Customer Service AI to Deliver Agent Support
Here’s a contact center secret that will always be true: Customers want to speak to a human being. Think about it. When you call up an organization’s phone lines, do you want to be confronted with a chatbot, or would you rather be routed to a human agent who can understand your issues first time, and advocate for you?
The answer is obvious. People want other people. They want to feel genuinely understood and empathized with. No matter how advanced your customer service AI, if the customer wants to speak to a human, there’s very little you can do to change their mind.
So if customer service AI is so unpopular, how do you leverage its massive potential without alienating your customer base?
- No use hiding it. Though the regulatory landscape is still in flux, it seems likely that businesses will be forced to indicate when and where a customer is speaking to a human, and when they are speaking to a chatbot, or a voice bot. That is to say, if you’re using AI in a customer-facing way, your customers are going to know.
- Unless, of course, it’s not the customer that interacts with the AI. AI powered solutions, such as Content Guru’s Agent Assist, leverage customer service AI to deliver higher-quality, more efficient experiences through the person on the agent, rather than removing them from the equation entirely. The customer gets to speak to a human; that human gets to leverage AI.
- Customer service AI can support agents in a range of key ways: It can transcribe and summarize interactions, pre-filling post-call forms. It can offer real-time script suggestions, drawing on comprehensive knowledge articles. It can provide detailed agent feedback, drawing on customer sentiment and empathy analysis.
Customer service AI will transform the contact center. That transformation begins with Agent Assist. But that’s not to say that there aren’t customer-facing AI applications.
Customer Service AI Structures Customer Interactions
Your customers have a certain impression of what ‘customer service AI’ means. Usually, they’re thinking of fully-automated chatbots, i.e. ChatGPT. But that’s not often the case. Usually, the most reliable customer service AI applications are the ones that seem the most simple.
From automated, intelligent routing, to proactive contact, to demand prediction and scheduling; these are AI applications that don’t necessarily involve the flashiest new generative AI technology. But, they still deliver exceptional value.
- Most delays in the customer journey come from customers who have been incorrectly routed. Either they don’t know how to find the best available outcome, or the business’ system actively prevents them from reaching it. In situations like these, intelligent routing is a must.
- Voice recognition and Natural Language Processing technologies can identify and process customer intent automatically, when spoken out loud or typed into a chatbot. From here, the AI can make routing decisions, ensuring that the customer reaches the best available outcome, first time. This spends valuable time otherwise used to re-route customers, lock them into queues, and generally cause them frustration.
- During an interaction, AI-powered sentiment analysis can provide a real-time picture of how the customer is feeling. Combined with agent empathy assessment, you can oversee every interaction in the highest level of detail. Not only is this valuable for boosting contact center performance; you can use sentiment data to plan proactive contact, working to address flagging customer loyalty before it leads to defection.
Customer service AI is an incredibly powerful tool, but rushing blindly into an AI transformation is a recipe for disaster. With so many businesses trying to make a quick buck off AI hype, navigating the AI space is an acute challenge.
Picking the Right Customer Service AI
The AI space is incredibly volatile. Such is the way with all new technologies. Several major companies (OpenAI, Microsoft, Meta, Google, etc.) lead the innovation, whilst a range of start-ups flock around their skirts, trying to apply their AI to everything from marketing copy to movies. These start-ups flit in and out of existence like mayflies, but even the titans of the space are at risk of losing their position.
The announcement of a new model or innovation can send one provider skyrocketing above the others, whereas a botched AI model can see a provider trail miserably behind. The point is, providers ‘leap-frog’ each other. Right now, the race is too close to call. Every provider has different strengths and weaknesses. So how do you pick between them?
- In a market landscape like this, every provider will be trying to tie you down; to lock you in as a customer before the competition can. That means there are incentives not to offer too much flexibility; they don’t want you to skip to another provider when the landscape shifts.
- Obviously, this is disadvantageous. If you tie yourself to the wrong provider, you’ll end up paying the price of their failure. And at this stage, there’s no way to know what the ‘right and ‘wrong’ providers are.
- Ideally, you need an omni-AI approach. In essence, this means that you bring together AI solutions from across different vendors, according to need. It means you leverage the knowledge of AI veterans and experts to build a solution that works for you and isn’t tied to the success or failure of an individual provider.
But how do you take an omni-AI approach? You need to the right AI partner.
