From Hype to Human Impact:
Designing AI-led Customer Experience in the Age of Agentic Automation

As generative and agentic AI accelerate from experimentation to enterprise priority, organisations face increasing pressure to define how these technologies should be applied technically, experientially, and ethically. While awareness of AI has surged, many businesses still struggle to align their ambition and understanding with what is realistically deployable today.

Join Ben Hancock, Manager Australia and New Zealand (ANZ) – Digital, Analytics, and AI NiCE and Michael Slip, Senior Director, Customer Experience , NTT DATA for an executive roundtable that will bring together CX, digital, technology, and business leaders to explore how conversational and agentic AI are reshaping customer experience and service delivery, and what foundations must be in place to move from hype to real-world outcomes.

The session will examine how advances in conversational AI and multimodal interfaces support more natural, human-like interactions, while agentic AI orchestrates end-to-end workflows across front-office engagement and back-office systems. Drawing on NiCE’s Cognigy AI capabilities and NTT Data’s deep integration, consulting, and ServiceNow expertise, the discussion will explore how organisations can design AI agents that deliver value and align with brand intent, governance, requirements, and customer trust.

Attendees will discuss:

• What agentic and conversational AI mean in practice, and how they differ from earlier automation approaches
• How natural language and multimodal AI interfaces have transformed customer experience
• Why trust, governance, and experience design are critical to deploying AI agents at scale
• How to determine where humans should remain in the loop, by design, not limitation
• What it takes to move from isolated AI use cases to end-to-end, integrated outcomes
• How systems integration and platforms such as ServiceNow support orchestration across the enterprise
• What practical steps organisations can take to close the gap between AI ambition and execution.