Estimate realistically how much time AI could free up in a workflow
A grounded estimate of capacity AI could create in a workflow, step by step, net of review time and with clear assumptions.
The prompt
Estimate how much capacity AI could realistically create in a workflow, without the hype.
The workflow: [STEPS]
Volume: [how often it runs, per week or month]
Time per step today: [from tracking or best guess - say which]
People involved and their roles: [DETAILS]
How AI would be used: [e.g. drafting, summarizing, data prep, classification]
Steps:
1. For each step, estimate time today vs. with AI, including the new time for prompting, reviewing, correcting, and handling errors. Most AI steps still need review; include it.
2. Give low, likely, and high estimates.
3. Account for adoption: learning time, how many people will actually use it, and the share of cases AI can't handle.
4. Separate hours freed (capacity) from cost saved (only if hours are actually redeployed or not hired).
Return:
1. A table: Step | Time today | Time with AI (incl. review) | Saving per run | Runs/month | Hours/month (low / likely / high).
2. Total monthly capacity created, with the assumptions listed.
3. The 2-3 assumptions the estimate is most sensitive to, and how to test them in a pilot.
4. A one-paragraph summary I could share with my manager.
Show your math. Label every number not provided by me as [ESTIMATE] and do not present estimates as measured results.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Working with AI prompts
What to fill in
- STEPS
- how often it runs, per week or month
- from tracking or best guess - say which
- DETAILS
- e.g. drafting, summarizing, data prep, classification
- ESTIMATE
How this prompt is built
This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.
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