12 months ago, MaxContact's KPI Benchmarking report 2025-2026 confirmed that 60% of contact centres said AI and automation would be the main areas of increased investment in 2026.
Against that backdrop, and to coincide with the launch of MaxContact’s new Auto QA solution, MaxContact hosted a roundtable conversation with analysts, partners, and consultants to discuss issues emerging from the sector’s growing use of AI. The conversation covered a broad range of topics, including compliance, usage, leadership, boardroom pressures and the overarching impact that AI is having on customer-facing agents.
The conversation began with a simple question: “AI is telling us more than ever before. But what actually happens once it does?”
Businesses can now use artificial intelligence to review customer conversations, identify risks, and produce insights at a scale previously impossible. But that data doesn’t always lead to better outcomes, improved compliance, stronger customer experiences or lighter workloads. A recurring theme was whether leaders were asking the right questions about AI and thinking about the wider implications beyond technical adoption. Technology may be advancing quickly, but too many organisations struggle to turn what they find into meaningful action.
The operating model has not caught up with the technology
One of the clearest challenges from the discussion was the growing gap between what technology can do and how businesses are set up to use it. A lack of operational readiness means AI is increasingly exposing weaknesses in existing processes because it cannot decide what the organisation wants the process to achieve.
As Conor Bowler, VP of Product at MaxContact, described it, “The technology is past the pilot stage, but the operating model is way behind.”
The conversation explored how many businesses use technology to automate existing processes without realising they can carry old problems into a new system. That’s why buying and installing technology itself is only part of the solution. If businesses haven’t clearly defined what good looks like, which insights matter, and how people can respond to the results, AI will never deliver its full potential.
More data insight can create more work, not more action
Even if businesses and senior leadership teams know what they want their technology to do, a growing challenge is the sheer volume of data AI can create at the touch of a button. It’s changing job expectations, and training often hasn’t kept up.
Ben Booth, CEO of MaxContact, said this is already changing what managers expect: “Team leads now need to become analysts.”
They now have insight into why customers contact them, what automation is picking up, which queries are dropping off, and how calls are handled. But the bigger question, he argued, is what they actually do with that information.
Data literacy is increasingly important, and most businesses face an emerging skills gap they need to resolve. Sue Duris, Principal Consultant & Founder, M4 Communications, said, “There's so much data available that people end up trying to use all of it - because they don't know how to tell what's actually relevant to the business problem in front of them. You don't need to use all the data. You need to use the right data.”
While more insight is hugely valuable, especially in key areas of the business such as quality assurance, that data is only valuable if the people reviewing it can separate clear signals from the surrounding noise. That visibility can quickly overwhelm teams unless people know how to prioritise insights and respond to the right information.
Automation is changing the work agents are expected to do
One selling point of automation is that it handles basic administrative tasks seamlessly, freeing contact centre staff to focus on more complex cases. But the conversation turned to what that means in practice for the people who still need to handle what automation cannot manage.
Dom Black, Principal Analyst at Cavell, suggested that the answer lies in being selective about how AI is used. Straightforward, high-volume enquiries may suit automation, but more sensitive conversations can benefit from human judgement and empathy. He argued that artificial intelligence “isn’t going to take over the whole thing in the contact centre; it’s going to take over specific roles.”
But as Conor Bowler, VP of Product at MaxContact, pointed out, that shift also has significant consequences for agents. He warned that automating simpler interactions can make their roles more demanding than ever and create a “massive cerebral load” for agents.
He said, “As you automate the stuff with AI, their job becomes increasingly more difficult because you take away all the simple things and they're dealing with the high complexity, high empathy cases all day long.”
Chris Noble, CRO at ChorusCX, agreed, saying, “We always talk about customer satisfaction, don't we? But how often do we talk about agent satisfaction?” He pointed out that heavy workloads are “one of the biggest reasons why agents leave contact centres.”
It's not just the nature of the work creating the pressure, but also the wider societal narrative around AI itself. Dom Black highlighted the difficult messages employees across all sectors hear as they read predictions that AI will replace their jobs while watching their own employers invest in the technology. “How can you have a motivated, efficient, loyal base of employees when the story that they're reading in the news is, you're going to lose your job?”
This creates a dual challenge for businesses. They need to decide when and where automation can genuinely improve customer experiences, while also showing employees how the technology will help them do their jobs better. If automation increases the proportion of stressful, complex workloads for agents, businesses will need to rethink job roles and how to support staff as their workloads and responsibilities change.
Coaching, training and ongoing support need to evolve for AI to succeed
If job roles themselves are changing, then so should the learning and development of the people using the new technology.
In some areas, this has already happened. Ben Booth of MaxContact said this is clear in quality assurance teams. He suggested that when businesses use automated quality assurance, they change those roles: “When you put QA in, they don't reduce the headcount; they convert them to coaching.”
But while changing roles solves part of the problem, it doesn’t address another key challenge. Do employees have the skills they need, and why aren’t learning and development leaders involved in the initial discussions about AI adoption?
For Marianne Withers, Founder & MD of Withers Jones Collective, this isn’t a new problem. She questioned why businesses are still “having the same conversations we were having 25 or 30 years ago” about people management. She argued managers need knowledge of people, operations, data, and systems to understand “what they were doing, why it mattered and how to improve results.”
AI is exposing those gaps faster than people can react.
Nathan Dring, Founder and Director of Nathan Dring and Associates, described AI as “the biggest skill gap in business that has ever been because it’s been adopted so fast.” Yet the people responsible for developing those skills aren't necessarily involved early enough in the conversation. As Marianne Withers commented, “L&D teams have never been at the top table.”
Technical training to teach individuals how to use new software isn’t always the answer. Nathan Dring said his view can often raise eyebrows among senior leaders because, “Training isn’t the issue; behaviour is. You don’t train behaviour.” He argued that businesses need to create an environment and working culture where new behaviours can take hold.
AI adoption needs to start with a clear problem, not the technology
But skills and training are only part of the story. Challenges get much harder when senior leadership teams pressure businesses to adopt AI without a clear idea of what they want it to achieve.
Ben Booth said MaxContact sees this pressure first-hand, with business leaders being told they need to adopt AI without necessarily understanding what that should mean in practice. But as he put it, “they don't know what that means. They know it needs to happen.”
Cross-functional involvement should be essential to any AI discussion, yet budget, compliance, legal, and technology considerations can dominate decisions. Sue Duris, Principal Consultant & Founder of M4 Communications, commented, “AI is still treated as a compliance, legal, and tech exercise. Those are the people making the decisions, and the people who should be in the room often aren't - especially the customer voice.”
If businesses expect AI to change roles, responsibilities, and ways of working, they can't bring in learning and development at the end to help the technology land. People and manager development must be part of the conversation from the outset. Marianne Withers concluded, “We keep coming back to the same problem: organisations working in silos, with different teams making decisions in isolation.”
The answer isn’t always more technology
So, what should happen in contact centres now that AI can tell us more?
The answer isn’t always to invest in more technology or add new software solutions. To use the data to deliver measurable outcomes, businesses must articulate what they want their AI to achieve. They need to help managers build the data literacy skills to interpret it and implement new ways to support employees as job roles change.
But the channel also plays an important role.
Chris Noble, CRO at ChorusCX, pointed out that partners need to move away from talking about technological features and instead focus on understanding a customer’s needs and problems. That way, they can become a “trusted advisor”, take customers on a clear journey, and “lead them to the outcome that they want.”
Dom Black, Principal Analyst, Cavell, agreed with this, making a final prediction that “The channel partners I think will be successful in the future will sell outcomes to their customers. They're not going to talk about technology at all.”
AI presents a significant opportunity, but its value will ultimately be determined not by how much information it can deliver quickly. For contact centres and their partners, what matters is what they can do with that information next.
