AI Workflow Time Savings Calculator: Include Review and Rework

The calculation

Let N be completed jobs per month, B manual minutes per job, P preparation minutes, R review minutes, q the rework fraction, E additional minutes per reworked job, H monthly maintenance hours and S setup hours. Manual hours = N × B / 60. Assisted hours = N × (P + R + q × E) / 60 + H. Net time saved is manual minus assisted. Setup payback in months is S divided by positive net time saved; otherwise no payback is defined.

Avoid the two easiest counting mistakes

Review minutes apply to all completed jobs, not only the ones needing fixes. Rework is additional effort beyond preparation and review, including repeated attempts and manual replacement. If you already included correction time in review, remove it there before entering it again as rework. Enter zero only when that work really is absent; a missing measurement is not zero.

Known-answer example

For 100 jobs, B=20, P=4, R=5, q=20%, E=15, H=2 and S=12, expected rework is three minutes. Manual time is 33.3333 hours, assisted time 22 hours, net time 11.3333 hours and setup payback about 1.0588 months. Break-even review time is 11.8 minutes per job. At 12 minutes of review, net savings are negative. This threshold is not permission to reduce quality checks.

Boundaries and interpretation

Zero jobs still incur entered maintenance and have no per-job review threshold. Zero manual hours means the percentage change is undefined. Non-positive net savings have no setup payback. If each assisted job takes at least the manual effort, no increase in volume covers non-negative maintenance. Volumes are whole jobs up to one million; times are non-negative up to 10,000 in their stated units; rework is 0–100%. No inputs are sent to an AI provider.

The model assumes recurring effort and workload mix remain stable. It is not a queueing model, confidence interval, cash ROI or causal estimate. Provider charges, elapsed waiting time, incomplete outcomes and quality incidents require separate review. Read the pilot worksheet before using a real estimate.

Sources and boundaries

Checked 30 October 2026. Anthropic's 16 October study of its own workforce describes human attention as a constraint as agent output increases. It is a company-specific observation, not a marketing benchmark. Microsoft's delegation guidance retains human responsibility for reviewing and approving AI-assisted work. The worksheet, model and examples below are our original synthesis, not a method endorsed by either source. No client results are claimed.