Contact centers frequently overlook the data quality and integration prerequisites that determine whether AI can actually function effectively, treating AI deployment as primarily a software installation rather than requiring foundational data work. The change management and training investment required for genuine agent and supervisor adoption is consistently underestimated relative to the technical deployment effort. Many organizations underinvest in defining clear success metrics before implementation, making it difficult to demonstrate value or guide iterative improvement after launch. The ongoing tuning and maintenance AI systems require to maintain accuracy as products, policies, and customer needs evolve is often not planned for beyond initial deployment. Finally, organizations frequently overlook the importance of starting narrow and proving value before expanding scope, instead attempting comprehensive transformation that increases risk and delays any individual use case from reaching maturity.
Expert POV