Controlled experiments, particularly A/B tests, are pivotal for understanding product impact, yet many programs struggle with issues like low experiment frequency, biases, high costs, labor-intensive setups, statistical errors, cognitive dissonance, lack of trust, leadership buy-in, and poor process prioritization. Successful experimentation programs increase test frequency by streamlining processes, reducing costs, and fostering a culture that values data-driven decisions. Addressing biases and assumptions, facilitating collaboration between design and product teams, and maintaining trust in data can enhance program effectiveness. Leadership must be educated on the long-term nature of experimentation, emphasizing incremental improvements and learning from failures. Additionally, granting autonomy to product teams for experiment selection can boost test velocity and overall program success.