The contact centre of the future will anticipate a customer's inquiry, predict what they want to discuss and even provide appropriate support throughout the interaction, all thanks to artificial intelligence (AI)-- be they customers or contact centre agents -- to get more done in less time. Think of it as human-plus.
There are three roles AI plays in contact centres:
- Anticipating needs: using big data to predict customer needs
- Augmenting conversations: providing instant help through virtual assistants
- Automating where possible: freeing human agents to manage interactions where human-touch and expertise is needed
What Is AI?
The AI we refer to is soft AI. The tools seen in this type of AI give the impression of intelligence by drawing meaning from data. Such tools are already in use and apply the following techniques:
- Big data: finding patterns in large amounts of varied, fast moving data
- Natural language processing: analysing language as spoken and written by humans (such as in Amazon's Alexa)
- Machine learning: self-programming by adapting to changing circumstance and data
When combined, these tools take resources that previously were of little value - such as hours of call recordings - and draw out knowledge that would otherwise be lost.
AI Anticipating Customer Needs
It's late on a Saturday night in 2022. Lily is having car trouble and calls her roadside assistance service. Even before the call is answered, the contact centre's AI judges it to be urgent. It made that determination, in a faction of seconds, by acting on the context of the call:
- Caller-ID was associated with Lily's account
- Lily calling the rescue line for the first time, despite being a customer for 10 years
- Other customers with a similar profile to Lily call only when they really need help
The AI puts Lily at the top of the queue; it finds out how long she'll have to wait for help, informing the agent when they answer her call.
The AI used the context of Lily's call to judge its purpose and urgency, and then routed her call appropriately. While it's not a leap to assume someone calling late at night might need urgent help, non-obvious patterns will be revealed in both public and private sources of data. Machine-learning tools will then anticipate how best to respond when it sees those patterns unfolding.
AI Augmenting Conversations
The contact centre agent answers Lily's call. Lily explains that she is downtown in her home city.
As Lily speaks, the agent's screen updates with a map of the area where Lily is stranded, along with live locations of roadside assistance trucks nearby. When Lily says she thinks she needs to be towed, the nearest tow truck is highlighted. All of this happens without explicit instruction from the agent; a virtual assistant is listening to the call and uses natural language processing to identify key terms.
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