What are the common challenges when implementing AI workflows?

Common challenges include data quality issues, integration complexity, change management resistance, and unrealistic expectations. Poor data quality leads to inaccurate AI outputs, so data cleaning and validation are crucial. Integration can be complex if systems don't have good APIs or documentation. Team members may resist automation fearing job loss, so clear communication about augmentation vs replacement is essential. Unrealistic expectations about AI capabilities can lead to disappointment—AI works best for specific, well-defined tasks. Other challenges include cost management, maintaining AI models over time, and ensuring compliance with regulations. Addressing these proactively increases success rates.