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.
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
Prove AI ROI with data
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
Cycle Time
PRs per Day
Review Time
Comparing Dec 1 - Feb 28 (before) vs Mar 1 - May 30 (after)
Select Metrics to Compare
Cycle Time
PRs/Day
Tasks/Day
Review Time
Bug Ratio
Rework Time
Deploy Frequency
Lead Time
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.
Identify best performers
Spot teams that have adapted quickly to AI tools and learn from their adoption patterns.
Find adoption gaps
Some teams may need more training or support to fully benefit from AI coding tools.
Team Comparison: Cycle Time
After AI adoptionOrganization 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
More PRs being created may be causing review bottlenecks. Consider adding reviewers or automating initial review checks.
Synergy Detected
AI-assisted code is shipping faster AND with fewer bugs. Your team is achieving velocity without sacrificing quality.
Impact Heatmap
All teams, all metricsTrack 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
Before AI
4.8 days
Current
2.3 days
Who it's for
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.
Ready to transform your engineering organization?
Start your journey to data-driven engineering management. Book a demo to see how iftrue can help your team.