---
title: "For Team Leads"
description: "AI adoption insights for team leaders"
---

# 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:**

1. Check your Organization Dashboard for inactive seats
2. Look for patterns in who's not using AI (new hires? certain roles?)
3. 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:**

1. Enable [Codebase Indexing](/docs/customize/context/codebase-indexing) to improve context quality
2. Review whether suggestions are relevant to your codebase
3. 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:**

1. Map AI to existing daily tasks (stand-ups, PRs, documentation)
2. Ensure the CLI is installed for terminal workflows
3. 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

1. **Set a baseline** — Note your score at the start of an initiative
2. **Check weekly** — Watch for trend changes, not absolute numbers
3. **Adjust tactics** — If a dimension isn't moving, try a different approach
4. **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 |

## Next Steps

- [Understand what each dimension measures](/docs/collaborate/adoption-dashboard/understanding-your-score)
- [Learn strategies to improve your score](/docs/collaborate/adoption-dashboard/improving-your-score)
- [Return to the dashboard overview](/docs/collaborate/adoption-dashboard/overview)
