Gartner defines conversational AI platforms (CAIPs) as platforms primarily used for developing applications simulating human conversation across multiple channels and on a mix of modalities such as text, voice and visual content. CAIPs leverage a composition of AI techniques, including classic natural language processing (NLP), and generative AI (GenAI) and agentic AI architectures. To support the building of conversational applications, CAIPs principally provide low-code and no-code coding options. Application areas include AI assistants and conversational AI agents. Conversational AI platforms are designed to address the increasing demand for organizations to efficiently build, deploy and manage AI-driven conversational systems at scale, addressing the requirements of both employee experience and customer experience use cases. While they may offer some predefined AI assistants or AI agents that can be modified, the primary focus of a CAIP is to equip organizations with tools for building customized conversational AI applications. CAIPs typically embed specialized, dedicated and feature-rich tooling for language-specific NLP and multimodal interactions, as well as conversational flow building and analytics. By offering a unified environment that supports low-code and no-code development — and, in some cases, extends to pro-code and GenAI-assisted options — CAIPs empower technically savvy business users, including citizen developers, to create and orchestrate both customer-facing and internal AI assistants and conversational agents. Unlike AI engineering environments, which primarily serve AI engineers, software developers and data scientists, CAIPs are purpose-built for broad enterprise adoption, enabling strategic, scalable and organizationwide conversational AI initiatives.
Customer data platforms (CDPs) are software applications that support customer experience use cases by unifying a company’s customer data from marketing, sales, service, commerce and other sources. CDPs unify customer data to facilitate its output to coordinate profiles between cross-functional systems, create segments and/or audience targets, optimize offers and/or decisions, and inform analysis while distributing insights that create triggers for other experiences.
Emotion AI, also known as Affective AI, refers to the area of artificial intelligence where systems are designed to recognize, interpret, process and simulate human emotions. The goal is to allow machines to understand and respond to human emotions in a way that feels more intuitive and human-like. The techniques used in Emotion AI include facial expression analysis, voice tone analysis, physiological measurement (e.g., heart rate variability), and natural language processing. Integration with other systems like CRM and virtual assistants also facilitates emotionally-aware interactions. Using these various techniques to analyze emotions in real time has spawned new use cases for customer experience enhancements, employee wellness and many other areas, including entertainment, healthcare, automotive, retail and advertising and education.