Organizations frequently deploy AI without first cleaning and integrating the underlying data it depends on, resulting in inaccurate recommendations or failed transactions. Overly broad initial scope - attempting to automate too many interaction types simultaneously - delays time to value and increases the risk of poor customer experiences during rollout. Insufficient agent involvement in design and testing produces tools that do not fit actual workflow needs, leading to low adoption. Underestimating the change management required for agents to trust and effectively use new AI tools is a persistent failure point. Many implementations also lack clear success metrics defined before launch, making it impossible to demonstrate ROI or guide iterative improvement. Successful implementations typically start narrow, involve frontline staff throughout, and measure rigorously from day one.
Common Mistakes