A Maturity Assessment Framework for Conversational AI Development Platforms

Abstract

Conversational Artificial Intelligence (AI) systems have recently skyrocketed in popularity and are now used in many applications, from car assistants to customer support. The development of such systems is supported by a large variety of conversational AI platforms—all with similar goals, but different focus points and functionalities. Unfortunately, a systematic foundation for classifying conversational AI platforms is currently lacking. In this thesis, we propose a framework for assessing the maturity level of conversational AI platforms. Our framework is based on a systematic literature review, in which we extracted common and distinguishing concepts (called features) of various open-source and commercial in-house platforms. Inspired by language reference frameworks, we identify different maturity levels a conversational AI platform may exhibit in understanding and responding to user inputs. Our framework can guide organizations in selecting a conversational AI platform according to their needs, and platform developers in improving the maturity of their platforms.