Empathy for Humans, APIs for AI: Why One CX Model No Longer Works

“An action has been taken, that then leads to a complaint and a complex query that the agents are then having to unpick,” Brough noted, “because customers acted on what they thought was the correct advice. But if it's been misinterpreted, it's a problem further down the chain." 

Why Human Customers and AI Agents Need Completely Different CX Responses 

The challenge for organizations lies in underestimating how differently AI agents consume information compared with human agents. What works well for a person (who can infer meaning and apply judgement) will need to be structured very differently for an AI agent. Agents require a different kind of information architecture, according to Brough: 

"We're designing for average demand, for human demand. And actually, what looks good to a human is very different to what looks good to an AI." 

The consequence of ignoring this distinction is predictable. 

"We've gone for one model to give consistency, but that's not going to be helpful in the future and will lead to mistakes," Brough warned. 

What does dual lane actually mean in practice? 

Routing Humans and AI Agents Separately 

Separating the paths is a natural design discipline that organizations should be building now, before volume forces the issue. 

"The first step is understanding: is it an AI or a human that wants the answer? And having that dual route will allow you to offer either that speedy, quick answer, or that more complex, more emotional, more brand-orientated answer that humans are looking for," Brough advises. 

Identifying what kind of entity is making the request and serving it the kind of response that will work for it requires changes to how organizations think about intent, how they classify demand, and crucially, how they build and maintain their knowledge. As Brough pointed out: 

“You still want good knowledge for your humans, so making sure that you've got a complete knowledge profile is important ." 

How Mixed Contact Flows Are Damaging Agent Experience 

 Getting this wrong has consequences across the contact center, but the impact is often felt most directly by agents. As with the early days of chat automation, teams can find themselves managing the fallout from technology that was intended to reduce pressure, improve journeys, and simplify work. 

"The agents can only then deal with what's left or what's gone wrong. And so they become frustrated," Brough said. 

That frustration is more about the nature of the work than the volume of it. Agents who came into the industry to handle complex problems and make a genuine difference start to feel like a backstop for the technology. As Brough put it, "We always talk about agents being there to handle the more complicated queries, and that's absolutely right. That's what humans are great at.” 

“We're not great if that has been caused by a failure of technology that was supposed to make life easy. We get frustrated. Your agents get disengaged with the service that you're offering." 

Dual-Lane CX Design Must be a Contact Center Priority Now 

The era of designing CX for a single type of interaction is coming to an end. As AI agents become part of the customer journey, organizations can no longer rely on one experience model to serve both people and machines. Human customers need empathy, context, and reassurance; AI agents need structured, consistent, and machine-readable information. 

By trying to serve both, organizations risk delivering an experience that works well for neither. 

The first step is a design decision that recognizes there are now two types of customers and understands what each of them needs, rather than assuming the same answer will satisfy both.