Adoption & AI-usage analytics
Where the Executive Summary gives the headline, Adoption and AI-usage analytics give the detail. This surface shows administrators and department leads how AI is being adopted, which agents and workflows generate the most volume, and where usage concentrates across the enterprise.
Adoption analytics
Section titled “Adoption analytics”Adoption analytics answer the question is automated AI actually being used across our teams?
- User engagement metrics — monthly and daily active users, plus onboarding completion rates by department.
- Leading indicators of value — which business units are integrating AI into daily routines, and which are falling behind.
- Targeted interventions — spot thin adoption early, so leaders can coach, share a successful template, or remove workflow friction before the investment stalls.
Usage analytics
Section titled “Usage analytics”Usage analytics answer the operational question what are our teams actually building and running?
- Execution volume tracking — total execution counts, successful workflow completions, and active agent run hours.
- Top-performing assets — the most heavily used agents, functions, and workflows across the organization.
- Cost and impact linkage — connects high-volume execution trends to model compute cost and resource allocation, feeding directly into the Cost Optimization Center.
What it enables
Section titled “What it enables”Analytics are a management surface, not a reporting one. The typical actions they support are retiring unused assets, promoting a high-value workflow to neighboring teams, redirecting run balance toward what works, and targeting training where adoption is weak.
Analytics comparison
Section titled “Analytics comparison”| Dimension | Key metrics tracked | Operational value |
|---|---|---|
| Adoption analytics | Active users, login frequency, onboarding completion | Identifies training needs and measures saturation |
| Usage analytics | Run counts, completion rates, top active agents | Connects platform activity to business productivity |
| Cost and compute analytics | Model call volumes, compute costs, balance burn | Informs budget allocation and cost optimization |
A worked example: optimizing team assets
Section titled “A worked example: optimizing team assets”Consider a Support team lead reviewing departmental usage.
- Usage inspection — the lead opens the team analytics view and finds a single ticket-summarization workflow accounts for 80% of total usage, while three custom agents have had zero runs in thirty days.
- Data-driven tune-up — the lead deprecates the unused agents to clean up the team’s catalog, and shares the ticket-summarization workflow with neighboring teams.
- Result — departmental return increases, asset clutter disappears, and compute balance shifts toward high-value automations.