Real-time Customer Context is the New Currency of Contact Centres

Pedro discusses how multi-agent CXA platforms turn messy, fragmented customer data into contextual, autonomous decisions, helping contact centres eliminate silos, improve accuracy, and deliver personalised resolutions at scale.

Excerpts from the interview:

With CXA systems turning messy, unstructured inputs into actionable insights, how has your definition of “customer context” evolved? 

Customer context has changed so much in the last few years. 

Before, the issue used to be about data collection. I’ve watched plenty of agents spend customer calls frantically scribbling on post-it notes, then have only a few minutes to collate those notes into the CRM. 

Nowadays, I don’t think the issue now is so much about data collection – but more about making good use of that data. 

Customers don’t want to be treated like a number; they want to be treated like individuals. They know that companies are collecting data about them: when they like to browse, on which device, and the products they’ve been interested in recently. 

So while using automation to provide a fast response is a benefit to the customer, generic responses that don’t take these preferences into account can quickly erode trust. 

Businesses need to make sure that data is managed effectively. Aggregating structured information like CRM records and unstructured data like transcripts and emails continues to be a challenge for many businesses. 

But at any touchpoint, customers only see one thing: the brand. It doesn’t matter to the customer where the conversation started; they expect brands to have the full context regardless of the communication method or device used. 

In your view, what’s the most overlooked challenge contact centres face, and how can CXA platforms help address this? 

Poor automation is a big problem, and many contact centres have attempted to bring in some elements of automation to keep up with wider industry changes and evolving customer expectations. 

But early-stage automation solutions were often rule-based and struggled to tackle the more complex enquiries that commonly come through to the contact centre. This poor implementation of automation resulted in a tangled web of disconnected bots and fragmented systems. 

Not only does that translate to a poor experience for the customer, but a source of frustration for teams as well. 

Customer experience automation’s (CXA) strength is allowing businesses to automate complete customer interactions at scale, without the limitations of earlier automation systems. It makes this possible by bringing together a number of expert AI agents, known as multi-agent orchestration, to solve more complex problems. 

Rather than simply enlisting a generic ‘AI agent’ with a standardised workflow, you might have ones that are more sector and task-specific. In healthcare, for example, that might look like a scheduling agent to book appointments, whereas in retail, you might have agents handling returns or shipping issues. 

These agents have advanced real-time processing capabilities and industry-specific intelligence that allow for problem-solving beyond simple logic rules. CXA goes beyond simple automation – it’s a coordinated, autonomous resolution of complex issues. 

How does siloed service delivery directly impact a customer experience? How could a more orchestrated, autonomous model change the outcome? 

Siloed service delivery might work for simple enquiries. But for more complex enquiries, siloed service delivery can frustrate and aggravate the customer, particularly if they feel that agents are lacking context for a specific calling customer. 

A more orchestrated, autonomous model means no customers falling through support gaps and being left without the right answer. With the multi-agent approach, every customer has the opportunity to speak to a ‘specialist’ and get the best advice for their specific enquiry, for the fastest resolution time. 

What mindset shift should the CX teams adopt to move from rule-based automation to AI-driven orchestration?

From my perspective, the biggest mindset shift is moving away from simply “brain dumping” scripts into a system. Instead, we should look at how we train human staff with set guidelines, rather than scripts, and let automation determine the best resolution from there. 

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