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.
The Investment
Typical AI tool spending for a 30-person engineering team:
- GitHub Copilot: $19/seat/month × 30 = $6,840/year
- Cursor Pro: $20/seat/month × 10 power users = $2,400/year
- Code intelligence (Glue): varies
- Claude Code/API usage: $2,000-$5,000/year
- Total: $15K-$50K/year depending on the stack
The Wrong Metrics
- "87% of developers use Copilot daily" → usage doesn't equal value
- "Developers report feeling 30% more productive" → feelings don't equal reality
- "We accept 35% of Copilot suggestions" → acceptance rate measures convenience, not impact
The Right Metrics
1. Cycle Time Reduction
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.
2. Regression Rate
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.
3. Context Acquisition Time
Measure: time from ticket assignment to first meaningful commit.
This isolates the Understanding Tax — the biggest lever for complex work.
4. Onboarding Velocity
Measure: time for new hires to submit their first independent PR.
Code intelligence tools should dramatically reduce this.
5. Knowledge Continuity
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.
The ROI Calculation
If your 30-person team:
- Reduces average cycle time by 25% (from 4 days to 3 days per complex ticket)
- That's ~1 day saved per ticket × 3 complex tickets per engineer per sprint
- 30 engineers × 3 tickets × 1 day × 26 sprints = 2,340 engineer-days saved per year
- At $700/day loaded cost = $1.64M in productivity gains
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.
Originally published on glue.tools. Glue is the pre-code intelligence platform — paste a ticket, get a battle plan.
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