Set a baseline before the change
Choose a unit that represents actual work: one intake request, one approval or one completed document. Record elapsed time, active work, corrections and exceptions for comparable cases. Note the dates and conditions, including seasonal volume or a staff change. Without a baseline, a faster demonstration cannot establish improvement in daily operations.
Decide in advance what counts as completion and an error. Keep the same definitions during the pilot. If private data is involved, use only approved records and restrict who can see the measurements. A simple tally maintained by the process owner is often enough to start learning.
Measure use and effort together
Track whether intended users complete the workflow, how often they use it and which steps still require help. Also record AI review time, failed executions and support work. A system that performs well in a demo but requires constant intervention may not improve the team’s operating load.
Compare similar cases and report the sample size and period. Use the median time when a few unusual cases distort the average, and keep exceptions visible. Describe uncertainty openly. A small before and after sample can suggest a direction, but other changes may explain part of the difference.
Use evidence for the next decision
Separate reduced handling time from money saved. Minutes recovered do not automatically reduce payroll or create new sales. Include provider consumption, maintenance and the work needed to keep the flow reliable. Only use financial assumptions that the business owner can verify, and label them as assumptions.
Review whether to continue, simplify or stop against the criteria agreed at the start. Keep the evidence and the reasons for that decision with the project. Broader functionality should respond to observed use and an approved scope. A pilot can be valuable even when it shows that the proposed automation is not worthwhile.
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