AI initiatives often stall because teams cannot prove adoption or value. They have usage charts, but not outcomes. Or they have outcome stories, but no telemetry. Operational analytics bridges that gap.
Start with the smallest set of signals
Good analytics focuses on signals that drive decisions:
- Activation. How many users try the AI more than once.
- Task completion. How often the AI completes the task without escalation.
- Time saved. Measured where you have baseline workflow data.
- Risk signals. Escalations, refusals, and incidents (see incident response).
Instrument the system, not just the UI
Usage analytics should include the AI system layers:
- Model and prompt versions used for each session.
- Retrieval coverage and citation usage (see citations and grounding).
- Tool-call success and error rates.
- Token usage and cost per task (see FinOps for LLMs).
Balance measurement with privacy
Analytics pipelines often become a hidden privacy risk. Apply minimization:
- Store structured metrics instead of raw prompts.
- Redact sensitive content at capture time (see data minimisation).
- Use sampling for deeper traces, not always-on storage.
Connect analytics to decisions
Analytics should drive release and investment choices: which workflows deserve optimization, which teams need enablement, and where risk is growing. Pair usage analytics with outcome metrics (see value metrics) and operational observability (see AI observability).
The goal is not more dashboards. The goal is actionable insight that keeps AI programs aligned to real outcomes.