Agentic AI promises more natural customer service automation, but too many deployments fail to deliver. According to experts from a recent CMP webinar, success depends on clean, accessible data and a clear-eyed view of where AI can replace live support - and where it should support human agents instead.
"If you aren't thinking about it the correct way, that's really a very negative sign for how you're going to care about customers," said Brian Cantor, managing director at CMP. He argued that the technology to create great experiences exists, but a lack of proper framework and vision holds leaders back.
Data and people are at the core of agentic AI
Getting agentic AI right starts with information. Every piece of customer data - addresses, emails, phone numbers - helps the system build a more personal experience, according to Merlin Bise, CTO at Inbenta. "Any amount of information you can gain, it's leverage," Bise said. "So anything a customer will give you, you should be collecting it. The more information, the more power."
But collecting data is not enough. Teams must store it securely, comply with privacy regulations, and structure it so AI can access what it needs. Bise warned against connecting a large language model to a simplistic database and expecting magic: "If you have any desire to do a true agentic AI at some point, and you're going to turn a reasoning model loose on poor data, it's going to behave based on what you give it."
For customer support professionals looking to build these skills, resources like AI for Customer Support can help teams understand the fundamentals of data-driven agentic systems.
Where agentic AI can - and cannot - replace live support
Deployed correctly, agentic AI will replace some live interactions, but leaders need to know their contact center workforce well enough to decide which tasks are safe to automate. "You're going to have some stuff you're not going to be able to do," Bise said. "That's why it's critical to get buy-in from all the people that impact your business."
Cantor emphasized that the shift requires a mindset change, not just a technology investment. Without a clear vision of how agentic AI fits into the overall customer journey, even the best tools will underperform. For contact center supervisors managing that transition, structured training can help. An AI Learning Path for Call Center Supervisors covers how to align data readiness with team roles and customer expectations.
Why this matters for customer support professionals
Agentic AI won't eliminate customer support jobs - but it will change them. Agents and supervisors who understand how data quality affects AI performance, and who can identify the gaps that still require human judgment, will be essential. The experts' advice is clear: treat agentic AI as a tool that augments people, not as a black box that replaces them. That means investing in data hygiene, securing buy-in from the front line, and keeping a clear framework for where automation adds value and where it falls short.
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