Common challenges include data quality issues, such as missing or noisy data, and the difficulty of obtaining labeled data for supervised learning. Additionally, ensuring scalability of AI/ML models in production environments, handling large datasets efficiently, and overcoming model interpretability issues are common hurdles. Furthermore, integrating AI/ML models with existing data systems and workflows can be complex.