Measure the cost of completing the work, including review, corrections, integration, support, and model usage. Compare that total with the previous process at the same quality standard. Report capacity released separately from cash savings or new revenue.
Define the Unit of Work
Choose a completed task with a measurable quality standard. Record the baseline effort and volume, including correction and escalation.
Include Review and Operating Costs
Measure assisted completion time including checking and correcting the output. Include implementation amortization, support, hosting, and model usage without counting a cost twice.
Separate the investment needed to establish a workflow from the cost of running it. A pilot may carry setup work that will support later use, but expansion can also introduce new source preparation, support, and integration costs. State how the comparison allocates those expenses and which task volume it assumes. Then examine what changes if adoption is slower or review takes longer than expected. This makes the estimate useful for a spending decision without presenting a favorable pilot calculation as a proven result at a different scale.
Show a Transparent Illustrative Calculation
The following calculation uses assumed figures, not measured customer results. Separate capacity value from realized cash savings.
Example: Hypothetical monthly case: 1,000 tasks at 12 minutes and $60 per labor hour have $12,000 of baseline labor value. At 6 minutes including review, assisted labor value is $6,000. With $2,000 of incremental monthly operating and allocated implementation costs, the difference is $4,000 of estimated net resource value, not automatically $4,000 of cash saved.
Verify How the Benefit Is Realized
Track whether released capacity reduces overtime, serves more demand, or remains unused. Revisit the calculation when task volume, error rate, model pricing, or support burden changes.
Discuss the implementation scope with Sprinklenet. A useful starting point: a task-level cost and value assessment using the customer’s own baseline.
References
The recommendations above are Sprinklenet’s practical guidance. Technical context: NIST AI Risk Management Framework.
