Making the case for expert logic

Here's why artificial intelligence solutions won't transform your company's assisted service capability.

Read time 4min 40sec
Ryan Falkenberg.
Ryan Falkenberg.

As pressure mounts on call centres to offer improved customer experiences at reduced costs, many are turning to new technologies, particularly artificial intelligence (AI).

Many of these innovations are focused on making agents more efficient, but if AI's really going to change contact centres, the core role of the agent itself has to evolve.

To understand why transformation is preferable to optimisation, it's worth taking a look at some examples of the latter.

Intelligent learning systems

A key issue across all contact centres is the time it takes to upskill new agents. Anything that can reduce this time is welcomed. As a result, some contact centres are turning to AI technologies to improve the way their agents learn.

Ditching the more traditional prescribed curriculum and assessment approach, these technologies focus on flexibility and collaboration. They aim to put the learner first, predict what they want and need, and deliver it to them without prompting.

Because the materials adapt and respond to the learner, learning is faster and more effective.

Intelligent call routing

Other forms of AI focus on ensuring customers are routed to the agent most likely to be able to help them.

This is either done by using voice analytics to digitally validate the customer's need, or using customer data to predict their needs. Under this model, complex AI algorithms consider factors including training modules completed, previous customer ratings linked to that call type, current availability, and the number of similar calls they have fielded.

The view is that with an intelligent knowledge base, agents will receive whatever knowledge there is more easily and more intuitively than ever before.

The idea is that by matching the right agent to each call, the probability of a successful outcome will be increased.

Intelligent knowledge bases

Even the best trained contact centre agents are human. They will forget stuff from time to time. To make that less of a problem, a lot work is being put into technologies that can help them call up knowledge instantly.

Predictive algorithms, for instance, work out what an agent would find useful based on a wide range of data points, and then proactively offer the agent these options as they are on the call.

Virtual assistants, meanwhile, allow agents to type a request using natural language and for the assistant to help locate it for them, much like a dutiful librarian.

These new search capabilities are great, but they can't reflect the multi-dimensional nature of expert logic. They can describe one example at a time, but rely on the reader to make the interpretation.

That said, the view is that with an intelligent knowledge base, agents will receive whatever knowledge there is more easily and more intuitively than ever before.

Targeting the symptoms not the cause

In reality, we can improve agent training, call channelling and knowledge bases as much as we want, it won't fundamentally turn the dial. All these approaches depend on a capable agent ultimately answering the call. And that is where the core problem lies.

By expecting agents to know everything required to correctly answer customer queries in line with a prescribed formula, we immediately limit our ability to scale. People learn differently and at different speeds. This means any change to policies or products can be incredibly disruptive.

As a result, contact centres end up with over-specialised agents and struggle to juggle varying call volumes and customer query types. Energy is also wasted doing quality control on calls that have already been fielded and where the damage has already been done.

Unless we can find a way to completely rethink our approach to assisted service, our efforts to reduce costs and improve service levels will remain incremental at best.

Redefining the role of the agent

With the increase in digital self-service, customers will tend to utilise assisted service for a more empathetic, human facilitated experience.

To facilitate these kinds of empathetic experiences at scale, there needs to be a shift in focus from training and knowledge management to customer engagement. That means leveraging human capabilities like empathy and conversational ability, and digitally augmenting the aspects that limit people; namely their ability to learn and replicate complex customer journeys.

Just like a GPS helps you navigate through streets you have never seen before, so AI can navigate agents through conversations they have never had before. And if the customer changes tack, the GPS will instantly reroute the agent so they can focus their efforts on the conversation, not the content.

Shifting the content focus away from the human brain and into a digital brain ensures every customer engagement is handled in a consistent, compliant way.

Ultimately, this shift allows agents to become more human and less robotic, differentiating themselves from digital alternatives that might otherwise replace them.

The technology that allows us to create this kind of experience is here and operational at enterprise level. By fundamentally changing the role of the agent, contact centres can rapidly transform, not simply optimise their service offering.

Ryan Falkenberg
co-founder and co-CEO of CLEVVA

Ryan Falkenberg is co-founder and co-CEO of CLEVVA, a company that specialises in artificial intelligence for people. Prior to founding CLEVVA, he co-founded Hi-Performance Learning, which created new ways for staff to learn more in less than half the time. In 2014, Falkenberg co-founded CUDA Technology, a company that developed enterprise learning and knowledge management technologies. He and his brother and long-time business partner Dayne sold both HPL and CUDA to form their Human Capital business. The two partnered again to start CLEVVA in 2011.

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