Measuring AI ROI takes more than one number

Measuring the ROI of AI coding tools is difficult because software productivity has no single reliable metric, and comparisons with a hypothetical no-AI baseline are vulnerable to self-reporting errors, selection bias, and changing organizational conditions. Rather than relying on lines of code, pull requests, stories, flow measures, or developer sentiment alone, the proposed approach compares several throughput measures per engineer against past performance and calculates both estimated ROI and the total cost per unit of work, including developer and AI-tool spending. An example shows how higher code output and pull-request volume can coexist with fewer completed stories, revealing shifts in work structure or bottlenecks outside coding rather than a definitive AI verdict. Quality indicators such as change failure rates, bug backlog, and maintenance work must also be monitored, since apparent speed gains may create later costs, while organizations should expect an initial productivity dip as teams learn tools and adapt processes. The Build framework groups assessment into AI leverage, engineering effectiveness, and return on engineering, encouraging teams to track multiple trends and business outcomes over time instead of presenting a single, potentially misleading ROI figure.

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