Step into the Future of CX - Experience AI Agents Live
October 13 - 14, 2026
| Free CX Network Webinar Series
Day One - 13 October 2026
10:00 am - 10:30 am
EST
How to prove ROI of agentic AI | What the CFO wants to hear
So you've invested time and money into agentic AI. It's improved resolution times and even boosted CSAT, but these aren't the metrics your CFO wants to hear about. CX Horizons research found that 52 percent of practitioners report rising pressure to prove ROI. Gartner also warned that over 40 percent of agentic AI projects are likely to be cancelled by 2027 due to unclear ROI and rising costs.
In this session we'll be dissecting strategies to prove ROI to your CFO beyond deflection and resolution stats. We'll look at understanding the variables that move the needle in the eyes of your board and how to effectively present outside of the CX team. We will take a closer look into how CSAT and resolution-time gains translate into cost-to-serve, retention and capacity metrics, and how to communicate them.
Attendees will learn:
- How to translate CX metrics, such as CSAT and resolution times, into a language your CFO understands
- Protecting your agentic CX rollouts with the facts and figures to prove value to the board
- How to understand the wider business impact of agentic CX investments through cross-departmental collaboration
10:30 am - 11:00 am
EST
What voice AI means for your customers and your bottom line
The voice AI agents market is expanding and it's not hard to see why. Voice is still where the biggest CX budgets and headcount sit. Voice, however, is also the most challenging channel to master, and, when it fails, there's no hiding it. Concerns around latency, interruption, accents and emotional cues can be off-putting to CX and contact center leaders erring on the side of caution. But the problem is, with such enormous efficiency gains on the table, ignoring this fast-developing tech simply isn't an option in 2026.
In this session we'll be realistic about what voice AI can do for your customer experience, your contact center workflows and, ultimately, your bottom line. We'll walk through success metrics that separate the the good from the bad, including the low latency that makes a conversation feel natural and why agents that do well in demos can degrade in production. We'll also take an honest look at the economics, including cost-per-contact.
Attendees will learn:
- What voice AI can – and can't – handle at scale in 2026, by contact type
- The metrics to test against before launch and after
- How to measure the financials of voice automation once failed containment and runtime costs are included
11:00 am - 11:30 am
EST
What is "agent washing", and how can you spot true agentic AI?
Gartner estimates that only around 130 of the thousands of vendors that claim to sell agentic AI are genuinely agentic – hence the "agent washing" phenomenon, in which chatbots and RPA are rebranded as "agentic AI". The CX Network Advisory Board has said similar - "too many tools and too much hype". This manifests in practitioners spending lots of time investigating tools that were never suitable in the first place.
In this session, we're giving attendees a way to separate the wheat from the chaff. We'll lay out a clear ladder of autonomy, from scripted to autonomous, and explain what each level can actually deliver to answer not "is this truly agentic?" and instead "which level of autonomy do I need?" We will also provide advice on the right questions to ask in demos and make a case that workflows aren't worse than AI agents – just suited to different tasks.
Attendees will learn:
- What are the four levels of autonomy and what can each deliver for CX?
- The questions to ask vendors during demos to ascertain if the product is genuinely agentic or a rebranded chatbot.
- How to recognize use cases for AI agents and know when a more traditional workflow is more appropriate
11:30 am - 12:00 pm
EST
What happens when the AI agent is wrong?
Hemang Upadhyay -
Sr. Manager - Product Management (AI),
LG Electronics
Everyone loves an AI-related PR disaster, as long as it isn't happening to them. Customer-facing AI carries real risk and even more perceived risk. Much of that risk starts with something ordinary. Two systems hold different answers, or one of them was never updated when the policy changed. A person spots the mismatch and knows which system to trust. An agent doesn't, and answers confidently anyway. When it gets that wrong, the damage is reputational, legal and financial, so plenty of leaders are still sceptical.
This session treats a wrong answer as a design problem, not an inevitability, and makes the case for a recovery layer built in at the start of a
deployment. A recovery layer catches failed interactions before the customer complains, shows which data or rule produced the answer, escalates with the context attached, puts the customer's outcome right quickly, and feeds what it learns back into the guardrails
so the same failure is less likely to happen twice.
Hemang Upadhyay has spent 16 years building enterprise AI, digital commerce and product data governance systems, and is currently Senior Manager for AI Product Management at LG Electronics as well as a member of CX Network's Advisory Board. Drawing from his experience across his career, he'll show how to design a recovery layer, and why CX teams can't do it alone.
Attendees will learn:
- What a recovery layer looks like in practice, and how it catches failures before customers report them
- Which three roles every AI journey needs named (process owner, data owner and recovery owner) and what each one answers for
- How to score the journeys you already have live, and why recovery quality says more about customer trust than containment rate does
12:00 pm - 12:30 pm
EST
Which agentic pricing model is best for your business?
Opaque pricing is one of the main objections we hear from the CX community. In June 2026, it became breaking news when Salesforce announced the acquisition of Fin, formerly Intercom and the vendor that pioneered per-resolution pricing. Ten days later Salesforce announced their own pay-per-resolution pricing model consisting of a charge when the agent resolves an issue autonomously from start to finish, and none if the customer asks for a human or is unhappy. Outcome-based pricing is a draw for CFOs but its impact on the internal buyer is unclear.
In this session we'll examine the pricing models currently at play in the market and explain how to identify the most appropriate model for your business. We'll look at the nuances of the controversial debate around how "resolution" is defined – and by whom. Drawing on case studies from companies who have first-hand experience selecting and implementing a solution, you'll leave with a solid grounding in the different pricing models and how to pitch them to your board.
Attendees will learn:
- The difference between agentic pricing models, and the specific commercial risk – and gain – each one representsThe questions to ask to uncover how a vendor defines a resolution, and how to work with partners to secure the best option for your business model
- How to measure your true effective cost per resolution approach, including hidden platform fees, escalation and failed containment
12:30 pm - 1:00 pm
EST
The rise of agentic AI: Trust, control, and the North American consumer
Michael Nevski -
Former Director, Global Insights,
Visa
Agentic AI is arriving faster than consumers can name it. According to the April 2026 Agentic AI Usage and Perception study, while almost every North American recognises AI and more than a third already use it, 60% have never heard of agentic AI and just 3% have used it.
Drawing on that research, Michael Nevski, former Director, Global Insights at Visa, unpacks the gap between curiosity and usage. Consumers hold hope and hesitation at once, with a majority believing AI will improve their lives but also holding concerns around it. That reluctance appears to be negotiable, with interest in AI shopping assistants increasing significantly.
The session will reveal and analyse the results of the major study, led by Nevski, and closes with a practical view of which customers adopt first, which safeguards move the needle, and where accountability lies when an AI agent makes a mistake.
Attendees will learn:
- A data-backed picture of agentic AI awareness, sentiment, and intent across generations and income levels
- The specific categories and tasks consumers will hand to an agent first - and the ones they won't
- Design and disclosure choices that convert hesitation into adoption
