Everyone’s rushing to build AI products, but in our study of 767 software users, labeling features as ”AI-powered” lowered performance expectations and didn’t boost willingness to pay. The AI product graveyard is filling up fast. This session reveals the science behind successful AI product adoption, showing how concepts like effort-reward balance and status quo bias determine whether users will adopt your AI features. You’ll learn why small changes in product architecture can drive big adoption differences, backed by real A/B test results, specific UI examples, and actionable frameworks.