
AI did the work. Who gets the credit?
Human timesheets and AI usage logs each record only half the work. A useful attribution model keeps accountability human while making material AI contributions visible.
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Tracking client delivery, organizing work, and recording what humans and AI each contributed.

Human timesheets and AI usage logs each record only half the work. A useful attribution model keeps accountability human while making material AI contributions visible.
Build a clear record of human responsibility, AI involvement, time spent, and outcomes without inventing machine hours.
Prompt counts and token totals measure activity, not value. Evaluate AI adoption through delivery, review, cost, and outcomes instead.

Human timesheets and AI usage logs each record only half the work. A useful attribution model keeps accountability human while making material AI contributions visible.

Build a clear record of human responsibility, AI involvement, time spent, and outcomes without inventing machine hours.

Prompt counts and token totals measure activity, not value. Evaluate AI adoption through delivery, review, cost, and outcomes instead.

Teams can progress from ad hoc AI disclosure to governed work records without collecting every prompt or deploying every integration at once.

AI changes the relationship between hours, cost, and delivered value. Compare how six common billing models handle that change.

A statement that says “AI was used” reveals almost nothing. Practical attribution records contribution, ownership, initiation, review, and evidence.

Use a durable layer between projects and tasks to improve estimates, time entry, reporting, and delivery decisions.

Turn time entries into a useful delivery record with stable workstreams, concise outcome notes, and timely review.
30-day free trial. Bring the time history you already have.