For businesses, customers are the number one priority. As a customer, when you contact customer service for help, you expect a resolution quickly, and without too much effort on your part. The ultimate aim of every customer contact centre should be to provide fast and reliable solutions so that customers are left feeling satisfied. Here is where AI comes in handy.

A master of Conversational AI and bots can build automated conversations that feel like human interaction — personal, intuitive, and contextual. Smart AI doesn’t make customers feel like they have to repeat themselves. It can juggle multiple ideas at once, it can adapt to changing topics of conversation, and it doesn’t dump you off to someone else the moment it misunderstands you. 

Most importantly, customers don’t feel like they’re chatting to a cold-hearted bot. 

In turn, the more conversations you can handle with chatbots, the more you can reserve agent time for when a human is truly required, so the race is on to have the best automated customer service.

How can we humanise automated customer service? 

Efficiency metrics won’t improve just because you add chatbots. Conversational AI is an art that must be mastered. Here are three things all masters do:

  1. React less like a machine

Masters of Conversational AI recognise cues and flow with the rhythm of dialog. If a chatbot fails to recognise the correct intent the first time and responds inappropriately, the experience is negative and repels customers away from your business — a net loss for your bottom line and your brand reputation.

Conversational AI only works when it can understand variations in how different people communicate. Some provide all details of their intent at once, while others start general and require more information to know which solution is right. Some hit Send after typing one long, thought-out paragraph, while others type a series of sentence fragments that must be assembled to understand a single intent. Some might even change their mind midway through the conversation and decide they want something else instead.

Regardless of how a customer behaves, chatbots need to respond more elegantly than to treat each variation as an error that gets escalated to an agent.

2. Treat AI as a growth experience

Masters of Conversational AI commit to a path of continuous improvement. AI is not Set and Forget or One and Done. It must learn from its mistakes just like people do. New cues are coming at your chatbots every day, so it’s crucial that businesses are able to test and improve their automated responses after go-live using the data that they’re accumulating.

Generic AI models maintained by third parties boast regular improvements, but only a business-specific model can get to know how customers ask for help with products in the languages the business is done in.

Conversational AI requires human vigilance and governance in order to be truly successful. Engage in regular testing and training to ensure that chatbots are bringing optimal value.

3. Know when not to automate

Masters of Conversational AI know when Containment Rate is not an indicator of success. For example, in financial services or banking, most chatbot conversations will escalate to a customer service agent, so the chatbot’s goal isn’t complete automation. The goal becomes gathering data and intent so the agent can pick up where the chatbot left off with full context.

In this scenario, Conversational AI might only have a 20% Containment Rate, meaning 80% of chatbot conversations escalate to agents. By traditional accounts, that’s a fail. But it’s a win for being able to route to the right agent who has the context needed to deliver a fast, personalized human experience.

No two strategies are the same when it comes to mastering Conversational AI. Customer use cases will drive which role chatbots play in the business. Businesses who are able to master the art of conversational AI will know that they need to be on channels that customers prefer with correct data, real-time insights, and AI-optimised responses. The key is in understanding how AI is used to make sense of unstructured data to automate intuitive conversations at scale.

Christoph Jourdan is vice president of growth markets solutions consulting at Sprinklr.