title: "Measuring AI Coding ROI" description: "Velocity metrics, code quality signals, and cost tracking frameworks for AI coding tool investment" section: "Adoption" readTime: "10 min"

Measuring AI Coding ROI

"We feel more productive" is not a metric your CFO will accept at renewal time. This guide gives you concrete, measurable signals to track AI coding tool ROI — and honesty about vanity metrics to avoid.

The ROI Formula

ROI = (Value Delivered - Cost) / Cost × 100%

Value = Time Saved × Engineer Hourly Rate
Cost = Licensing + Setup Time + Training Time + Overhead

For a 10-engineer team:

• Copilot Business: ~$200/month
• If each engineer saves 30 minutes/day: 10 × 0.5h × $120/h × 22 days = $13,200/month saved
• ROI: (13,200 - 200) / 200 × 100 = 6,500%

Even conservative estimates (15 min/day saved) produce ROI > 1,000%.

Key Metrics to Track

1. Completion Acceptance Rate (Copilot)

GitHub Copilot provides this in VS Code telemetry. Target: >30%.

• <20%: Suggestions aren't relevant — check your copilot-instructions.md quality
• 20–40%: Normal range
• >40%: Excellent; Copilot understands your codebase well

Access via: VS Code → GitHub Copilot → Usage Statistics

2. PR Cycle Time

Measure time from PR open to merge, averaged across the team.

# Using GitHub CLI + jq
gh pr list --state merged --limit 100 --json createdAt,mergedAt | \
  jq '[.[] | {
    hours: (((.mergedAt | fromdateiso8601) - (.createdAt | fromdateiso8601)) / 3600)
  }] | add.hours / length'

Compare monthly. AI assistance should reduce this 15–30% for typical feature work.

3. AI-Generated Code Churn Rate

What percentage of AI-written lines are modified within 7 days? High churn means low-quality output.

# Mark AI commits with a convention
git commit -m "feat: add auth middleware [ai-assisted]"
 
# Then measure churn for those commits
git log --grep="\[ai-assisted\]" --format="%H" | \
  xargs -I {} git diff {}~1 {} --stat | \
  # Compare with follow-up commits to those files within 7 days

Target: <25% churn within 7 days (similar to human-written code baseline).

4. Test Coverage Trend

AI should help increase test coverage since writing tests is one of the highest-value AI coding tasks.

# Monthly baseline
npx jest --coverage --coverageReporters="json-summary" 2>/dev/null | \
  jq '.total | {lines: .lines.pct, branches: .branches.pct}'

Track monthly. Expect +5–10% coverage per quarter with active AI test generation.

5. Lines of Code per Engineer-Hour (Productivity Proxy)

Not a pure quality metric, but useful as a directional signal.

# Git log for last 30 days — lines added by team
git log --since="30 days ago" --numstat --format="" | \
  awk '{add += $1} END {print add}'

Divide by engineer-hours worked. Compare pre- and post-rollout baselines.

6. Engineer Satisfaction Score

A quarterly survey question:

"On a scale of 1–5, how much does [AI tool] help you do your job better?"

Track average score. Below 3.0 means adoption is struggling; above 4.0 means high value perceived.

What NOT to Measure

Cost Tracking Template

## Monthly AI Tools Cost Analysis
 
### Licensing
- GitHub Copilot Business: $N × $19/month = $XX
- Claude Pro/Max seats: $N × $XX/month = $XX
- Cursor Pro seats: $N × $20/month = $XX
- Total: $XXX/month
 
### API Usage (if using Claude Code heavily)
- Average daily tokens: XXX,XXX
- Monthly estimate: $XXX
 
### Overhead
- Training time (one-time): XX engineer-hours × $120 = $XXX
- Ongoing admin: XX hours/month × $120 = $XXX
 
### Total Monthly Cost: $XXX
 
---
 
### Value Delivered
 
#### Time Savings (Survey + Telemetry Triangulated)
- Avg minutes saved per engineer per day: XX min
- Team size: N engineers
- Working days: 22/month
- Hourly rate blended: $120/hr
- Monthly time value: N × (XX/60) × $120 × 22 = $XXXX
 
#### PR Cycle Time Improvement
- Pre-rollout average: XX hours
- Post-rollout average: XX hours  
- Time saved per PR: XX hours × $120 = $XXX
- PRs per month: XX
- Monthly value: $XXXX
 
### ROI: (value - cost) / cost × 100% = XXX%

Stakeholder Reporting

For engineering leadership (monthly):

• PR cycle time trend
• Completion acceptance rate
• Tool satisfaction score
• Notable wins (one specific story)

For executive leadership (quarterly):

• ROI summary (use the formula above)
• Security incidents (should be zero)
• Headcount equivalent (time saved ÷ 160 hours ≈ N FTE-equivalent productivity gain)
• Comparison to industry benchmarks

Industry Benchmarks (2025)