service or team | Is the cross-functional group responsible for a service delivering changes effectively and safely? | Code quality; review time; technical debt; software delivery performance; stability |
| Organization | Is the work contributing to a broader organizational outcome? | Customer count or another relevant business outcome |
The service-level boundary matters. Measure the cross-functional team responsible for the service, rather than attributing a shared delivery outcome to one developer or one role. A service can move faster while accumulating review delays, technical debt, or operational risk; the separate layers help make that visible.
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Why aren’t adoption and acceptance rates productivity targets?
Adoption and acceptance describe tool use, experience, or trust. They do not establish that useful work is being delivered faster or that customers are better served. Treat them as context or leading indicators, then look for corresponding movement in workflow, service, and business outcomes.
For example, suggestion acceptance can help a team investigate whether a coding assistant fits its work, while trust can reveal concerns about generated code. Neither metric shows whether changes pass review, reach production safely, reduce time to deliver a validated need, or improve a user outcome. A high acceptance rate could coexist with extra review work or avoidable defects; a low one may reflect a mismatch between suggestions and a particular task rather than low team productivity.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →In its 2025 report, DORA says 90% of surveyed respondents used AI at work, more than 80% believed it increased their productivity, and 30% reported little or no trust in generated code. The survey included nearly 5,000 technology professionals. These are report findings about respondents’ use and views, not a target for an individual company or evidence that a given team’s output improved. See DORA’s 2025 report and the September 23, 2025 report announcement.
Keep four kinds of signal distinct when reviewing an AI rollout:
- Adoption: whether and how the team uses AI assistance.
- Experience and trust: whether people find the assistance useful and feel able to evaluate its output.
- Delivery: whether the service’s workflow or delivery performance changes.
- Impact: whether users or the organization get a meaningful benefit.
Combining these signals can help explain a result. Treating one as a substitute for the others can create a misleading success story.
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What does the evidence say about speed and stability?
AI-related productivity gains can accompany delivery risk, so an acceleration goal needs stability and quality guardrails. DORA’s 2025 report describes a positive relationship between AI adoption and throughput and product performance, alongside a negative relationship with delivery stability. The relationship is not a guarantee of what will happen to a particular team. DORA points to automated testing, mature version control, and fast feedback as controls when change volume rises. The report and its findings are available at DORA’s generative AI report page and in the 2025.2 report PDF.
It is especially important not to turn a past percentage into a universal productivity quota. DORA’s 2025 landing-page summary describes earlier findings in which a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. Google Cloud summarized the same earlier findings on May 14, 2025. DORA’s later 2025 report describes the throughput relationship as positive while stability remains negatively related. These are associations from different research years, not causal predictions or promised effects for a local team.
| Finding | What it says | How to use it |
|---|---|---|
| Earlier finding summarized in 2025 | A 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. | Context for why teams should track speed and stability together; not a target or forecast. See DORA’s report summary and Google Cloud’s May 14, 2025 summary. |
| DORA’s 2025 report | AI adoption has a positive relationship with throughput and product performance, and a negative relationship with delivery stability. | Use the directional finding to justify balanced local measurement; it does not supply a universal percentage target. See DORA Research: 2025. |
| DORA’s 2024 infographic | For a 25% increase in AI adoption, it lists associations with documentation quality (+7.5%), code quality (+3.4%), review speed (+3.1%), approval speed (+1.3%), and code complexity (-1.8%). | Keep these as year-specific associations, not enduring benchmarks or forecasts. See the 2024 DORA infographic. |
The shift in reported throughput direction is a reason to resist simplistic rules such as “a given increase in adoption should produce a given increase in output.” The findings concern different research years, and the source material does not establish a single ideal target, a universal observation window, or one best metric. A team’s baseline, service risk, and ability to release safely determine what target is meaningful.
How do you set targets without trading away quality?
Use a sequence that begins with the outcome and baseline, adds measures at the right levels, and names guardrails before the experiment starts. This makes it easier to see whether faster work is genuinely beneficial or merely shifting effort and risk elsewhere.
- Name the outcome. State the change the service or business needs, such as reducing the time from a validated need to a safe release, improving a user outcome, or reducing operational burden. Do not define success as producing more AI-generated code.
- Choose a service boundary and observation window. Decide which service and cross-functional team are in scope, and choose a consistent period for comparing results. The sources do not prescribe a universal window; choose one that makes sense for the service’s work and release pattern.
- Record a local baseline. Capture existing delivery and stability measures, quality indicators, developer experience, and relevant user or business outcomes. Use the same definitions and service boundary when comparing the experiment with the baseline.
- Separate adoption from outcomes. Track AI use, acceptance, or trust as context. Track acceleration through established delivery and workflow indicators, and track impact through product or business outcomes. Do not report an adoption change as proof of a productivity change.
- Pair the goal with guardrails. If the goal is to improve throughput or cycle time, select relevant countermeasures such as code quality, change stability, review time, technical debt, and customer-facing performance. Before starting, state which deterioration would make the team pause or revise the experiment.
- Run a bounded experiment and review the full set. Where feasible, compare the same service over time or like services. Look for a speed gain alongside deterioration elsewhere; adjust workflow, change size, testing, platform support, or the target in response.
- Use results to improve the system. Ask what made the outcome better or worse, and whether the organization can act on what it learned. Do not turn the measures into individual code-volume quotas or a ranking system.
This is a practical synthesis of DORA’s measurement guidance and findings, not a DORA-prescribed quota recipe. DORA’s guidance cautions that “Simple software delivery metrics alone aren’t sufficient. They tell you what is happening but not why it’s happening.” Pairing delivery data with experience and outcome measures can help a team investigate the why, not just reward a faster-looking number.
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Which quality and stability guardrails should teams choose?
Choose guardrails that fit the service and its risk; the sources do not provide universal thresholds. A customer-facing service with high consequences for disruption may need stronger emphasis on stability and customer performance than a lower-risk internal tool. The point is to agree in advance what counts as an unacceptable trade-off, not to apply an arbitrary number everywhere.
| Target or signal | Pair it with | What the pairing helps reveal |
|---|---|---|
| Throughput or cycle-time improvement | Delivery stability and service performance | Whether a faster flow is accompanied by less reliable delivery or worse customer-facing performance. |
| Faster reviews or approvals | Code quality and technical debt | Whether work is moving through review without shifting the cost into maintainability or defects. |
| Higher AI use or suggestion acceptance | Workflow outcomes, quality, and user or business impact | Whether increased use translates into useful, safe delivery rather than just more tool activity. |
| Improved perceived productivity | Burnout and service-level results | Whether the experience improvement is sustainable and consistent with what the service delivers. |
These pairings are a selection aid, not a claim that every service needs every measure. Use indicators that can plausibly detect the failure modes that matter for the service. For example, if a team’s main concern is review burden, review time and technical debt may be more informative than a broad code-volume count. If changes can affect customer availability, stability and customer-facing performance deserve explicit attention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should leaders interpret results and respond?
Read the measures together and treat mismatches as diagnostic information. A faster delivery result paired with worse stability is not an uncomplicated win; a rise in AI adoption without a change in workflow or user outcomes does not establish a productivity gain.
If delivery improves and guardrails remain healthy
Check whether the result persists across the chosen comparison period and service boundary, and whether the intended user or business outcome moved as well. If it did, the team has stronger grounds to continue or cautiously expand the practice. Preserve the practices that made safe delivery possible, including automated testing, mature version control, and fast feedback.
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If speed improves but quality or stability worsens
Use the pre-agreed guardrail to pause, narrow, or revise the experiment rather than celebrating throughput alone. Investigate whether change size, testing, review workload, platform quality, or workflow design is contributing to the regression. Adjust the system and reassess the full set of measures before expanding.
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If adoption rises but delivery does not
Do not raise an adoption target to compensate. Examine whether the assistance fits the tasks, whether generated output requires extra review, and whether workflow or platform constraints prevent any saved effort from reaching users. Adoption can tell you the tool is being used; it cannot explain a missing outcome by itself.
If developer experience and service results disagree
Keep both signals visible. DORA’s framework separates experience from service outcomes because a delivery metric can show what is happening without showing why, while experience measures can illuminate how work feels without proving service impact. Use the mismatch to investigate where a process change helps or burdens people and where that effect appears in delivery.
Why do platform quality and team conditions belong in the target?
Because AI does not operate independently of the engineering system around it. DORA characterizes AI as an amplifier of existing strengths and weaknesses: platform quality, clear workflows, team alignment, and user-centricity affect whether increased output becomes useful delivery. As Nathen Harvey put it in the September 23, 2025 Google Cloud announcement, “AI doesn’t fix a team; it amplifies what’s already there.”
That framing changes the response to a disappointing result. If suggestions are accepted but work stalls in unclear review paths, increasing the adoption target addresses the wrong problem. If output rises but teams lack fast feedback or adequate automated testing, the organization may be increasing change volume without the controls needed to manage its risk. Targets should therefore make room to improve the conditions around the tool, not just measure the tool.
When choosing between rollout proposals, compare them on five dimensions: the level measured (developer, service/team, or organization); speed against stability and quality; adoption and trust against actual delivery outcomes; service risk and ability to test and release safely; and user or business outcome. The evidence supports those dimensions but supplies no numeric thresholds. Set those locally, then revisit them when the service, workflow, or evidence changes.
Frequently Asked Questions
Should AI-generated code count toward an engineer’s performance review?
A code count is a poor stand-in for value because it does not show whether a change solves a validated need, passes review, or creates maintenance work. Keep measurement focused on team and service outcomes rather than using generated volume to rank individuals.
Can a team set different AI targets for different services?
Yes. The target should reflect each service’s business outcome, risk, baseline, and capacity to test and release safely. A single organization-wide percentage can hide important differences between services.
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Treat that as a signal to investigate, not as proof that either perspective is wrong. The saved effort may be going into review, testing, learning, or other work that the chosen delivery measure does not capture; compare experience with workflow and service evidence before changing the target.
How often should an organization reset its AI productivity targets?
The cited guidance does not prescribe a reset cadence. Revisit a target when the service boundary, workflow, risk, business outcome, or evidence changes enough that the original comparison no longer helps the team decide what to improve.
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