It’s no surprise that agentic AI dominated conversations during the Contact Centre Expo at Excel London, with its promise of delivering new ways to enhance the customer experience while reducing costs. But behind the glossy marketing, the challenge for tech buyers is to cut through the noise and find the right solution for their needs.
For many enterprises, the toughest part of navigating the agentic AI wave is determining whether the technology actually solves a real problem. Danny Gunn, Head of Workforce Planning at Bet365, put it candidly:
“Part of the understanding of AI is the use case. It may sound great from the sales pitch, but does it actually work? There are quite a few [solutions] where we tried them and they don’t actually work, whether that’s because we’re not ready and our backend processes can’t use all of that or the technology isn’t quite as good as the sales pitch that we get to see.”
For organizations operating under financial constraints, the hype can create tension to turn to AI as a cure-all to deliver cost savings. Kim Baker, Head of Operational Support Services at UK housing association L&Q, noted that leaders are under “huge pressure to not spend too much money and save as much as we can.”
“Everyone just says AI as if it’s now the answer to everything, but I don’t think people really fully understand what AI is and what it might be able to do for them.”
Baker added a critical reminder that any organization considering agentic AI for automation needs to address “simple truths” before jumping on the bandwagon:
“There’s no point launching AI if your data is not right in the first place, because where’s it going to look to answer these questions?”
Without reliable data, even the most advanced agentic AI implementation will deliver inconsistent results, and undermine trust in the technology.
Understanding Where AI Truly Adds Value
The pressure to “have AI” is evident across the industry, often overshadowing the need for alignment with real organizational challenges. Keith Griffin, Cisco Fellow VP, noted how easily organizations default to AI without planning or frameworks: “It is very much about ‘we need to have some AI capability’ but not think deeply about where there’s evidence of where it [gets results] and some of the reasons why AI adoption scores.”
“Mismatched use cases, expecting AI to do things that it’s not very good at, or assuming that it can do more than possible,” all result in failed implementations, Griffin added. “People are getting caught up with which AI models should be used, and it really doesn’t matter… as long as it’s safe to use and an appropriate use for the organization.”
Chris Rainsforth, Director of Learning & Innovation at contact center industry body The Forum, noted that AI has become “a catch all” for any operational challenge and highlighted the pitfall of rushed deployments:
“What we’ve seen a lot of examples of, unfortunately… people trying to deploy something without understanding the problem they’re trying to solve. In the first instance, they spend a lot of money, they spend a lot of time, spend a lot of effort doing something, and then it doesn’t get the results.”
Leadership often grows frustrated when a costly AI project fails to deliver results, questioning why the investment isn’t paying off. Untangling those underlying issues then becomes a difficult and time-consuming process.
But encouragingly, more organizations are beginning to pause and reassess, Rainsforth said.
“On the flip side, we are starting to see more people take a more considered approach, going, ‘what are the outcomes? What am I trying to solve? Let’s then work back from that to understand what technology can enable us to deliver it.’ And AI might not be the answer to every problem. It might be something else.”
“People are starting to have those conversations be a bit more kind of thoughtful about that approach, rather than just wasting time and effort and money,” Rainsforth said.
Putting the Customer First in Tech Decisions
Ultimately, when leaders pay close attention to what their customers need, rather than what the market is hyping, they gain a clearer sense of which tools will genuinely improve experiences and which investments aren’t worth pursuing, several speakers emphasized.
Listening to customers provides the grounding needed to make purposeful, informed choices about where and how to deploy new technology, whether that’s agentic AI or other systems.
Frontline experience shows that customers don’t tend to share the industry’s fixation on AI-driven speed and automation; they’re simply looking for problems to be solved efficiently. As David Holmes, Director of Sales at UK utility SSE observed:
“I don’t have customers tell me, ‘I hope you hurry up with that AI’. I don’t have customers saying, ‘I hope you handle my call quicker’; customers care about the resolution, they care about the time on the phone. They do care about simplicity and I think most sales could benefit from all of that, and that’s where technology can help.”
Across discussions on the show floor, that theme consistently resurfaced. The path to meaningful AI adoption starts with understanding customer needs. When enterprises anchor technology decisions in real-world pain points rather than hype cycles, they’re more likely to avoid missteps and deliver measurable improvements.
