The problem

Not all efficient AI use actually moves the business forward. A engineering team can run a clean, well-structured AI session, spend exactly what they planned to spend, and still ship nothing that matters. Leadership isn't asking"was the session efficient?" They're asking "What resulted from it?" Most teams can't answer that with anything more than a token spend chart.

What we'll cover‍

From session quality to business outcomes‍

Why nailing the six efficiency signals is necessary but not sufficient, and what closes the gap between efficient AI coding sessions and results that count.‍
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Attribution without a perfect data pipeline

You don't need every system wired together to trace AI coding spend to business outcomes. We'll show how to build a credible attribution  picture at different levels of data access, whether or not you have clean Jira and GitHub integration. The goal is a trustworthy picture of impact built directly from the data you alrady have. Spend gets classified into one of three buckets: direct impact, contributing work, or no outcome signal yet, so you always know what the data can and can't tell you.

A model for connecting spend to results

PRs merged and tickets closed show that work happened, but they don't tell you whether it mattered. This session walks through a simple way to follow AI coding spend from the work itself to the impact it had—whether on adoption, revenue, cost, risk, reliability, or progress toward a bigger goal—and how to handle the cases where that link isn't clean.

What you'll leave with‍

• A framework for distinguishing efficient AI coding work from work that delivers proven business value
• A model for attributing AI coding spend to outcomes that works even with incomplete data
• Practical questions to ask your team when an AI coding session looks efficient but the output is thin
• Language for the "so what did this get us" conversation with your leadership