AI adoption in enterprises is fraught with challenges that prevent organizations from deriving real value, despite widespread enthusiasm and initial investments in artificial intelligence. While many companies are experimenting with AI, a significant number face hurdles such as poor data quality, lack of understanding and skills, weak governance and ethics frameworks, and difficulties in scaling from pilot projects to full production. Interdepartmental conflicts, unclear success metrics, and security or bias concerns further complicate the landscape, leading to fragmented initiatives that often fail to align with business goals. Successful AI integration requires a methodical approach, including cleaning and standardizing data, training employees, creating robust governance structures, ensuring scalable infrastructure, achieving leadership alignment, setting measurable goals, and embedding strong security protocols. The real-world success of AI projects depends on addressing these human-centric challenges rather than merely focusing on technological advancements.