Answer Questions:
Not all intents can be predicted. Generative AI can pull information from an index, such as company websites, employee resources, internal spreadsheets, etc. to generate answers to unexpected questions. The search is restricted to approved sources, so answers won’t include any surprises.
Understand Customer Intent:
Customers contact a business for many reasons. People phrase each of these “intents” up to 100 different ways. So If a virtual agent responds to 50 intents, engineers must program up to 5,000 variant phrases. Generative AI creates and tests phrases automatically, reducing workload and error.
Identify Variant Entities:
“Entities” are data points like dates, times, monetary values, and locations. Virtual agents expect specific responses, but people still provide variations. For example, “Los Angeles,” “Los Angeles International,” “LA,” and “LAX” all describe the same airport. Generative AI lists variants that engineers may miss.
Automate Testing:
Engineers familiar with a system often struggle to create hard tests. Generative AI can initiate conversations with a virtual agent, pretending to be a customer who is angry, anxious, verbose, blunt, and more as prompted. These test conversations are easy to read and discover where virtual agents succeed and fail.
Carry Conversations:
IVAs (intelligent virtual agents) already carry out natural-sounding conversations with customers, but every variant intent and entity must be programmed before that’s possible. Through text, customers can comfortably communicate with generative AI without fixed dialogue and with less backend training.
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