Many companies aim to significantly increase their experimentation velocity, with advanced techniques helping to streamline the process. Feature rollouts and A/B testing measure new feature impacts, while parameters and layers enhance agility by removing hardcoded experiment references, allowing for faster iterations without altering the code. The use of statistical methods like CUPED reduces variance and noise in results, expediting the realization of experiment outcomes. Holdouts provide a way to measure the cumulative impact of features over time, filtering out external influences. Successful companies have adopted large-scale experimentation systems, integrating data into decision-making to avoid prolonged debates. Tools like Statsig facilitate this process, offering features such as feature gates and easy configuration changes to optimize the experimentation workflow, thereby closing the gap between sophisticated experimenters and others.