Build the business case for automating a repetitive task with AI. Compare what it costs done by hand versus done by a model, and see your monthly savings, how fast the setup pays back, and the hours you free up.
All amounts in USD (model pricing is USD). Prices reviewed —.
Cumulative net savings. It crosses zero at payback.
Be honest about review time
Few AI workflows are fully hands-off. The residual minutes a human spends checking and fixing output are where optimistic business cases quietly fall apart. Put a realistic number in.
Savings scale with volume
The model cost per task is tiny next to a person's time, so the more often a task runs, the stronger the case. Low-volume tasks rarely justify the setup.
Freed time only counts if reused
Hours saved become real money when people move to higher-value work or you avoid a hire. If the time just evaporates, the savings are softer than the chart suggests.
Manual cost = (minutes ÷ 60 × hourly rate) × tasks. AI cost = model cost per task + residual review time, × tasks, + fixed tooling. The gap is your monthly saving; payback = setup ÷ monthly saving.
Usually yes — most AI workflows still need a person to check or handle exceptions. Omitting it overstates savings. Even a minute per task adds up at volume.
The one-time investment to get it working: engineering or prompt-building, integration, testing, and upfront tooling — what the savings must repay before the project is net positive.
It lowers risk, but a longer-payback project that scales bigger or frees senior people can be the better call. Use payback alongside total annual savings.
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