Experimentation & A/B Testing

AI products need robust experimentation frameworks because intuition about what works is even less reliable than in traditional software. A feature that seems obviously better might actually confuse users, and a model that performs better on benchmarks might deliver worse business outcomes. A/B testing with AI introduces complications that standard testing doesn't have: models can behave differently across user segments, effects can take longer to materialise, and the interaction between multiple AI components can create unexpected results. You need larger sample sizes to detect meaningful differences, careful attention to what you're actually measuring, and patience to let experiments run long enough. Beyond formal A/B tests, a culture of experimentation means being willing to try things quickly, measure honestly, and kill ideas that don't work regardless of how much effort went into them. It also means documenting what you learn from failed experiments, because understanding why something didn't work is often more valuable than confirming that something did.