Automation has become a defining force in the customer experience. Between the chatbots that answer our questions and the recommendation systems that shape our choices, AI-driven tools are now embedded in nearly every interaction. But the latest wave of so-called “agentic AI”—systems that can plan, act, and adapt toward a defined goal—promises to push automation even further.“Every single person that I’ve spoken to has at least spoken to some sort of GenAI bot on their phones. They expect experiences to be not scripted. It’s almost like we’re not improving customer experience, we’re getting to the point of what customers expect customer experience to be,” says vice president of product management at NICE, Neeraj Verma.For businesses, the potential is transformative: AI agents that can handle complex service interactions, support employees in real time, and scale seamlessly as customer demands shift. But the move from scripted, deterministic flows to non-deterministic, generative systems brings new challenges. How can you test something that doesn’t always respond the same way twice? How can you balance safety and flexibility when giving an AI system access to core infrastructure? And how can you manage cost, transparency, and ethical risk while still pursuing meaningful returns?These solutions will determine how, and how quickly, companies embrace the next era of customer experience technology.Verma argues that the story of customer experience automation over the past decade has been one of shifting expectations—from rigid, deterministic flows to flexible, generative systems. Along the way, businesses have had to rethink how they mitigate risk, implement guardrails, and measure success. The future, Verma suggests, belongs to organizations that focus on outcome-oriented design: tools that work transparently, safely, and at scale.“I believe that the big winners are going to be the use case companies, the applied AI companies,” says Verma.Watch the webcast now.This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.