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.
Marketing refers to the products and services that enable organizations to plan, execute, measure, and optimize strategies for attracting, engaging, and retaining customers across digital and physical channels. This category includes markets that support content creation, campaign management, data-driven personalization, performance analytics and brand strategy—empowering businesses to deliver targeted, measurable, and customer-centric marketing experiences.
Gartner defines multichannel marketing hubs (MMHs) as software applications, primarily delivered as SaaS, that orchestrate personalized campaigns and event-driven customer journeys across marketing channels. These applications leverage customer data, predictive models and real-time insights to optimize the timing, channel and content of interactions. MMHs apply advanced analytics, AI and prescriptive intelligence to help marketing and technical teams manage the end-to-end life cycle of customer journeys. Although MMHs overlap with customer data platforms (CDPs) and personalization engines, their primary focus is enabling marketing users to manage large-scale consumer interactions, particularly in owned media channels such as email and app push. Multichannel marketing hubs empower marketers to deliver personalized media and orchestrate customer journeys, thus driving revenue, engagement and loyalty. These SaaS applications unify customer data, predictive insights and real-time decision making to optimize interactions across digital channels. MMHs enable multidisciplinary teams to manage campaigns and event-driven journeys via advanced analytics, artificial intelligence/machine learning (AI/ML) and prescriptive intelligence.