Technology Record - Issue 27: Winter 2022

76 I NT E R V I EW AI-powered assistance Anywhere365’s Enrico Karsten explains how artificial intelligence and machine learning empower contact centre agents with the real-time information, dialogue flows and other capabilities they need to better serve customers Almost everyone has experienced the frustration of contacting a customer service department for help, only to be forced to wait in a long queue, answer the same basic questions multiple times and then discover that the person they are speaking with doesn’t have the information to be able to assist them anyway. “The biggest challenge for any contact centre agent is being able to quickly and confidently provide the most helpful answer to the customer the first time they ask the question,” says Enrico Karsten, CEO of cloud-based contact centre and dialogue management platform provider Anywhere365. “Often, agents don’t have the in-depth knowledge to resolve a specific query – and they don’t know where to find the contextual information they need to help them, so the process becomes unnecessarily long and arduous for both the agent and the customer. This can be detrimental to organisations as just one bad experience can prompt customers to go elsewhere and never return.” According to Karsten, there are two key technologies that can help businesses to improve and expedite the customer service experience: artificial intelligence and machine learning. “AI can be used to analyse interactions and pinpoint optimal responses, as well as to deliver detailed contextual information to agents in real time so they can complete their interactions as quickly as possible with the highest success rate,” he says. In addition, AI can be used to perform sentiment analysis to help agents understand how their behaviour might be impacting the customer experience. “Agents can usually recognise when a customer is becoming frustrated, but they don’t always know which actions to take or what to say to quickly de-escalate and resolve the situation,” says Karsten. “But if an AI engine is continually analysing customer sentiment and sharing Net Promoter Scores (NPS) with the agent in real time, they can easily gauge how well they’re performing and whether their answers are satisfying the customer. Plus, they can follow the AI-generated suggestions to improve the interaction and achieve a better outcome.” By implementing machine learning technology, enterprises can eventually create recommended dialogue flows for agents to follow in specific scenarios. “If, for example, 20 agents have all answered the same question from different customers, we can use AI to analyse their responses and determine which one had the highest NPS score and success rate,” says Karsten. “We can look at what the agent said, what information they referred to when looking for the answer and where they found it within the organisation. From there, we can use machine BY R E B E CCA G I B SON “ The AI and machine learning tools will enable the bot and the human agent to work together in harmony”