Run a two-week baseline, then two weeks with the automation active. Keep all else standard, and protect critical customers from early glitches. Compare both periods using medians to reduce the influence of odd days. Document any surprises openly. Guardrails ensure that a disappointing trial does not become a costly incident, while still giving you credible evidence to trust. The process builds courage for the next improvement and secures buy-in.
Split by customer segment, product type, or time blocks rather than complicated randomization. Use simple assignment rules anyone can audit. Track spillover: people may adopt the faster method even in the control. That is a useful signal too. When the difference is large and consistent, stop early, declare success, and spread the better path carefully. Practical A/B keeps learning nimble under real constraints and protects service quality.