What problem should the pilot solve?
State the workflow, current burden and desired improvement. “Use AI” is a technology preference; “reduce manual document sorting while maintaining accuracy” is a testable operational objective.
What baseline will you compare against?
Identify existing handling time, cost, error patterns and exception rates where available. Without a baseline, a compelling demonstration may be mistaken for a measurable improvement.
Are representative inputs available?
A few carefully selected examples may not resemble real work. Consider typical cases, difficult cases, data quality, permissions and the boundaries of what can be shared with providers.
What happens when the system is wrong?
Specify review, escalation and fallback responsibilities. Error tolerance differs between drafting an internal note and taking an action that affects a customer or financial record.
What result would justify the next stage?
Agree the success measures, operational constraints and stop conditions before the test. Include review effort and integration needs so any apparent saving reflects the full workflow.