What is Generative AI for Customer Service

AI is a broad field with many different technologies in its landscape. Generative AI is a relatively new type of AI that can generate new content or data based on input content/data. The hyper-successful launch of ChatGPT has catapulted generative AI into boardrooms and living rooms alike. In fact, generative AI has reignited interest in the broader domain of knowledge management (KM), the #1 solution to improve CX, EX, and operating performance, per Gartner.

While earlier AI technologies were able to recognize patterns and make predictions, generative AI can create new content—text, audio, video, and so on, opening up exciting new possibilities for creativity, productivity, and business performance. It uses large language models (LLMs) to predict and create text, based on the data and content it has been trained on.

The use of AI for customer service is not new. It has been a key ingredient of the broader domain of knowledge management (KM) though some might consider them as two separate areas. As a KM pioneer, we have leveraged AI technologies such as ML for intent inference and case-based reasoning for conversational guidance in the customer service context, creating transformational business value for our clients. Generative AI is another exciting new ingredient that can be added to the KM recipe that will enhance various aspects of KM, which in turn will elevate the business functions KM serves—customer service, sales, helpdesk, HR, and more in the enterprise. On the flip side, robust KM is critical to the success of generative AI. In fact, if generative AI is not part of your KM strategy and tech stack, it will create yet another silo of inconsistent information, resulting in even more chaos for the business and the customer.

The KM process can be broken down into the following essential steps—create, curate, deliver, optimize. Generative AI can help automate or accelerate these steps to speed up “time to knowledge” and thus “time to business value.” Here are some early examples of use-cases pertinent to the KM process and for the customer service function:

• Just like anything else, customer service knowledge is GIGO (Garbage In Garbage Out). Content needs to be consumable, correct, and compliant with regulations and organizational best practices. Contact center agents and worse, customers find themselves having to read long documents, although they may have accurate and compliant content, to find answers. This results in poor CX and AX (agent experience). Generative AI can be used to create drafts of more consumable and findable content, using compliant and correct long-form documents as the reference sources
• Repurposing content, based on factors like the target personas, brand voice, interaction channels, and customer sentiment, to name a few
• Answering a customer question by compiling relevant information from multiple reference sources, which is what eGain’s Instant Answers feature does, leveraging GPT
• Automating the creation of a conversation summary for context retention when a contact center agent has to escalate a conversation to an SME
• Summarizing the voice of the customer from reams of customer feedback to optimize knowledge
• Translating content across languages, which is very relevant especially to multinational businesses

Integrated with a robust KM platform and with ongoing governance and KM best practices, generative AI can take business productivity and performance as well as user experiences to the next level in the customer contact center and beyond!