In the enterprise, LLM-powered applications are customized to work in context of your business data and optimize your business process, in alignment with security and privacy requirements. The enterprise application of LLMs is revolutionary because it enables organizations to scale AI across vast amounts of information and business data to quickly surface insights, information and recommendations, and develop a draft response using context from historical interactions with the customer, such as recent purchases and preferences, to help compose responses in the context of an email or over a messaging channel like chat. Ultimately, it’s about getting to business insights and impact faster—and improving the customer and employee experience along the way.
The new LLM-powered customer service bot
LLMs give customer service chatbots incredible new and supercharged skills—natural language understanding, conversational user interface and the ability to quickly source and reason over internal and external information. In short, these new customer service bots are programmed to generate both answers and actions, making them more resourceful and useful.
The “copilot” era of generative AI represents a paradigm shift in chatbot programming. Previously, developers had to program a chatbot with both information and answers. The path from question to answer is pre-defined and limited by common customer questions and pre-determined answers. For example, “if the customer asks about business hours, provide X information.” Now, instead of pre-defined workflows, developers will design apps to connect to various sources of information and data for the chatbot to reference and create a contextual reply—such as the customer service handbook, the local weather, the scheduling calendar, the customer data platform and so on.
Consider the example of rebooking a cruise. Prior to generative AI, a chatbot might direct the customer to the company FAQ on rebooking policies and a link to the website for scheduling. An LLM-powered chatbot can recommend options for the customer by bringing in contextual insights about the trip, advise on dates with preferable weather and consult the schedule for availability and price ranges. This saves the customer significant time in researching new dates, weather, pricing and working with the cruise line to rebook over the phone, ultimately improving the customer experience.
LLMs will boost agent job satisfaction and efficiency
How does this new type of AI change the role of customer service agents if chatbots are able to handle a much higher case load? As LLM-powered bots enable more timely and satisfactory self-service, the most complex issues will still surface to agents, and this next-generation AI will help them resolve those faster, freeing them to focus on creating exceptional customer experiences, improving brand loyalty and creating space for more revenue-generating opportunities.
Customer service agents often need to navigate internal knowledge sources, team chats and external web sites to find the right answer for customers, but AI can quickly look across a variety of sources and synthesize a suggested answer for the agent. This can be especially powerful for new agents that have yet to learn where all the information is stored or which solution might be best. AI can also expedite an agent onramp to a new case by quickly summarizing the numerous data points and conversations that have already taken place. These summaries allow agents to save reading time and focus on resolution, resulting in an improved end customer experience.