AI coding assistants can help developers complete more tasks, but faster code generation does not automatically mean faster, safer software delivery. The strongest evidence points to a more practical conclusion: AI amplifies the organization it enters. Teams are more likely to benefit when they redesign the work around the tools—how tasks are scoped, changes are checked, people learn, and outcomes are measured—rather than treating tool adoption as the transformation itself.
What the evidence says about AI and software productivity
There is evidence of increased output, but it measures different things in different settings. Individual task completion, engineers’ reported experience, pull-request flow, and organization-wide delivery are not interchangeable measures of productivity.
| Source and study design | Reported result | What the result does—and does not—show |
|---|---|---|
| DORA / Google, 2025 report; research inputs described by DORA | More than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. | DORA describes AI as an amplifier of existing organizational strengths and weaknesses, and says returns depend on the underlying system. These are the report’s research inputs, not a census of all technology workers or a controlled estimate of AI’s effect. |
| Microsoft Research authors, 2025; pooled analysis of three randomized field experiments involving 4,867 developers | Developers offered an AI coding assistant completed an estimated 26.08% more tasks; the standard error was 10.3%. | The authors characterize the result as noisy. It concerns task completion in the participating companies, not a guaranteed company-wide productivity gain, code quality, or end-to-end delivery speed. |
| Microsoft Research, 2025; mixed-methods workplace study including a randomized trial and a three-week diary study at one large multinational software company | 84% of participants reported positive changes in daily work practices, and 66% noted shifts in how they felt about their work. Perceived usefulness and enjoyment rose with sustained use, while trust in AI-generated code did not change. | These findings describe participant reports and one company’s workplace context. They are not proof that every team will experience the same changes. |
| Anthropic, internal study published December 2, 2025; data collected August 2025 | Employees described being able to take on a broader range of tasks, alongside concerns about technical expertise, supervising outputs, mentorship, and collaboration. | Anthropic cautions that its employees had early access to frontier models and may not represent other organizations. The concerns are signals to monitor, not established workforce-wide effects. |
| GitLab / The Harris Poll, survey announcement published June 23, 2026; 1,528 developers and technology buyers across six countries | 80% of respondents said their organization adopted AI tools faster than it developed policies to govern them; 92% reported governance challenges with AI-generated code. | These are respondents’ reports from a vendor-released survey, not independently verified rates across all software organizations. |
| McKinsey & Company with Sonar, case study | The case reports up to 0.2x higher pull-request throughput, up to 0.4x lower pull-request cycle times, and self-reported productivity gains of 0–80%. | The shorthand corresponds to up to a 20% throughput increase and up to a 40% cycle-time decrease in this case. The results are case-specific; the productivity range is self-reported, not a controlled general estimate. |
The evidence supports neither “AI makes every team faster” nor “AI-generated code is inherently poor.” It shows that task output can rise, while organizational experience, governance, and delivery outcomes depend on context. DORA’s 2025 report page summarizes its position this way: “The greatest returns on AI investment come not from the tools themselves, but from a strategic focus on the underlying organizational system.”
Why faster coding may not mean faster delivery
Writing code is only one part of shipping a change. Software still needs an understandable problem statement, relevant context, review, testing, security checks, integration, and a safe release. If code generation speeds up but these steps remain unchanged—or become overloaded—the queue can simply move downstream.
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More generated changes can also create more work for reviewers and maintainers. A change that compiles may still be difficult to verify, inconsistent with the architecture, or costly to support later. The practical question is therefore not only how quickly an assistant produces code, but whether the whole delivery path can absorb, validate, and maintain the resulting changes.
What software organizations should redesign
Redesign does not mean replacing every existing process or handing entire systems to agents. It means deciding deliberately where AI fits, where human judgment is essential, and how accountability follows the work.
Workflow and ownership
Specify which tasks are appropriate to delegate, what context the tool needs, who reviews its output, and who owns the change after release. McKinsey and Sonar’s case describes a lifecycle in which agents receive context, generate code, verify quality and security, and address issues through feedback. The useful lesson is the connected workflow, not a promise that this pattern will produce the same results elsewhere.
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Make ownership explicit at each handoff. The engineer or team responsible for a change should be able to explain what it does, assess whether it is correct, and respond if it causes a problem. AI assistance does not transfer that responsibility to the tool.
Code quality and engineering foundations
Generated code is easier to evaluate when the codebase has clear structure, tests that exercise meaningful behavior, and architecture that makes dependencies legible. Teams should make technical-debt work part of the operating plan instead of assuming AI can compensate for a codebase that is hard to understand.
Sonar CEO Tariq Shaukat argues in the McKinsey case study that “The companies getting the most out of agentic development are the ones with the strongest foundations.” This is a vendor executive’s perspective, but it fits a practical constraint: a team cannot reliably verify a change it cannot understand.
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Governance, security, and provenance
Set clear rules for approved tools and data handling, and define how AI-assisted changes are reviewed, tested, and attributed. A useful control system makes it possible to identify the source and ownership of a change, investigate incidents, and apply the same security expectations regardless of whether a person or an AI tool drafted the code.
GitLab’s survey announcement identifies reported difficulties distinguishing AI-generated from human-written code, fragmented toolchains, and missing origin tracking. Those findings do not establish that every organization has the same gaps; they do point to concrete questions for an internal review:
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- Can the team trace a change’s origin and identify the accountable owner?
- Do existing security and release controls cover AI-assisted work?
- Can teams investigate and learn from an incident without relying on assumptions about how code was produced?
Skills, mentorship, and collaboration
AI can help engineers work in unfamiliar parts of a codebase, but delegation should not eliminate the practice needed to understand those systems. Engineers need opportunities to reason through implementation choices, inspect failures, and build technical judgment—not just accept a working-looking answer.
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Anthropic’s internal interviews also surfaced concerns about AI becoming the first stop for questions that might otherwise go to colleagues. Managers should pay attention to whether junior engineers still receive useful feedback, whether specialists are consulted on consequential work, and whether teams retain the informal collaboration through which knowledge is shared. These are risks worth checking, not proof that AI inevitably weakens mentorship.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to measure whether the redesign is working
Do not use code volume, accepted suggestions, or assistant usage as a stand-in for delivery performance. Those measures can describe adoption, but they do not tell leaders whether useful software reaches users safely or remains maintainable.
Choose a small set of measures that reflects the team’s goals and follow them together. The following dimensions are a practical synthesis of the evidence, not a universally validated KPI formula:
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- Work completed: Are teams completing more of the work they intended, and is the work useful?
- Flow: Are throughput and cycle time improving across the delivery path, rather than only at code generation?
- Quality and rework: Are defects, failed changes, review effort, and corrective work changing?
- Security and operations: Can teams detect, investigate, and learn from problems in AI-assisted changes?
- Team experience and capability: Do engineers find the workflow useful, and are they still building the skills to understand and maintain the systems?
Establish a baseline before changing the workflow, then compare results over time and across work of similar type. Look for trade-offs: shorter cycle time is not a win if rework, incidents, or maintenance burden rise. Pair delivery measures with feedback from engineers, and investigate unexpected changes rather than attributing them automatically to the assistant.
A practical way to introduce AI into the delivery system
- Choose a bounded workflow. Pick a recurring task with clear inputs, an identifiable owner, and a way to assess the result. Avoid starting with work whose correctness or risk is difficult to evaluate.
- Map the current path. Record how the task moves from request to release, including review, testing, security checks, handoffs, and common delays. This shows where AI might help and where it could create a new queue.
- Set expectations and controls. Define what information can be provided to the tool, which outputs require human review, which checks are mandatory, and who is accountable for the result.
- Run a limited trial against a baseline. Compare task completion and flow with quality, rework, security, and team experience. Treat an early result as local evidence, not a forecast for every team.
- Adapt before expanding. If review capacity, code structure, tests, or traceability become bottlenecks, address those constraints before increasing the volume of AI-assisted work.
- Keep learning in the loop. Ask engineers what became easier or harder, check whether knowledge is spreading or concentrating, and revise the workflow when it undermines maintainability or collaboration.
Martin Harrysson, a McKinsey senior partner, said of the Sonar case study: “What distinguished this effort was the focus on the operating model—not just the tools.” That is the core leadership decision: improve the system that turns code into dependable software, then judge the tools by their contribution to that system.
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