What's the difference between traditional A/B testing and AI-driven A/B testing?

Traditional A/B testing is largely manual. It involves setting up tests and analyzing results manually and then making decisions based on that data. It can take time and often requires a lot of trial and error. AI-driven A/B testing, however, automates much of the process. AI analyzes data quickly, continuously optimizes tests, and provides insights faster and more accurately. While traditional A/B testing is limited in scope and speed, AI can handle more complex tests, make adjustments instantly, and help you get better results quickly, making it a much more efficient approach to optimization.