AI Impact | iftrue
AI Impact
Prove the ROI of your AI coding assistants. Compare team metrics before and after AI adoption to see exactly how tools like Copilot, Cursor, and Claude are changing your engineering velocity, with real data, not guesswork.
AI Impact · Q1 2026 vs pre-adoption
28 engineers
Cycle time impact↓ 52%
2.1d from 4.4d before AI adoption
By AI tool · merged PR share last 30 days
- Cursor 52%
- GitHub Copilot 38%
- Claude 7%
- OpenAI 3%
Velocity
+34% vs pre-AI
Bug ratio
+9% watch
Deploy freq
+22% vs pre-AI
Before/after comparison with any metric
Per-tool breakdown across Copilot, Cursor, Claude, OpenAI
Track velocity, quality, and DORA metrics
Justify AI tool investments
Leadership asks "Is Copilot worth it?" and you don't have an answer. iftrue gives you one. Set your AI adoption date and automatically compare metrics before and after, across your entire organization.
- Set your AI adoption date and comparison period (30d, 60d, 90d, 180d)
- Automatic before/after calculations across all metrics
- Organization-wide summary with clear % improvements
Organization Summary
90 days after AI adoption
| Metric | Before | After | Improvement |
|---|---|---|---|
| Cycle Time | 4.8 days | 2.3 days | 52% |
| PRs per Day | 7.4 | 12.4 | 68% |
| Review Time | 6.8 hrs | 4.2 hrs | 38% |
Comparing Dec 1 - Feb 28 (before) vs Mar 1 - May 30 (after)
Select Metrics to Compare
- Velocity
- Cycle Time
- PRs/Day
- Tasks/Day
- Quality
- Review Time
- Bug Ratio
- Rework Time
- DORA
- Deploy Frequency
- Lead Time
- Sprint
- Story Points/Sprint
- Issue Cycle Time
Pick your metrics
AI impact isn't one-size-fits-all. Different teams care about different outcomes. Choose the metrics that matter to your organization. From velocity and quality to DORA and sprint performance.
Velocity metrics
Cycle time, PRs per day, tasks completed
Quality metrics
Review time, bug ratio, rework time
DORA & Sprint metrics
Deploy frequency, lead time, story points
Compare teams side-by-side
AI tools don't impact every team equally. See which teams benefit most from AI coding assistants, identify best performers, and find teams that might need additional training or adoption support.
Team Comparison: Cycle Time
| Team | After AI Adoption | Improvement |
|---|---|---|
| Platform Team | 1.8 days | -62% |
| Backend Team | 2.4 days | -48% |
| Frontend Team | 2.9 days | -41% |
| Mobile Team | 4.1 days | Needs support |
Organization average improvement: 41%
Spot trade-offs and synergies
AI tools don’t just speed things up. They can change how your team works. iftrue detects when metrics move together (synergies) or in opposite directions (trade-offs), so you can make informed decisions.
Trade-off Detected
| Metric | Change |
|---|---|
| PRs per Day | +68% |
| Review Time | +23% |
Synergy Detected
| Metric | Change |
|---|---|
| Cycle Time | -52% |
| Bug Ratio | -18% |
Impact Heatmap
| Team | Cycle Time | PRs/Day | Review Time | Bug Ratio |
|---|---|---|---|---|
| Platform | -62% | +85% | -35% | -22% |
| Backend | -48% | +72% | +12% | -15% |
| Frontend | -41% | +58% | 0% | -8% |
| Mobile | -12% | +28% | +18% | +2% |
Track improvement over time
Initial AI impact is just the start. Track trends daily, weekly, or monthly to see if improvements are sustained. Identify when gains plateau or if teams are continuing to accelerate.
Sustained gains
See if initial improvements continue or if teams plateau after the novelty wears off.
Multiple time granularities
Daily views for sprint-level analysis, weekly and monthly for executive reporting.
Milestone tracking
Mark key events like new tool rollouts or training sessions to correlate with metric changes.
Cycle Time Trend
| Time Period | Cycle Time |
|---|---|
| Before AI | 4.8 days |
| Current | 2.3 days |
| Improvement | 52% faster |
Built for engineering leaders
VPs of Engineering
Present hard data to leadership when justifying AI tool budgets. Show exactly how Copilot or Cursor is impacting velocity across teams.
Engineering Managers
See which of your teams are benefiting most from AI coding assistants. Identify where more training or adoption support is needed.
Tech Leads
Compare your team's AI-assisted performance against other teams. Understand if AI is helping with velocity, quality, or both.