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AI can help an engineer finish a task sooner without proving that the engineering organization—or the business—received a return. Adoption, perceived time saved, delivery performance, software quality, customer outcomes and financial results are different measures. To make a credible case for AI in engineering, teams need to track usage alongside downstream outcomes, over a defined period, and account for the delivery system in which the tools are used.
Why faster individual work does not prove enterprise ROI
A coding assistant may make a particular task feel quicker, but that observation sits near the beginning of a longer chain of possible value. The relevant questions are whether the saved effort changes team delivery, whether that delivery maintains or improves quality, and whether those outcomes support a business objective. Each link needs evidence; none follows automatically from the one before it.
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Adoption is an input, not a return. A high rate of AI-feature use shows that people are using a tool, but it does not establish that they are shipping more valuable software, reducing defects, or lowering costs. Likewise, a developer’s estimate of time saved is useful feedback, not by itself an organization-wide financial result.
The delivery system changes the result
DORA’s 2025 research characterizes AI as an amplifier of existing organizational strengths and weaknesses. In its words, “AI’s primary role is as an amplifier, magnifying an organization’s existing strengths and weaknesses.” It also says, “The greatest returns on AI investment come not from the tools themselves, but from a strategic focus on the underlying organizational system.” These are findings and statements from DORA’s organizational research publication, not quotations from a named individual.
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This framing helps explain why results can differ across teams even when they use similar tools. A team’s workflow and organizational capabilities may shape whether time saved on a task becomes useful delivery capacity, or is absorbed by bottlenecks elsewhere. When outcomes vary, leaders should examine the surrounding system as well as the tool; attributing every change to AI alone risks confusing correlation with cause.
Measure inputs and outcomes together
McKinsey’s software-development guidance recommends overlaying outcome measures with input measures. That distinction gives engineering leaders a practical starting point: keep track of whether and how AI is used, then compare those signals with results that matter to delivery and the business.
| Measurement layer | Examples to track | What it can tell you |
|---|---|---|
| Adoption inputs | Use of AI features; tasks supported; defect detection | Whether the tools are being used and what activity they are involved in. These are not proof of business impact by themselves. |
| Engineering outcomes | Productivity; speed; software quality | Whether delivery-related results change alongside adoption. Use consistent definitions when comparing teams or periods. |
| System context | Workflow and organizational capabilities | Conditions that may amplify or constrain the relationship between AI use and outcomes. |
| Business value | An outcome tied to the organization’s stated value objective | Whether engineering changes support the value the organization intends to create. No single financial proxy is established as universally suitable. |
These layers answer different questions; combining them makes it harder to mistake visible activity for demonstrated value. Avoid treating lines of code or adoption alone as proof of impact. A single metric is not established as sufficient to show AI ROI.
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Build a comparison that can support a decision
The available guidance supports measuring adoption and outcomes, but it does not prescribe a universal experimental design or one ROI equation. An organization can still make its own comparison more useful by deciding in advance what it will measure and applying the same definitions consistently.
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- State the value objective. Specify the business or engineering outcome the organization wants to improve. Do not assume that a generic measure such as time saved represents value in every context.
- Set a baseline and observation window. Record the relevant measures before assessing a change, and define the period over which the team will observe them. The sources do not establish one universally correct window.
- Track adoption inputs and downstream outcomes together. Include measures such as AI-feature use and tasks supported alongside delivery speed, productivity and software quality. Make definitions consistent across the teams or periods being compared.
- Record local costs and friction. For an organization-specific calculation, account for implementation and operating costs, review, rework and quality effects. These factors are important to measure locally; the cited sources do not quantify them.
- Interpret changes in context. Consider workflow and organizational capabilities when results differ, and avoid presenting a change observed alongside AI use as proof that AI caused it.
What the published survey figures do—and do not—show
McKinsey & Company’s 2025 report draws on a survey of 3,613 employees and 238 C-level executives conducted in October and November 2024. It covers employees and executives across functions and industries, so its enterprise-return findings describe respondents’ reported experience and perceptions. They are not an engineering-only estimate or a causal finding that AI produced a particular return for every organization.
| Reported figure | Scope and interpretation |
|---|---|
| 19 percent of surveyed C-level executives said revenue increased by more than 5 percent; 39 percent reported a 1–5 percent increase; 36 percent reported no change. | Executives’ reported enterprise-wide AI returns across industries in McKinsey & Company’s 2025 survey—not software-engineering-specific results. |
| 23 percent of surveyed C-level executives saw any favorable change in costs. | A broad enterprise perception in the same survey—not a causal estimate of engineering ROI. |
These figures help illustrate that respondents did not uniformly report favorable enterprise returns. They cannot establish whether AI generated a particular engineering team’s results, or predict what another organization will experience.
Where to find frameworks for the next step
DORA’s publications index lists an ROI of AI-assisted Software Development report described as a practical framework for navigating AI adoption, as well as a DORA AI Capabilities Model report with implementation strategies and methods for monitoring progress. McKinsey’s software-development guidance also advocates measuring impact alongside upskilling and change management. Together, these resources reinforce the practical distinction between introducing tools and building the conditions to assess their results.
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