Your board wants to know: what's the ROI on the $300K/year you're spending on AI coding tools?
If you answer with adoption rates and survey satisfaction scores, you'll lose credibility. If you answer with hard productivity metrics tied to business outcomes, you'll secure budget for next year.
Here's the framework.
Typical AI tool spending for a 30-person engineering team:
Measure: average time from ticket assignment to PR merge, by complexity tier. Before AI tools vs. after. This captures the full workflow impact, not just code writing speed.
Measure: % of deployments that require a follow-up fix within 48 hours. AI tools should improve quality (through better context and planning), not just speed. If regressions increase with AI adoption, you have a quality problem.
Measure: time from ticket assignment to first meaningful commit. This isolates the Understanding Tax — the biggest lever for complex work.
Measure: time for new hires to submit their first independent PR. Code intelligence tools should dramatically reduce this.
Measure: productivity impact when key engineers go on vacation or leave. If a team member's absence causes a measurable slowdown, knowledge is too concentrated.
If your 30-person team:
Against $50K in tool costs, that's a 32x ROI.
Even at a conservative 10% cycle time reduction, the ROI is 10x+. The key is measuring cycle time, not adoption rate.
The ROI framework centers on the Understanding Tax. Cycle time reduction, the metric that actually drives ROI, is primarily a function of how fast developers understand what to build.
For the dollar cost of not investing, read The Real Cost of Not Understanding Your Codebase.
Glue is the pre-code intelligence investment that delivers measurable cycle time reduction. Track time-to-first-commit before and after — that's your ROI number.