AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals based on logic and reasoning. AI agents offer the promise to create systems for complex workflows, deliver on goals and learn from outcomes. They can improve marketing’s ability to meet customer expectations, with reasoning, learning and context. AI agency exists on a spectrum, ranging from current systems that act on user guidance to future systems that learn and perform tasks autonomously.
Gartner defines conversational AI platforms (CAIPs) as SaaS products that primarily enable the development of applications simulating human conversation across multiple channels and media. CAIPs leverage composite AI, including generative AI (GenAI) and natural language technologies. Conversations can use a mix of modalities such as text, voice and visual content. To support the building of conversational applications, platforms provide extensive coding options, from pro-code to no-code. Application areas include chatbots, virtual assistants (VAs) and conversational AI (CAI) agents. CAIPs are used to create, deploy and manage AI-driven conversational interfaces. These platforms enable businesses to develop VAs and conversational AI Agents that facilitate both customer-facing and internal interactions through pro-code/low-code/no-code tools. CAIPs empower businesses to centralize and democratize the development and management of multiple, diverse CAI initiatives, leading to more cohesive and efficient operations. The blend of capabilities provided by CAIPs is distinctive compared to those offered by other CAI solutions, such as targeted extensions for CAI found in other enterprise applications (e.g., CRM systems, contact center platforms) or stand-alone GenAI-native apps. In comparison, CAIPs are a better fit for strategic and scalable enterprise-grade CAI adoption.
A data and analytics governance platform is a set of integrated business and technology capabilities that help business leaders and users develop and manage a diverse set of governance policies and enforce those policies across business and data management systems. These platforms are unique from data management in that data management focuses on policy execution, whereas D&A platforms are used primarily by business roles — not only or even specifically IT roles — for policy management. Data and analytics (D&A) leaders who are investing in operationalizing and automating the work of D&A governance should evaluate this market. The work of D&A governance primarily includes policy setting and policy enforcement, and collaborates with data management (policy execution). Use cases are employed across numerous governance policy categories and multiple business scenarios and asset types (data, KPIs, analytics models). The intersection of use-case/business scenarios, policy categories and assets to be governed is then used to identify the technology capability. These capabilities may share similar names across policy categories, but may not mean the same thing, or may be used differently by various governance personas. For example, data classification in a data security implementation would be quite different from a data classification effort for creating trust models, which would be based on lineage and curation.
Gartner defines enterprise AI search as platforms that enable retrieval and synthesis of information across enterprise repositories. They are a key technology for developing AI assistants and AI agents that scale to enterprise needs using retrieval-augmented generation (RAG). They integrate with a wide range of advanced natural language processing (NLP), machine learning (ML) and large language model (LLM) technologies that are essential to knowledge management processes. They are designed to be customized and tuned for specific domains but often come with prepackaged integrations and experiences for some enterprise applications. Enterprise AI search tools are pivotal tools for humans and machines that need to find information and synthesize it to derive insight, so they can subsequently make decisions and take actions. These platforms connect to a wide variety of data sources, normalize and classify information, index it, and match and rank the most relevant results. Their user experiences are commonly customized and are increasingly used as a platform for building AI assistants for a wide variety of operational use cases. Those building RAG-based systems should consider how to configure enterprise search platforms to deliver AI assistants and, in the future, AI agents.
Enterprise search engines are specialized search tools designed to help organizations index, search, and retrieve information stored within their internal data repositories. Unlike general web search engines that index and search the entire internet, enterprise search engines focus on the internal data of an organization, which can include documents, emails, databases, intranet sites, and other digital assets or data sources. Modern enterprise search engines often incorporate Natural Language Processing (NLP) and Machine Learning (ML) and AI-powered technologies to enhance their capabilities and improve the search experience. This type of search engine is adept at handling both structured and unstructured data, making it invaluable for diverse use cases such as knowledge management, customer support, and business intelligence. By integrating these enterprise search software capabilities, organizations can ensure that employees have quick and relevant access to the information they need, thereby improving productivity and decision-making.
Gartner defines the generative AI (GenAI) knowledge management apps/general productivity submarket as technologies that enable companies to better retrieve and contextualize information and insight from their knowledge bases, including enterprise AI search, conversational AI platforms, and productivity tools for communications and content development.
Knowledge Management (KM) Software helps organizations centralize, organize, and share information efficiently across teams. It provides a centralized repository for storing diverse content types—such as documents, presentations, and multimedia—making knowledge easily accessible and searchable. A robust search functionality ensures quick retrieval of relevant information, while features like file version history, access control, and content editing enhance collaboration and governance. These capabilities reduce duplication of effort, preserve institutional knowledge, and streamline workflows. KM software is widely used by customer support, product and operations, HR and training, and IT and compliance teams—any function that depends on consistent, accurate, and easily retrievable information.