Many businesses are rushing to incorporate AI into their products, but about 42% fail due to poor market fit and misunderstanding of user experience (UX). A key issue is the disconnect between AI capabilities and user expectations. For instance, labeling an AI action as “Generate Report” misleads users into thinking the task is complete, while a more accurate term like “Draft Report” sets the expectation that further input is needed. This highlights the need for UX that builds trust and fosters understanding of AI’s limitations and role. The article identifies several specific pitfalls contributing to AI product failures. The "black box" problem leaves users in the dark about how AI makes decisions, leading to a "trust void." Solutions include showing confidence scores and providing citations for information used by the AI. Automation bias emerges when users overly rely on AI, which can be mitigated by framing outputs as suggestions rather than commands. The "blank canvas problem" occurs when users are unsure of how to start, which can be resolved with clear prompts or templates. Friction in user experience, such as complicated navigation, can frustrate users. Simple solutions like inline actions or predictive UI can enhance usability. AI’s propensity for “hallucination,” where it generates incorrect information confidently, can undermine user trust. Using honest microcopy and visual cues for uncertainty can help users navigate these shortcomings. Lastly, without feedback loops, AI systems struggle to learn from mistakes, making it crucial to implement mechanisms for user feedback and improvement. These UX strategies are essential for transforming AI products from potential failures into effective tools that genuinely assist users.