What a team AI value report should actually show
Most AI coding reports answer questions that do not matter much: how many tokens were spent, how many sessions ran, how many lines changed. Those are easy to collect and easy to misread as value. A team AI value report should answer a harder question: of all that activity, how much produced validated, trustworthy outcomes, and did it stay in scope and safe along the way.
Activity is not value
Spend and activity metrics are proxies, and weak ones. High token usage can mean productive work or expensive thrashing. Many sessions can mean momentum or repeated false starts. Lines changed can mean delivery or bloat. Reporting these as outcomes rewards motion, not results.
tokens, dollars
sessions, lines
proof, scope, safety
What to show instead
A value report connects effort to evidence. The questions worth answering:
- How much AI work ended with validated proof, not just output
- How often work stayed inside its declared scope
- How much review load was avoided by scope repair before approval
- Whether context carried forward or was rebuilt each time
- Raw token totals presented as productivity
Useful without surveillance
A value report does not require tracking individuals. Aggregating by repo, workflow, tool, and capability gives the signal a team lead needs without ranking people or exposing raw prompts and code. The unit of insight is the work, not the worker.
Report outcomes, not effort, and aggregate by work, not by person. A value report should make trustworthy progress visible without turning into surveillance.
How Avorelo helps
Avorelo's team reports are built on receipts, so they show validated outcomes rather than raw activity: how much work ended in proof, how scope held, and where review load was reduced. Metrics aggregate at the repo, workflow, tool, model class, and capability level, giving leads real signal without exposing individual contributors or raw code.