For Executives

Last updated: September 2026

For executives

You don’t have time to learn the product. You have time for four numbers and a story. This page is exactly that.

The four numbers

Open /ai-adoption/executive. Five hero cards stretch across the top. The four that matter most:

1. AI Adoption %

Percent of your active developers at Explorer tier or above (score ≥ 25). This is your “how broadly is AI used here?” answer.

Read it as: a leading indicator. Adoption usually moves before output does.

Healthy trajectory: up and to the right, with the curve flattening as it approaches 100%. A flat or declining number deserves an investigation — usually it is onboarding, tooling friction, or a vocal skeptic on a team.

2. AI-Assisted %

Percent of changes (PRs + direct commits, weighted by lines changed) where the developer used AI in or around the change. Different from Adoption %: a developer can have AI installed (counts toward Adoption) without using it on a given change (doesn’t count toward AI-Assisted).

Read it as: a behavioral indicator. The closer this gets to AI Adoption %, the more thoroughly AI is integrated into actual work.

3. Cycle Time

Average hours from a PR’s first commit to merge. The single best operational health metric in the dashboard.

Read it as: a trailing indicator of flow. If cycle time creeps up, look at WIP and review-cycle counts.

4. Throughput

Number of merged PRs per week, normalized per active developer.

Read it as: volume of shipped work. Read alongside Output Score (effort-weighted) — Throughput is “how many things”; Output Score is “how much stuff.”

The fifth card, Power User Growth, is your quarter-over-quarter trend on how many developers moved into the top tier.

How to read the trend lines

The executive view has six trend lines below the hero cards: Cycle Time, Throughput, Deployment Frequency, CFR, AI Adoption, and (when configured) Customer Bugs.

The single most useful reading pattern: look at adoption trend and cycle time trend together.

• Adoption ↑ and Cycle Time ↓ → AI is delivering. This is the story you want to tell.
• Adoption ↑ and Cycle Time flat → AI use is increasing but flow hasn’t caught up. Usually a review-bottleneck issue.
• Adoption flat and Cycle Time ↑ → Operational drag. Investigate WIP, headcount, scope.
• Adoption ↓ and Cycle Time ↑ → Compound problem. Likely a morale or attrition signal — talk to your engineering leaders.

What to put in your monthly report

Three lines, in this order:

1. The headline. “AI adoption climbed from 62% to 71% this month, while cycle time dropped from 4.1 to 3.4 days.”
2. The story. Why it moved. Did you ship a training? Did a new team onboard? Did you launch Codex alongside Claude?
3. The next bet. What you are going to push on next month. “Next month we are focused on the Platform team — currently 40% Emerging, target 70% Explorer+.”

The dashboard’s job is to give you the first two. The third one is your call.

What not to do with the dashboard

• Don’t show individual developer scores in your monthly report. It will get someone fired by a different chain of command than the dashboard was designed for. Aggregate by team or by tier mix instead.
• Don’t read a single week. Engineering work has 2–4 week natural cycles. Read trends, not weeks.
• Don’t quote ROI to the dollar. The Productivity Uplift number is a directional estimate. Use it for order-of-magnitude statements (“low single-digit millions in annualized productivity gain”), not for finance.

Where to drill further

If you want one more level of detail:

• For “is AI actually paying off?” → Productivity Uplift
• For “where are we slow?” → /ai-adoption/ai-impact (Cycle Time phase breakdown)
• For “which team needs attention?” → /ai-adoption/teams