it's a mistake to theorize before you have data. Historically, however, the extraction of information from volumes of data has been difficult—especially when it's unstructured. AI changes the game by automating the process of pulling specific details from customer support tickets, chat transcripts, conversation sentiment or surveys to surface vital but buried information.Generative AI can be trained to scan immense data stores and distill them into concise summaries in seconds.
AI can be trained to proactively suggest replies, resources and next-best steps when building journeys and workflows. Teams can vet and tweak them and then pass them to end users, eliminating time spent searching through help articles or manuals. Eventually, as AI models are trained on additional data like email and chat conversations, they can augment prewritten replies with suggested customizations, including modifications according to each communication channel.This is the use case that most people are familiar with, thanks to applications like ChatGPT. As AI models prove consistent performance, they can gradually take on some end-user interactions to free up human resources for more complex troubleshooting and support.
By extracting this Q&A, it can be used to populate a knowledge library that trains the AI to generate responses and eliminates the need to write help articles from scratch. As AI has more information to work with, it can generate more relevant responses.