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.
WhoWorked team8 min read
Blog
Ideas for clearer work records
Tracking client delivery, organizing work, and recording what humans and AI each contributed.
A practical guide to attributing human and AI work
Build a clear record of human responsibility, AI involvement, time spent, and outcomes without inventing machine hours.
WhoWorked team4 min read
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.
WhoWorked team5 min read
Latest posts
Agency operationsAI operations
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.
WhoWorked team5 min read
AI operationsAgency operations
An AI disclosure policy you can actually enforce
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.
WhoWorked team6 min read
Agency operations
What to say when a client asks for an AI discount
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.
WhoWorked team6 min read
AI attributionAI operations
Agent runs are not timesheets
An agent works while nobody is watching. That work still needs a record with an owner, a scope, a cost, and a reviewer, and an hours grid cannot hold one.
WhoWorked team7 min read
AI operationsAgency operations
Start an AI work register before you automate it
You do not need new tooling to start recording how AI contributed. You need eleven columns, one project, and a month of honest entries.
WhoWorked team6 min read
AI attributionAgency operations
What a human review actually checks
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.
WhoWorked team6 min read
AI operations
Measure AI adoption without turning work into surveillance
Prompt counts and token totals measure activity, not value. Evaluate AI adoption through delivery, review, cost, and outcomes instead.
WhoWorked team9 min read
AI attribution
The AI attribution maturity model
Teams can progress from ad hoc AI disclosure to governed work records without collecting every prompt or deploying every integration at once.
WhoWorked team8 min read
Agency operations
How should you invoice AI-assisted work?
AI changes the relationship between hours, cost, and delivered value. Compare how six common billing models handle that change.
WhoWorked team10 min read
AI attribution
A practical model for AI attribution
A statement that says “AI was used” reveals almost nothing. Practical attribution records contribution, ownership, initiation, review, and evidence.
WhoWorked team9 min read
AI attribution
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.
WhoWorked team8 min read
AI attributionWorkstreams
A practical guide to attributing human and AI work
Build a clear record of human responsibility, AI involvement, time spent, and outcomes without inventing machine hours.
WhoWorked team4 min read
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