Generative AI has already changed how brands understand and interact with customers but the next leap is agentic AI, with Gartner predicting that agentic AI will resolve 80 percent of common customer service issues by 2029. These intelligent systems can autonomously process and act on customer interactions, creating faster, more personalized experiences.

In this session, Nitin Kumar, Director of Data Science at Marriott Hotels, will explore how organizations are using agentic architectures to transform customer care and automate complex workflows. We’ll also discuss the practical challenges of deploying these systems responsibly, from data security and consistency to governance and ethics.

Attendees will learn:

• How agentic AI is redefining customer care through human–AI collaboration rather than automation alone.
• The guardrails and governance frameworks required to deploy generative AI responsibly at scale.
• How unstructured data and real-time intelligence are powering more personalized, efficient, and empathetic customer experiences.

The next evolution in customer experience is moving beyond isolated AI interactions toward intelligent orchestration where AI agents autonomously manage and coordinate customer journeys across channels. Gartner predicts that by 2029, agentic AI will resolve 80  percent of common customer service issues, fundamentally transforming how brands interact with customers. This new approach can enable faster and more personalized experiences while maintaining consistency across touchpoints.

AI agents are not just reactive tools but proactively orchestrate workflows, pulling in insights from multiple data sources across journeys and escalating complex cases to humans when necessary. In this session, we will hear from companies that have embraced this architecture who are seeing measurable improvements in efficiency and CSAT. We’ll give you the tools to assess whether agentic AI is right for your business, reveal the secrets of securing buy-in and successfully implementing the technology.

Attendees will learn:

• How AI agents orchestrate end-to-end customer journeys, coordinating multiple interactions and channels for seamless CX.
• Practical strategies for integrating agentic AI into existing workflows while ensuring consistency and accuracy.
• How to balance autonomous AI actions with human oversight to deliver both efficiency and empathy across all customer touchpoints.

Customers expect convenience and control and are increasingly willing to help themselves when the experience works well. Research shows that 69  percent of consumers try to resolve issues on their own before contacting support, yet many companies do not provide simple self service options such as knowledge bases or automated assistance. There is a major opportunity for brands to elevate customer satisfaction and operational efficiency by embracing intelligent self service powered by adaptive automation.

Intelligent self service, such as AI driven chatbots and virtual assistants that automate workflows and guide customers, transforms routine interactions into fast and personalized resolutions.

Attendees will learn:

• How intelligent self service automation reduces friction and meets customer expectations for fast, always-on resolution options.
• Strategies for adaptive automation that interprets and responds to customer intent in real time, steering users to the right answers or escalating when needed.
• Best practices for balancing AI with human touch, ensuring that automation enhances, rather than replaces, empathy, while freeing teams to focus on higher value interactions.

Customers are engaging across an ever-growing array of channels – voice, chat, video, and in-person – and brands are increasingly challenged to deliver experiences that feel contextual and effortless. Voice AI and multimodal support are transforming CX by enabling interactions that understand intent and sentiment across channels. Some research indicates that by 2025, 50  percent of all searches are expected to be voice-based, and companies that leverage voice and multimodal AI effectively can reach customers more easily as well as increase customer engagement and satisfaction significantly.

Multimodal AI goes beyond single-channel interactions by combining visual, textual, and auditory signals to deliver more accurate and human-like responses. For example, a customer might start a query via voice, continue through chat, and on the app, all orchestrated seamlessly by AI. This session will cover the basics of voice and multimodal AI, explaining the benefits and risks, and discussing how it can be leveraged to improve CX.

Attendees will learn:

• How Voice AI and multimodal support enable more natural and context-aware customer interactions across channels.
• Strategies for implementing seamless handoffs between channels and AI systems while maintaining consistent CX
• How to measure the impact of voice and multimodal solutions on key metrics such as first-contact resolution and customer satisfaction.

Moving AI from prototype to production is where the promise of better CX meets reality. In regulated industries especially,u that journey is littered with hard constraints. In this session, Mrunal Gangrade, VP at JPMorgan Chase, will walk us through what it takes to deploy and govern AI in customer-facing environments where data privacy and cybersecurity aren’t afterthoughts but non-negotiables. Drawing on two decades of engineering experience in financial services and hands-on work moving systems to cloud and into production, we will focus on real operational challenges such as training, choosing build vs buy, spotting data drift, and putting transparency and ethics at the centre of AI deployments.

You’ll hear practical guidance on starting small, proving value, and scaling safely, including how to identify what to automate, how to embed explainability into your models, and why continuous monitoring is essential to prevent models from silently degrading.

Attendees will learn:

• How to move from development to operationalization in a regulated environment.
• Practical guardrails for safe AI, including explainability, continuous monitoring for data drift, and integrating ethics and security into governance.
• How to make build vs buy decisions and prepare teams (training, tools, cross-functional governance) so AI delivers real, sustainable CX improvements.

As AI becomes embedded across CX, from self-service and personalization to automated orchestration, a central challenge remains: How do you measure actual value? According to research by Lumoa, 71 percent of business leaders are already using generative AI in CX initiatives, yet CX Network research found that just 29 percent of professionals measure the return on how AI improves CX, pointing to widespread difficulty in quantifying impact.

This session will equip leaders with practical approaches to defining meaningful success metrics that link AI initiatives directly to business outcomes such as revenue, efficiency, retention, and satisfaction. While AI is widely adopted, many organizations lack frameworks that connect activity metrics (like automation rates) to real outcomes. By exploring established methods and case examples, we’ll show how to translate investment into measurable value and use those insights to guide scaled adoption and strategic decision-making.

Attendees will learn:

• How to define meaningful AI-CX success metrics that go beyond usage to quantify business impact (e.g., financial gains, cost-to-serve improvements, and customer outcomes such as churn).
• Approaches to measurement frameworks that isolate the contribution of AI even when multiple initiatives influence the CX journey.
• How to balance quantitative KPIs with qualitative indicators (such as customer trust and loyalty) to justify AI investment to leadership and stakeholders.

Personalized experiences are now a baseline customer expectation, with research showing that 67 percent of consumers expect personalized online shopping experiences. It is no surprise, then, that personalization has been found to create between 5 – 25 percent uplift in revenue. This session brings together leaders from Apple and Walmart to explore how advanced recommendation systems, powered by LLMs and behavioral data, shape customer journeys from discovery to purchase, and how to balance relevance with trust and control.

Scaling personalization across millions of users introduces unique challenges: managing huge data volumes, keeping models functional under enormous loads, and ensuring recommendations remain relevant rather than generic or intrusive. Attendees will get a practitioner’s view into these trade offs and how intent driven systems (like app store ads or ecommerce discoverability layers) are evolving to meet them.

Attendees will learn:

• Key strategies for building and evolving LLM powered recommendation systems that boost engagement and revenue.
• How to maintain relevance and measure success at enterprise scale.
• Best practices for aligning personalization technologies with brand experience and customer trust.

AI investment, adoption, and hype have reached unprecedented levels but so have questions about sustainability, ROI, and expectations. Global investment in AI continues to rise, and yet Gartner has suggested that, due to lack of good data, 60 percent of organizations will abandon AI projects in 2026. As economic pressure mounts and boards scrutinize spend, CX leaders are increasingly asking whether we’re in an AI bubble, and what will happen to the discipline if (or when) that bubble bursts.

This interactive roundtable, open to all attendees, will close the conference by cutting through the hype to explore what the current AI moment really means for CX. Participants are invited to discuss where AI is genuinely transforming customer outcomes and where it may be over-engineered, under-measured, or misaligned with customer needs. The conversation will focus on how CX leaders can separate long-term, beneficial capabilities from short-term trend, and make smarter bets going forward.

Participants will explore:

• Whether current AI adoption patterns in CX signal a bubble or a necessary but somewhat messy transition phase.

• What an AI correction could mean for CX priorities, budgets, vendors, and customer trust.

• How to future-proof CX strategies by focusing on measurable value, customer outcomes, and long-term capability rather than hype.