Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
As the demand for an improved and personalized customer experience grows, organizations are turning to AI to help bridge the gap.
Advancements in AI continue to pave the way for increased efficiency across the organization — particularly in customer service. Chatbots continue to be at the forefront of this change, but other technologies such as machine learning and interactive voice response systems create a new paradigm for what customers — and customer service agents — can expect. Not every piece of technology is right for every organization, but AI will be central 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. Businesses already use chatbots of varying complexity to handle routine questions such as delivery dates, balance owed, order status or anything else derived from internal systems. By transitioning these frequently asked questions to a chatbot, the customer service team can help more people and create a better experience overall — while cutting operational costs for the company.
In many modern omnichannel contact centers, agent assist technology uses AI to automatically interpret what the customer is asking, search knowledge articles and display them on the customer service agent’s screen while they’re on the call. The process can save time for the agent and the customer, and it can decrease average handle time, which also reduces cost.
Customer self-service refers to customers being able to identify and find the support they need without relying on a customer service agent. Most customers, when given the option, would prefer to solve issues on their own if given the proper tools and information. As AI becomes more advanced, self-service functions will become increasingly pervasive and allow customers the opportunity to solve concerns on their schedules.
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Robotic process automation (RPA) can automate many simple tasks that an agent used to perform. Automating bots to focus on updating records, managing incidents or providing proactive outreach to customers, for example, can drastically reduce costs and improve efficiency and processing time. One of the best ways to determine where RPA can assist in customer service is by asking the customer service agents. They can likely identify the processes that take the longest or have the most clicks between systems. Or they may suggest simple, repetitive transactions that don’t require a human. When prioritized and deployed correctly, this type of business process improvement can save customer service companies millions of dollars each year.
At its core, machine learning is key to processing and analyzing large data streams and determining what actionable insights there are. In customer service, machine learning can support agents with predictive analytics to identify common questions and responses. The technology can even catch things an agent may have missed in the communication. Additionally, machine learning can be used to help chatbots and other AI tools adapt to a given situation based on prior results and ultimately help customers solve problems through self-service.
Many customer service teams use natural language processing today in their customer experience or voice of the customer programs. By having the system transcribe interactions across phone, email, chat and SMS channels and then analyze the data for certain trends and themes, an agent can meet the customer’s needs more quickly. Previously, analyzing customer interactions was a lengthy process that often involved multiple teams and resources. Now, natural language processing eliminates these redundancies to create deeper and more efficient customer satisfaction.
While Interactive Voice Response (IVR) systems have been automating simple routing and transactions for decades, new, conversational IVR systems use AI to handle 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 turn to visual IVR systems via mobile applications to streamline organized menus and routine transactions. Blending many of these AI types together creates a harmony of intelligent automation.
Using sentiment analysis to analyze and identify how a customer feels is becoming commonplace in today’s customer service teams. Some tools can even recognize when a customer is upset and notify a team leader or representative to interject and de-escalate the situation. In conjunction with a voice of the customer tool, sentiment analysis can create a more honest and full picture of customer satisfaction. Vendors such as Brandwatch, Hootsuite, Lexalytics, NetBase, Sprout Social, Sysomos and Zoho offer sentiment analysis platforms that proactively review customer feedback.
As the COVID-19 pandemic forced employees into remote positions, many training teams began using AI to construct simulations to test employee aptitude for handling various situations. Previously, the training involved a blend of classroom training, self-paced learning and a final assessment — a routine that’s much harder to implement in remote or hybrid offices. With AI taking the role of the customer, new agents can test out dozens of possible scenarios and practice their responses with natural counterparts to ensure that they’re ready to support any issue a user or customer may have.
The practical applications for organizations and customer service teams are still a work in progress, but smart assistants such as Alexa, Google Assistant and Siri are an exciting avenue for personalized service. Customers appreciate and prefer when an organization communicates via their preferred platform, and for some people, that may be via their smart home device. Imagine a future where a user can bypass a phone call or email and troubleshoot any product or service concern via a simple question to their smart speaker. Simplified communications like this could be the difference between a satisfied or frustrated customer.
With several use cases for AI in customer service and many more to come, customer service teams must think more critically, handle higher-tiered issues and take advantage of all available tools to create an unforgettable customer experience.
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