# 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.