# For Team Leads > AI adoption insights for team leaders - 网址：https://funcoding.ai/agents/kilo-code/collaborate/adoption-dashboard/for-team-leads/ - 来源：Kilo Code 官方文档原文（英文），MIT 许可，同步于 2026-10-11 - 官方原文：https://kilo.ai/docs/collaborate/adoption-dashboard/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](https://funcoding.ai/agents/kilo-code/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](https://funcoding.ai/agents/kilo-code/collaborate/adoption-dashboard/understanding-your-score/) - [Learn strategies to improve your score](https://funcoding.ai/agents/kilo-code/collaborate/adoption-dashboard/improving-your-score/) - [Return to the dashboard overview](https://funcoding.ai/agents/kilo-code/collaborate/adoption-dashboard/overview/)