Adoption Telemetry Dashboard Template

What this is

A panel specification for tracking how AI tools are used at work. It has eight panels. Each names the question it answers, the data source and the decision it supports. It is a page, not a dashboard file. You copy the panels into whatever tool you already run.

Version 0.1 covers coding assistants and chat assistants. Version 0.2 will cover SaaS tools with AI built in and internal agents. I will write it when clients who run them appear.

The panels fall into three kinds. The coverage, seat and retention panels count what people did. The two survey panels count what people said. The outcome, delivery health and cost panels read what the work produced. The outcome, delivery health, cost and trust panels carry the weight, and the trust survey is read as opinion.

That order is a synthesis of the sources, not a finding any of them states. The adoption telemetry article gives the evidence behind it.

The eight panels

The delivery health and cost panels apply to engineering and workflow use cases. Write the outcome panel for each use case.

Fix these definitions before you build

• What counts as active. GitHub needs two active days in the trailing 28 to place a user in a usage phase. Microsoft counts a returning user as one with an action in both the current and the previous period. Choose one rule, write it on the panel and keep it. Mine is two different active days within four weeks, and I count people, not accounts.
• When you check retention. The retention panel uses 4, 8 and 12 weeks. Week 4 is the first check. I read it as people active in week 4 over people active in week 1, per team, from gateway logs.
• The smallest group you show. Mine is five people. Microsoft’s Viva Insights needs its minimum group size set to at least five. That is a product setting. The sources read here give no legal figure for a minimum group size. Write down your own.

What stays off the dashboard

• Seat count as adoption. A seat shows money spent, and GitHub keeps seat data apart from its usage reports.
• Daily or weekly active users with no written definition. GitHub, Microsoft and DORA each define active differently.
• Prompt counts. DORA’s 2024 report, published by Google Cloud, said measuring usage frequency is “likely not as meaningful as measuring reliance”.
• Lines of code accepted as a success measure. GitHub calls its own lines figure directional.
• Acceptance rate as a success measure. In a 2022 preprint on Copilot users it tracked how productive users said they felt.
• Vendor hours saved or ROI as value. Microsoft builds assisted hours from action counts, fixed minutes per action, and meeting recaps counted by meeting length. A default hourly rate then values them.
• Rankings of named people. DX, a vendor of measurement software, warns against using these metrics to judge individuals.
• Prompt content. The GDPR asks for no more data than needed, and Claude Code’s documentation says prompt text is off by default. I collect none.

Who sees what

Show team-level figures by default, for teams of five or more. Where you control the collector, filter or redact the person field before data reaches the dashboard. Claude Code includes the signed-in user’s email by default, and its documentation says to filter it at your own backend if that is a concern.

The paid versus active panel shows idle seats by team. Reclaiming a seat needs a name, and a name is person-level data. Show named views only for a narrow stated purpose, recorded, approved by two people and kept 30 days. Write down who sees the list. Then run the screening in the adoption telemetry article before the list exists.

What it is not

It is not a live dashboard, and it is not an export for one tool. It does not show that an AI tool works. Only the outcome panel can do that, against a baseline or a comparison group, and that is a synthesis, not a finding any source states. It is a summary of public documents, not legal advice.