
When AI-assisted work goes wrong on a client project
The mistake is rarely the expensive part. The expensive part is the day you spend unable to say how far it reaches.
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Tracking client delivery, organizing work, and recording what humans and AI each contributed.

The mistake is rarely the expensive part. The expensive part is the day you spend unable to say how far it reaches.

Most AI policies are aspirations nobody can check. A short policy backed by a record that already exists will hold up in a client conversation.

The question is fair and it is coming. Answer it with a record of what the work involved, not with a reflexive discount or a defensive denial.

You do not need new tooling to start recording how AI contributed. You need eleven columns, one project, and a month of honest entries.

Human reviewed is the claim every AI disclosure leans on, and most teams cannot say what the reviewer looked at. A checklist turns a signature into evidence.

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

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.