Gartner defines the CRM customer engagement center (CEC) as a cohesive set of software built around core case management tools, dedicated to providing customer service and support by engaging with customers, and intelligently orchestrating the processes, data, systems, and resources of an organization. CRM CEC is a key software platform used in delivering end-to-end customer service and support experiences. It enables customers to engage and interact with an organization as well as for the organization to orchestrate its internal and external processes and resources to fulfill the customer’s expected outcome.
Workforce engagement management (WEM) software is a collection of technologies that help manage the customer service workforce to ensure a high level of operational performance while elevating employee well-being, discretionary effort and satisfaction. The core capabilities of WEM products include: • Evaluation and improvement • Time management • Metrics and recognition (that is, performance management) • Assistance and task management • Voice of the employee (VoE) feedback • Recruitment and onboarding
Conversation analytics platforms enable service departments to extract insights by processing natural language interactions between customers and the organization. Conversation analytics platforms focus on voice and text interactions, whether postcontact or real-time, by telephone, chatbot, virtual assistant, live chat, messaging, and email channels. Extracting insights from natural language customer interactions is crucial for optimizing customer service. Conversation analytics allows customer service leaders to identify opportunities to improve customer and agent experience. It helps track process improvements, customer sentiment and service department compliance with regulatory and quality assurance guidelines; reduces the cost of agent training; and provides agent assistance to improve productivity.
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.