A Maturity Assessment Framework for Conversational AI Development Platforms
This framework helps organizations select appropriate conversational AI platforms and guides researchers and developers in improving platform maturity, addressing a lack of systematic classification in the field.
The paper proposes a maturity assessment framework for conversational AI development platforms. This framework classifies platforms based on their ability to understand and respond to user inputs, derived from a systematic literature review of common and distinguishing features across various platforms.
Conversational Artificial Intelligence (AI) systems have recently sky-rocketed in popularity and are now used in many applications, from car assistants to customer support. The development of conversational AI systems is supported by a large variety of software platforms, all with similar goals, but different focus points and functionalities. A systematic foundation for classifying conversational AI platforms is currently lacking. We propose a framework for assessing the maturity level of conversational AI development platforms. Our framework is based on a systematic literature review, in which we extracted common and distinguishing features of various open-source and commercial (or in-house) platforms. Inspired by language reference frameworks, we identify different maturity levels that a conversational AI development platform may exhibit in understanding and responding to user inputs. Our framework can guide organizations in selecting a conversational AI development platform according to their needs, as well as helping researchers and platform developers improving the maturity of their platforms.