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

Illustrative example for a 10-engineer team (every number is an assumption to replace with your own):

• Claude Code usage: official guidance is roughly $13 per developer per active day and $150–250 per developer per month on average, with 90% of users under $30 per active day. Take 10 × $200 = $2,000/month as a planning figure, then replace it with your own pilot baseline.
• Seat cost on top, if applicable (Claude Team standard seat: $20/seat/month billed annually or $25 monthly; premium seat $100 annual or $125 monthly; check claude.com/pricing, as prices change).
• If each engineer saves 30 minutes/day (an assumption, not a benchmark): 10 × 0.5h × $120/h × 22 days = $13,200/month
• ROI: (13,200 - 2,200) / 2,200 × 100 = 500%

Run the same arithmetic at 10 and 15 minutes/day to see how sensitive the result is. Time-saved inputs are the weakest part of any ROI model, so prefer the measured signals below over self-reported savings.

Key Metrics to Track

1. Suggestion Acceptance Rate

Both vendors report an acceptance metric, but they are not comparable with each other.

• Claude Code: the analytics dashboard at claude.ai/analytics/claude-code (Team/Enterprise, Admin or Owner role) shows suggestion accept rate (how often users accept Edit, Write, and NotebookEdit changes), lines of code accepted, daily active users, and sessions. API customers get a similar dashboard at platform.claude.com/claude-code, which adds spend per member.
• Copilot: use GitHub's organization-level Copilot usage metrics (dashboard or metrics API) rather than per-editor telemetry.

Treat the rate as a trend and a friction signal, not a target. A sudden drop usually means poor instructions (CLAUDE.md or copilot-instructions.md) or a model or config change; there is no universally "good" percentage.

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 against your own pre-rollout baseline. Cycle time is also affected by review load, so watch it alongside review time. For Claude Code on Team/Enterprise, the dashboard's contribution metrics (GitHub integration, public beta) compare PRs with and without Claude Code assistance and show PRs per user over time. Merged PRs containing Claude Code-assisted lines are labeled claude-code-assisted in GitHub, so you can filter for them:

gh pr list --state merged --label claude-code-assisted --limit 100 --json createdAt,mergedAt

These metrics are deliberately conservative (an underestimate) and are not available for organizations with Zero Data Retention or for usage through the Console API or third-party providers.

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

Compare against your human-written baseline measured the same way; there is no standard industry target. Tags like [ai-assisted] depend on developer discipline. Claude Code adds a Co-Authored-By trailer to its commits by default (configurable through the attribution setting), which is a more reliable marker.

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 against your own baseline. Coverage is a weak proxy on its own; pair it with defect escape rate or mutation testing.

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

Use with caution: it rewards verbosity, and AI makes verbose code cheap. Prefer PRs merged per engineer and cycle time. If you track it at all, treat it as a directional signal only.

# 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 the average over time and read the free-text comments. As a rough heuristic, a score trending below 3 suggests adoption friction.

What NOT to Measure

Cost Tracking Template

## Monthly AI Tools Cost Analysis
 
### Licensing
- GitHub Copilot Business: $N × (current per-seat price) = $XX
- Claude Team/Enterprise seats: $N × (current seat price; see claude.com/pricing) = $XX
- Cursor Teams seats: $N × (current per-seat price) = $XX
- Total: $XXX/month
 
### Claude Code usage cost
- Seat-based (Team/Enterprise): usage inside the seat allowance is not metered in dollars; track usage-credit spend in the admin spend report
- API / Console / Bedrock / Vertex / Foundry: per-token billing; pull spend from the Console dashboard, your cloud bill, or OpenTelemetry (claude_code.cost.usage)
- Planning baseline (official): ~$13 per developer per active day, $150–250 per developer per month
- Monthly actual: $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)
• Your own before/after baselines (avoid quoting vendor or third-party benchmarks as your results)

Collecting Usage and Cost Data

To enable OpenTelemetry, set CLAUDE_CODE_ENABLE_TELEMETRY=1, OTEL_METRICS_EXPORTER=otlp, OTEL_EXPORTER_OTLP_PROTOCOL and OTEL_EXPORTER_OTLP_ENDPOINT in the env block of managed settings so they apply to everyone. Useful metrics include claude_code.session.count, claude_code.cost.usage, claude_code.token.usage, claude_code.lines_of_code.count, claude_code.pull_request.count, and claude_code.commit.count. Prompt text is redacted unless you set OTEL_LOG_USER_PROMPTS=1; leave it off unless your privacy review approves it.

A Note on Published Benchmarks

Published productivity figures vary widely and are not directly comparable to your codebase:

• GitHub's often-cited "55% faster" comes from a 2022 controlled experiment on one small, well-scoped task (writing an HTTP server in JavaScript). It does not describe typical feature work.
• Consultancy estimates (for example McKinsey's 2023 analysis) report time reductions on specific task types, not end-to-end delivery.
• A 2025 randomized controlled trial by METR found experienced open-source developers working in their own mature repositories took about 19% longer with AI tools, while believing they were faster.

The takeaway: measure your own baseline before rollout, compare after, and be skeptical of self-reported savings.