AI projects encounter multiple challenges, including poor data quality, misaligned use cases, and integration issues with legacy systems. Lack of explainability reduces trust, while skill shortages and high computational costs hinder progress. Ethical concerns, poor governance, model drift, and uncertain ROI also complicate implementation. Addressing these challenges is crucial for ensuring the long-term success and value of AI solutions.