For Team Leads

This guide covers how engineering managers and team leads can use the AI Adoption Dashboard to drive AI integration, identify gaps, and communicate progress to stakeholders.

Reading Team-Wide Metrics

The Organization View

Disable the "Only my usage" toggle to see aggregated metrics across your entire team. This view shows:

Overall AI Adoption Score — Your single benchmark number
Dimension breakdown — Frequency, Depth, and Coverage contributions
Week-over-week trends — Direction and magnitude of change
Historical timeline — Score progression over days, weeks, or months

Dimension Detail Panels

Click on any dimension card (Frequency, Depth, or Coverage) to open its detail panel. Each panel provides:

A focused timeline for that dimension
The goal statement for that dimension
Three actionable improvement suggestions tailored to what that dimension measures

Use these panels to diagnose specific issues and identify targeted actions.

Comparing Time Periods

Switch between time filters to understand different patterns:

Filter	Best For
Past Week	Recent changes, sprint-level trends
Past Month	Adoption initiative tracking, onboarding results
Past Year	Long-term trends, seasonal patterns
All	Historical baseline, major milestones

Identifying Adoption Gaps

Low Coverage Signals

A low Coverage score often indicates adoption gaps—pockets of your team that aren't using AI.

Questions to investigate:

Are all team members logged in and active?
Are certain roles or squads under-represented?
Is usage concentrated on specific days (spiky pattern)?

Actions:

Check your Organization Dashboard for inactive seats
Look for patterns in who's not using AI (new hires? certain roles?)
Consider targeted onboarding or pairing sessions

Low Depth Signals

Low Depth indicates that developers may be trying AI but not trusting or shipping its output.

Questions to investigate:

Are acceptance rates low? (Developers rejecting suggestions)
Is AI-generated code being merged?
Are developers using AI across multiple stages (plan → build → review)?

Actions:

Enable Codebase Indexing to improve context quality
Review whether suggestions are relevant to your codebase
Introduce chained workflows to increase multi-stage usage

Low Frequency Signals

Low Frequency suggests AI hasn't become a daily habit.

Questions to investigate:

Are developers aware of all available AI surfaces (IDE, CLI, Cloud)?
Is AI usage triggered only by specific, infrequent problems?
Have developers built AI into routine tasks?

Actions:

Map AI to existing daily tasks (stand-ups, PRs, documentation)
Ensure the CLI is installed for terminal workflows
Run a "try autocomplete for a week" challenge

Running Adoption Initiatives

Setting Goals

Use the score tiers as milestones:

Current Tier	Reasonable Next Goal
0–20 (Minimal)	Reach 30–40 within 4–6 weeks
21–50 (Early)	Reach 55–65 within 4–6 weeks
51–75 (Growing)	Reach 75–80 within 6–8 weeks
76–90 (Strong)	Maintain and optimize

Tip: Focus on one dimension at a time rather than trying to improve everything at once.

Initiative Ideas

For Frequency:

"Autocomplete Week" — Everyone commits to using autocomplete daily
CLI onboarding session — 30-minute walkthrough of terminal AI
Daily AI tip in Slack — Share one use case per day

For Depth:

"Chain Challenge" — Complete one feature using plan → build → review
Codebase Indexing rollout - Enable better context for the whole team
Deploy previews — Validate AI output before merging

For Coverage:

New hire onboarding includes Kilo setup
Weekly "AI wins" sharing in stand-ups
Pair low-usage developers with enthusiastic adopters

Tracking Progress

Set a baseline — Note your score at the start of an initiative
Check weekly — Watch for trend changes, not absolute numbers
Adjust tactics — If a dimension isn't moving, try a different approach
Celebrate wins — Acknowledge when the team hits a milestone

Benchmarking Against Goals

Internal Benchmarking

Use the score to compare:

Teams within your organization — Which teams are leading adoption?
Before vs. after — Did a specific initiative move the needle?
This quarter vs. last — Are you trending up or down?

Communicating to Stakeholders

The AI Adoption Score is designed to be quotable:

"Last quarter we were at 38. This quarter we're at 57. Our goal is to reach 70 by Q2."

When presenting scores:

Lead with the trend, not just the number
Explain the tier and what it means
Connect to business outcomes ("Higher adoption → faster development cycles")
Share specific actions you're taking

Sample Stakeholder Update

AI Adoption Update — January 2025

Current Score: 57 (Growing adoption tier)

Last Month: 48

Change: +9 points, driven by improved Depth scores

Key Actions Taken:

Enabled Codebase Indexing for better AI context

Introduced Code Reviews for all PRs

Onboarded 3 inactive team members

Next Steps:

Target 65 by end of February

Focus on Coverage—spread usage across the full week

Privacy and Data Considerations

Anonymous Data

Individual usage data is anonymized in the dashboard. While you can see aggregate metrics, the dashboard does not expose individual developer activity to managers.

Focus on Teams, Not Individuals

The Dashboard is designed for:

Team-level insights
Organizational trends
Comparative benchmarking

It is not designed for:

Individual performance evaluation
Identifying specific low performers
Surveillance of developer activity

Use the score to identify adoption gaps, not to judge individual developers.

Future Enhancements

Code Contribution Tracking

A future enhancement will track AI-contributed code from feature branch to main branch:

What percentage of AI-suggested code actually ships?
How much of the codebase was AI-assisted?

This metric is separate from the Adoption Score but valuable for measuring AI impact on output.

Team Comparison Views

Additional views for comparing multiple teams within an organization are planned, enabling leadership to identify best practices from high-performing teams.

Quick Reference: Dashboard Actions

What You Want to Know	Where to Look
Overall adoption level	Main score display
Which dimension needs work	Trend indicators (look for negative trends)
Specific improvement actions	Click dimension → detail panel
Historical patterns	Timeline chart with time filter
Your personal usage	Toggle "Only my usage"
Week-over-week change	Metric cards at bottom