What is the task?
Choose the customer intent, action and measure of completion before selecting the model.
AI in customer experience applies machine learning, generative AI, agentic AI, speech technology and automation to customer journeys and service operations. It can understand intent, generate and retrieve information, recommend actions, complete approved workflow steps, analyze interactions and support or escalate to human specialists.
AI in customer experience applies machine learning, generative AI, agentic AI, speech technology and automation to customer journeys and service operations. It can understand intent, generate and retrieve information, recommend actions, complete approved workflow steps, analyze interactions and support or escalate to human specialists.
A practical AI CX architecture usually includes channels, identity, knowledge, models, tools, enterprise systems, workflow orchestration, observability and a human escalation path.
Generative AI creates content or responses. Agentic AI adds planning, tool use and multi-step action toward an outcome. A customer-service agent may both generate language and act through CRM, order, booking or ticketing systems.
Human specialists remain important for ambiguity, high-value relationships, emotional situations, risk, negotiation and exceptions. The operating model should make escalation intentional rather than a failure state.
Treat AI as part of a customer operation, not as a stand-alone response generator.
Choose the customer intent, action and measure of completion before selecting the model.
Specify permissions, stale knowledge, policy boundaries and human recovery.
Assign ongoing owners for evaluation, incidents, releases and workflow improvement.
Run what matters today. Reinvent what comes next. Create more moments where customers can simply say, “Aha, it’s handled.”