Contact Center Pipeline May 2023 | Page 16

GENERATIVE AI

ILLUSTRATION PROVIDED BY ADOBE STOCK

THE AI DUO CONTACT CENTERS NEED HOW GENERATIVE AI AND CONVERSATIONAL AI IMPROVES THE CX .

BY HARDY MYERS , COGNIGY

There has been much chatter recently about Generative AI . So , let ’ s deep dive into it and discover how it can help contact centers . Generative AI is a type of artificial intelligence ( AI ) that focuses on creating new content or output based on an understanding of data inputs and patterns , referred to as “ prompts .” Unlike traditional AI systems that follow predefined rules and patterns , Generative AI uses deep learning algorithms to analyze existing data and generate new outputs that are not limited to existing data .

16 CONTACT CENTER PIPELINE
The outputs generated by Generative AI can be in various forms , including text , images , audio , and other types of media . This makes Generative AI a valuable tool for a wide range of applications , including content creation , product design , and customer service .
In the context of the contact center , Generative AI is excellent at processing and generating human-like text in response to a wide range of questions and prompts .
Specifically , it excels at natural language processing ( NLP ), meaning it can understand and interpret human language . This can improve the efficiency and accuracy of customer support and can also improve overall customer experience ( CX ).
In a world where improving CX is critical for building and maintaining brands , companies are actively seeking solutions that will help them establish next-generation service levels . As we will see , Generative AI , combined with an enterprise Conversational AI platform ( ECAIP ), has the potential to deliver enhanced customer service in ways never seen before .
IN A NUTSHELL : HOW GENERATIVE AI WORKS
Generative AI works by analyzing existing data patterns and using deep learning algorithms such as Generative Adversarial Networks ( GANs ) to generate new outputs . The Generative AI system is trained on large amounts of data and , as it analyzes the data , it learns the patterns and relationships within the data .