Scaling up experimentation entails moving away from a data-scientist-centric approach to one that empowerseveryoneon product, marketing, engineering, and operations teams—product managers, software engineers, designers, marketing managers, and search-engine-optimization specialists—to run experiments. But that presents a challenge. Drawing on our experience working for and consulting with leading organizations such as Airbnb, LinkedIn, Eppo, Netflix, and Optimizely, we provide a road map for using experimentation to increase a company’s competitive edge by (1) transitioning to a self-service model that enables the testing of hundreds or even thousands of ideas a year and (2) focusing on hypothesis-driven innovation by both learning from individual experiments and learningacrossexperiments to drive strategic choices on the basis of customer feedback. These two steps in tandem can prepare organizations to succeed in the age of AI by innovating and learning faster than their competitors do. (The opinions expressed in this article are ours and do not represent those of the companies we have mentioned.)