The AI boom has seen countless innovations, but launching a product is only half the battle. Many promising AI startups stumble and fail in the critical post-launch phase. This article delves into the most common pitfalls, from failing to understand genuine user needs and grappling with data quality issues to underestimating scaling costs and neglecting MLOps. Understanding these mistakes is crucial for founders and product managers aiming to build sustainable and successful AI solutions in the competitive US market.