Insights
Path: /insights
The Insights page helps engineering leads quantify the return on investment from AI coding tools. While the main dashboard shows operational metrics, Insights translates those into business value.
Metrics
Productivity Multiplier
How much more output the team produces with AI assistance compared to manual-only coding. Calculated from AI ratio and throughput data.
A multiplier of 2.5x means the team is producing 2.5 times the output they would without AI assistance. This metric is most meaningful when tracked over time — a rising multiplier indicates the team is getting better at leveraging AI.
Capacity Freed
Hours of developer time freed by AI assistance per week or month. Derived from AI-generated lines and estimated manual coding time.
This answers the question: “If AI wasn’t writing code, how many more developer-hours would we need?” Useful for headcount planning and budget justification.
Cost per Task
Average engineering cost per completed task. Combines session duration with team cost assumptions.
Track this over time — as AI adoption increases, cost per task should trend downward. A rising cost per task may indicate that tasks are getting more complex or that AI isn’t being used effectively.
Throughput Chart
Tasks completed over time, showing trends in team velocity. Compare different time periods to see if the team is accelerating.
Cost Efficiency Chart
Cost per task over time — ideally trending downward as AI adoption increases and the team becomes more proficient with AI-assisted workflows.
Token Usage
AI token consumption patterns across the team. Helps track the operational cost of AI tool usage.
AI Adoption Leaderboard
Per-developer ranking by AI code ratio, showing who’s leveraging AI tools most effectively.
This is not a productivity ranking — it shows adoption patterns. Developers with low AI ratios might benefit from training or pairing with high-adoption teammates. Developers with very high ratios should ensure they’re reviewing AI output carefully.
How to Use Insights
For budget justification
Show leadership the productivity multiplier and capacity freed metrics. These translate AI tool costs into concrete business value: “Our team produces 2.5x more output, freeing 40 hours per week of developer capacity.”
For team coaching
Use the adoption leaderboard to identify developers who might benefit from AI coding training. Pair low-adoption developers with high-adoption ones for knowledge sharing.
For trend analysis
Track cost per task and throughput over time to validate your AI tool investment. If costs are flat or rising while throughput isn’t increasing, investigate whether the team needs better tooling, training, or process changes.