AI can help teams complete parts of complex technology projects faster, but buying a coding assistant does not guarantee a shorter schedule or lower total cost. The strongest case for automation comes when teams redesign the workflow around it, verify its output, and measure end-to-end results—including rework, quality, security, and reliability.
How can AI automation reduce project costs?
AI automation can reduce the time people spend on repeatable work such as drafting tests, documenting changes, or preparing a first pull request. Those savings become project savings only if they survive the costs of reviewing and correcting output, maintaining the workflow, training teams, and governing access to data and systems.
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That distinction matters because faster code generation is not the same as faster delivery. If generated work increases review queues, defects, or rework, a team may produce more code without shipping useful changes sooner. DORA describes AI as an amplifier of the delivery system: existing strengths and weaknesses shape the outcome, so tools alone are not a dependable proxy for project performance. DORA’s 2025 report emphasizes strategic improvement to the underlying organizational system.
What the reported numbers do—and do not—show
DORA’s generative AI report, last updated April 13, 2026, found that a 25% increase in AI adoption was associated in its analysis with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. These are reported associations, not a universal causal forecast for an individual team. The same report says 39% of developers trusted AI outputs “a little” or “not at all.” Read DORA’s findings and recommendations.
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Organization-level surveys and case studies show that some teams report substantial gains, but their results are not a promise of typical returns. McKinsey’s November 2025 survey of nearly 300 senior leaders at publicly traded companies included 100 who assessed outcomes across software quality, time to market, team productivity, and customer experience. Among top performers, reported improvements were 16–30% in team productivity, customer experience, and time to market, and 31–45% in software quality. Those figures describe the survey’s top-performer group, not expected gains for every organization. McKinsey explains the survey and its results.
A 2026 McKinsey survey of agentic product and software development reported average time savings of 11.8% and rework reduction of 6.2% across surveyed use cases. For development tasks, it reported 11.2% average time savings and 6.8% rework reduction. The separate measures are a useful reminder: speed and quality-related outcomes should be tracked independently. McKinsey also reported that organizations redesigning processes before adding technology were more than twice as likely to report productivity gains above 20% as those layering AI onto existing processes; this is a survey finding, not proof that process redesign alone caused the difference. See McKinsey’s 2026 account of agentic development.
Can AI speed up complex software projects?
It can speed particular stages, but project-level acceleration depends on whether the whole path—from a clear requirement through implementation, verification, review, and release—gets faster. Automating one step can simply move the bottleneck to another.
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In a case study with three front-runner Sonar teams, McKinsey described an “Agent Centric Development Cycle” built around context-setting, code generation, verification of quality and security, and issue resolution through automated feedback loops. At the end of the pilot, the teams reported pull request throughput up to 2.2 times higher, pull request cycle time up to 3.4 times lower, and self-reported build productivity gains of 50–80%. The case states that not all improvements could be attributed solely to the pilot, so these are case-specific reported results rather than a general benchmark. Read the case study.
The case also describes an example in which agents take a bug report from a collaboration channel, create a Jira ticket, clarify requirements, and draft a pull request. That is an illustration of a possible connected workflow, not evidence that teams should let an agent handle every step without human oversight.
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Why context and verification are part of the work
An agent needs relevant, approved context: the task’s acceptance criteria, repository conventions, documentation, ownership, and the path for escalating uncertainty. Output then needs checks that can catch errors before they become release problems, including automated tests, code review, security analysis, and continuous integration. DORA recommends clear governance and acceptable-use policies alongside automated testing, fast code review, and continuous integration. DORA’s report discusses these practices.
In the McKinsey case study, Sonar CEO Tariq Shaukat said, “The companies getting the most out of agentic development are the ones with the strongest foundations.” He also said, “Agents are more cost efficient and effective when they run on well-structured code. Verification, clean architecture, and close attention to technical debt aren’t taxes on speed; they’re what makes speed sustainable.” These are Shaukat’s statements in the case study, not independently measured conclusions.
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How do you measure AI productivity in software development?
Measure the outcome the project is trying to improve, not just how much AI is used. License counts, prompt counts, or the share of code generated can describe adoption, but they do not establish that a project became faster, cheaper, safer, or more reliable.
Establish a baseline before the pilot
For a bounded workflow, record comparable results before introducing automation. Include the time from work starting to an accepted change, throughput, review and rework effort, escaped defects, reliability, security findings, and labor and tool costs. Define each measure consistently: for example, specify when cycle time starts and ends, and count rework in a way that does not confuse follow-up fixes with new work.
Track the full cost and quality of delivery
During the pilot, pair task completion speed with output quality and downstream effort. Track the time spent reviewing, correcting, testing, and maintaining generated work, as well as training and governance costs. Measure delivery outcomes such as quality, time to market, security, reliability, total cost, employee experience, and customer impact. McKinsey reported that 86% of top-accelerating organizations track outcome metrics such as quality, productivity, and speed; that figure reflects its surveyed organizations, not a requirement or guarantee for every team. McKinsey’s 2026 article discusses outcome measurement.
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Compare like with like
Compare the pilot with a relevant baseline or similar work, accounting for differences in task difficulty, team experience, and release conditions. Report both gains and regressions. A faster first draft is not an end-to-end productivity gain if review time, rework, or incident risk rises enough to erase it.
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- Choose a bounded workflow. Start with recurring, reviewable work such as drafting tests, documenting a change, or preparing an initial pull request—not an entire high-risk delivery process.
- Record the baseline. Capture cycle time, throughput, rework, escaped defects, reliability, security findings, and labor and tool cost using definitions the team can repeat.
- Prepare the workflow and context. Clarify requirements, acceptance criteria, repository conventions, approved data access, ownership, and escalation paths before widening automation.
- Put verification in the release path. Use automated tests, code review, security scans, and continuous integration. Keep human approval for changes whose risk or uncertainty warrants it.
- Pilot with representative teams. Include normal work and report completion speed alongside output quality, review effort, and downstream effects.
- Expand only if end-to-end results hold. Account for verification, rework, training, governance, and maintenance costs before scaling the workflow.
This approach synthesizes recommendations and findings from DORA and McKinsey’s implementation case and 2026 survey article; it is a practical framework, not a guarantee of savings.
What are the risks of using AI to write code?
The central risk is treating plausible output as verified work. Code that appears complete may still fail requirements, introduce defects, create security problems, or be difficult to maintain. The more consequential the change, the more important it is to define who checks it, which controls must pass, and when the agent must stop and escalate.
- Quality and reliability: Generated changes can add rework or destabilize delivery if tests and review do not catch problems.
- Security and data handling: Set clear acceptable-use rules and permissions for the information and systems an AI workflow can access, and include security checks in verification.
- Weak foundations: Poorly structured code, unclear ownership, and unresolved technical debt can make generated changes harder to validate and sustain.
- Misleading productivity claims: Adoption or code volume can rise while throughput, stability, or total cost worsens. Judge results on end-to-end outcomes.
DORA’s reported association between higher AI adoption and lower delivery throughput and stability is a reason to monitor those outcomes during adoption—not evidence that every implementation will produce the same effect. Its guidance on governance, testing, review, and continuous integration addresses controls that teams can build into the delivery system. DORA Research: 2025.
How should teams evaluate automation options?
There is no neutral side-by-side product test in the cited evidence. For an organization choosing an implementation, compare the workflow and controls it enables rather than assuming that a particular class of tool will deliver a preset return.
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Quick Recap
- Workflow coverage: Does it assist one discrete coding task, or connect requirements, development, testing, and release?
- Context and integration: Can it work with approved repositories, tickets, documentation, and existing development processes?
- Verification: What testing, code-quality, security, review, and audit-trail controls are available?
- Governance: Can the organization set data permissions, human approvals, and escalation paths?
- Measured outcomes: Can the pilot establish cycle time, throughput, rework, reliability, quality, security, and total cost?
- Adoption conditions: What learning time is needed, do teams trust the output enough to use it responsibly, and can the underlying codebase support the workflow?
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