AI product adoption is heavily influenced by a psychological factor known as "Confidence in AI Results" (CAIR), which can be measured, predicted, and optimized to determine the success of AI products beyond just their technical capabilities. The CAIR framework considers the value users derive from AI, the risk of AI errors, and the effort needed to correct these mistakes, emphasizing that product design plays a crucial role in influencing user confidence and adoption. Successful AI products, like Cursor, demonstrate high CAIR by minimizing risk and correction effort while maximizing value, showing that even with identical AI technology, product design can significantly impact user adoption and confidence. In high-stakes domains such as finance and healthcare, where numerical precision is critical, AI products must navigate inherent limitations by integrating strategic human oversight and offering features like undo capabilities and transparency to boost CAIR. The emphasis on CAIR suggests that AI readiness should be evaluated not merely on accuracy but on how product design can enhance user confidence, guiding product leaders to focus on engineering confidence through thoughtful design rather than solely on advancing AI technicalities.