A Maturity Assessment Framework for Conversational AI Development Platforms

TL;DR

This paper introduces a systematic framework for assessing the maturity of conversational AI development platforms, aiding organizations and developers in evaluation and improvement.

Contribution

It presents a novel maturity assessment framework based on literature review, classifying platforms into levels of understanding and response capabilities.

Findings

Framework differentiates platform maturity levels.

Guides platform selection based on organizational needs.

Assists in improving platform capabilities.

Abstract

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.