As AI automates insight generation, the primary role of CX professionals will shift to training and refining AI models. Their focus will be on validating AI-driven recommendations, teaching the system brand standards, and ensuring the AI is current and accurate, rather than performing manual data analysis themselves.
As both consumers and companies adopt personal AI agents, many transactions will occur directly between these bots without human involvement. This disintermediates the customer from the company, fundamentally changing the nature of CX and requiring new ways to measure success and reinforce brand value in a fully automated interaction.
Despite heavy investment, overall CX scores are falling because new problems constantly emerge from new products, technologies, and crises. The goal isn't to solve all issues permanently, but to embrace CX as a continuous game of "whack-a-mole," focusing on building agility to rapidly address issues as they appear.
AI assistants will deliver proactive, conversational insights, freeing CX teams from reactive dashboard analysis. Instead of monitoring static reports, leaders will simply ask their AI what to focus on, rendering traditional dashboards obsolete and enabling a more strategic, real-time approach to customer experience management.
When AI can directly analyze unstructured feedback and operational data to infer customer sentiment and identify drivers of dissatisfaction, the need to explicitly ask customers through surveys diminishes. The focus can shift from merely measuring metrics like NPS to directly fixing the underlying problems the AI identifies.
Rather than radically restructuring teams around AI, the immediate future involves individuals augmenting their personal workflows with AI assistants. This "cyborg model" treats AI as a personal tool for finishing tasks, fixing errors, and handling busy work, creating a hybrid where each person learns to use AI to enhance their own abilities.