A/B testing is crucial for continuous product improvement by enabling informed decision-making through the comparison of two product versions. However, it often presents challenges due to competing priorities, limited resources, and suboptimal testing conditions. Despite these hurdles, valuable insights can still be gained from imperfect conditions. For example, a SaaS company considering changes to its onboarding flow may face dilemmas between increasing conversion rates and maintaining data collection on user personas. Ideal A/B tests involve randomizing users to ensure unbiased results, but real-world scenarios often necessitate deviations from this approach. Strategies such as randomizing customer accounts instead of individual users, experimenting with fewer users, excluding certain customer groups, prioritizing customer value over strict adherence to A/B results, and defining rollout decision criteria can still yield beneficial outcomes. Ultimately, A/B testing serves as a valuable tool for achieving digital success through quicker learning and minimal risk.