For Engineering Leaders

Last updated: September 2026

For engineering leaders

You run multiple teams. You have a board meeting next week and a 1:1 with your VP the week after. This guide is how to extract the right answers in 30 minutes flat.

Your weekly 10-minute scan

Every Monday morning, open /teams with the date range set to the last 14 days. Look at three things:

1. Tier mix bars. Are any teams disproportionately Emerging or Explorer? That’s a leading indicator of an adoption problem you can intervene on this week.
2. Cycle Time outliers. Any team over 4 days? Click in — almost always it’s a review bottleneck (high review cycles) or a WIP problem (too many open PRs), not coding speed.
3. Output Score swings. A team’s Output Score down 30% week-over-week deserves a check-in. Usually it’s a quarter-end shift in priorities, but sometimes it’s a process breakage.

If nothing flagged, you’re done. If something flagged, drill into the team’s expanded row → System Metrics tab.

Your quarterly review

A 45-minute structured review using four pages. Do this once per quarter with your engineering leadership group.

Step 1 — Org-level AI rollout health (5 min)

Open /executive. Note: AI Adoption % current vs. last quarter, Power User % current vs. last quarter, AI-Assisted % current vs. last quarter.

Question to answer: Did we move the rollout forward this quarter? If yes, by how much and on which teams? If no, what’s blocking us?

Step 2 — Delivery health (10 min)

Stay on /executive. Look at Cycle Time, Throughput, Deploy Frequency, and CFR trends.

Question to answer: Did delivery health improve, hold, or regress alongside AI adoption? You’re testing the thesis “AI use should compound into better flow.”

If Cycle Time worsened despite adoption climbing — flag for investigation. It usually means the team has adopted AI without the flow practices (small PRs, tight reviews) to take advantage of it.

Step 3 — Team-by-team review (20 min)

Open /teams. Walk through each team’s expanded row.

For each team, write down: their adoption trajectory (improving / steady / declining), their delivery trajectory (improving / steady / declining), one specific question they should answer at their next team retro.

The dashboard’s job is to surface the questions. The answers come from the team.

Step 4 — Investment review (10 min)

Open /ai-impact. Look at: Productivity Uplift % vs. baseline, estimated $ saved per week, AI-Assisted % of lines changed.

Question to answer: Is the AI tooling spend still returning more value than it costs? For most orgs running Claude Code + Cursor, breakeven happens in single-digit weeks. If yours hasn’t, look at adoption depth — the math fails when adoption is shallow even if it’s broad.

How to read scores fairly

The most common mistake leaders make with this dashboard is reading individual developer scores as a performance review proxy. Read How to think about developer scores before you go anywhere near a 1:1 talking about a score.

What scores are good for: Cohort comparison (“Our backend team has 80% Power Users, our mobile team has 20%. Why?”), Onboarding signal (“New hires are landing in Emerging after 8 weeks instead of Explorer. Something’s wrong with our onboarding.”), Tooling signal (“Three teams using Cursor + Claude Code together hit Power User faster than teams on Claude Code alone.”).

What scores are not good for: “Why is Alex at Explorer when Sam is at Power User?” Without context (project, role, week), that question is unanswerable from the dashboard.

Settings to verify quarterly

These three settings drift over time and need a quarterly check:

→ Playbook — Set tier weights for your org’s maturity

Where to drill further

• Investigating a slow team → Playbook — Investigate a slow cycle time
• Planning a new tool rollout → Playbook — Roll out AI tooling with the Adoption Score
• Quality regression → Playbook — Interpret a high CFR week