Getting Started with AI: What Should Customer Support Leaders Consider?

In this webinar, Luba Chudnovets from Cordless, Mervi Sepp Rei from Klaus, and Mathew Patterson from Help Scout got together to discuss AI in customer support. They share their experiences, highlighting the ups and downs of using AI in real-world situations.

We've summarised the key points for you below.

During the webinar, the speakers shared how AI can really help out customer support teams. They explained that AI can take care of the simple stuff, like answering basic questions or handling a lot of requests at once. This gives human team members more time to tackle the trickier problems that need a human touch. A key takeaway here is that AI isn't there to take over from humans. Instead, it's like a helpful sidekick, making the team's job easier.

It's thought by some that as AI gets better, we might not need as many human agents. But others see it differently - they think that AI might actually create new kinds of jobs.

Mat brought up the example of ATMs: when they were first introduced, people thought bank tellers might disappear, but instead new roles popped up.

Another concern that came up was about customers' interactions with support teams. If people are unsure whether they're talking to a bot or a person, they may not communicate in the same way or might feel deceived if they think they're talking to a person that later turns out to be AI. Of course, transparency in communicating with customers here is key.

Part of the discussion touched upon how AI potentially has the ability to improve the quality of customer service. Especially through faster response times and the handling of high volumes of requests with chatbots. However, the accuracy of responses made by AI should be very carefully monitored to ensure that customers are being advised correctly.

One of the most widely-used applications of AI in customer support is chatbots. And it's clear why they're so popular; chatbots are able to handle common customer queries, provide instant responses, and operate 24/7. The outcome of this is that the workload of human agents is reduced and customer satisfaction is improved through speed of service.

Call summaries might sound simple, but this is one of the most important applications of AI in customer support right now. Put simply: these are short descriptions of what was talked about on phone calls. They help managers understand what a call was about without needing to listen to a full recording. This is particularly useful when dealing with long conversations. Mathew even went as far as referring to call summaries as "gold", when trying to figure out what has gone on with a customer call and finding out that a summary is ready and waiting.

Manual quality assurance can be extremely time-consuming, and many CS leaders are only able to listen to a handful of calls per week. However, AI can be used to monitor and analyse 100% of customer interactions to ensure quality of service. This can include monitoring call and chat transcripts for script adherence and identifying areas for improvement. This can help free up hours of time per week for managers.

Sentiment analysis is a way to figure out how customers feel about something based on what they say. AI can help understand customer feelings by looking at what they say in their communications and identifying whether they feel positive, neutral or negative about specific aspects of a company's support, services or products. This then allows companies to focus their efforts on the most pressing areas for improvement, based on customer needs.