One of the most common uses of AI in customer service is customer service chatbots. Companies use chatbots for a variety of reasons, the main one being the automation of customer service interactions. Support teams use chatbots to automate the most repetitive and redundant customer service inquiries. Artificial intelligence as a service (AIaaS) is a variety of artificial intelligence tools (often APIs).
In this case, third-party vendors offer these tools through ready-to-use solutions. AIaaS allows companies to adopt and implement artificial intelligence solutions without significant investment and with lower risk. Advances in AI continue to pave the way for increasing efficiency across the organization, especially in customer service. Chatbots are still at the forefront of this change, but other technologies, such as machine learning and interactive voice response systems, create a new paradigm of what customers (and customer service agents) can expect.
Not every technology is right for every organization, but AI will be critical to the future of customer service. Here are 10 examples of the future of AI in customer service. One of the most common uses of AI in customer service is chatbots. Companies are already using chatbots of varying complexity to manage routine issues, such as delivery dates, balance due, order status or any other issue arising from internal systems.
By turning these FAQs into a chatbot, the customer service team can help more people and create a better overall experience, while reducing operating costs for the company. In many modern omnichannel contact centers, agent assistance technology uses artificial intelligence to automatically interpret what the customer asks, search for informational articles and display them on the customer service agent's screen during the call. The process allows the agent and customer to save time and reduce average service time, which also reduces costs. Self-service customer care refers to customers being able to identify and find the assistance they need without depending on a customer service agent.
Most customers, when given the choice, would prefer to resolve issues on their own if they were provided with the right tools and information. As AI advances, self-service functions will become more and more widespread, offering customers the opportunity to answer their questions on their schedules. In essence, machine learning is key to processing and analyzing large data flows and determining what actionable information exists. In customer service, machine learning can help agents use predictive analytics to identify common questions and answers.
Technology can even detect things that an agent may have missed in communication. In addition, machine learning can be used to help chatbots and other artificial intelligence tools adapt to a given situation based on previous results and, ultimately, help customers solve problems through self-service. Today, many customer service teams use natural language processing in their customer experience or customer voice programs. By having the system transcribe interactions in phone, email, chat and SMS channels and then analyze the data based on certain trends and topics, an agent can meet customer needs more quickly.
Previously, analyzing customer interactions was a lengthy process, often involving multiple teams and resources. Now, natural language processing eliminates these redundancies for deeper and more efficient customer satisfaction. While interactive voice response (IVR) systems have been automating routing and simple transactions for decades, new conversational IVR systems use artificial intelligence to manage tasks. Everything from verifying users with voice biometrics to directly telling the IVR system what needs to happen with the help of natural language processing, is simplifying the customer experience.
Some companies use visual IVR systems through mobile applications to simplify organized menus and routine transactions. The combination of many of these types of AI creates a harmony of intelligent automation. When the COVID-19 pandemic forced employees to occupy remote positions, many training teams began using artificial intelligence to create simulations that tested employees' ability to manage various situations. Previously, training consisted of a combination of face-to-face training, self-paced learning and a final evaluation, a routine that is much more difficult to implement in remote offices or hybrids.
With AI assuming the role of the customer, new agents can test dozens of possible scenarios and practice their responses with their natural counterparts to ensure that they are prepared to address any problem a user or customer may have.
