Gartner defines AI applications in IT service management as tools that augment and enhance IT service management (ITSM) workflows using AI. These analyze ITSM data and metadata (primarily found in ITSM platforms) to provide intelligent advice and actions on ITSM practices and workflows, such as IT service desk and support activities. This software can either be a stand-alone product, features extending an ITSM platform or an add-on to an ITSM platform. AI features enable I&O teams to optimize IT support and service management processes (such as incident and problem management) through insight and automation. This can lead to tangible reduction in costs, such as labor savings by handling support issues and requests automatically, faster resolutions, and improved accuracy in triage, categorization and expert identification. In addition to addressing overheads, AI solutions can improve the employee-facing user experience and enhance IT’s relationship with the business consumer. Some features, such as intelligent risk advisory, can help I&O leaders reduce disruptions and provide reliable IT services.
'Application integration platforms enable independently designed applications, apps and services to work together. Key capabilities of application integration technologies include: • Communication functionality that reliably moves messages/data among endpoints. • Support for fundamental web and web services standards. • Functionality that dynamically binds consumer and provider endpoints. • Message validation, mapping, transformation and enrichment. • Orchestration. • Support for multiple interaction patterns, content-based routing and typed messages.
Gartner defines business orchestration and automation technologies (BOAT) as a consolidated software platform that delivers enterprise process automation by enabling capabilities including orchestration of business processes, enterprise connectivity, low code development and agentic automation. A BOAT platform includes a cross section of certain capabilities from different markets such as business process automation (BPA), low-code application platforms (LCAP), integration platform as a service (iPaaS), intelligent document processing (IDP), robotic process automation (RPA), collaborative workflow management and document management. However, this list is not necessarily all-encompassing.
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.
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.
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.
Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
Gartner defines integration platform as a service (iPaaS) as a vendor-managed cloud service that enables end users to implement integrations between applications, services and data sources, both internal and external to their organization. iPaaS enables end users of the platform to integrate a variety of internal and external applications, services and data sources for at least one of the three main patterns of integration technology use: data consistency, multistep process and composite services. These integration use cases are most commonly implemented via intuitive low-code or no-code developer environments, though some vendors provide more complex developer tooling.
Gartner defines intelligent document processing (IDP) solutions as specialized data integration tools that enable automated extraction of data from multiple formats and various layouts of document content. IDP solutions ingest data for dependent applications and workflows and can be provided as a software product and/or as a service. Organizations receive and process documents in multiple formats to enable activities such as onboarding new suppliers, receiving applications for loans or insurance claims. This results in large volumes of documents, the content of which is designed for human comprehension rather than machine processing. Extracting data from content is essential for document processing and the automated activities this supports. IDP solutions fulfill this role, augmented by and potentially replacing people. Documents are received in physical form, typically paper, which must be scanned for digitization, or in digital form, such as emails and PDFs. The content of these documents has varying layouts, ranging from structured formats, such as tabular or outline (e.g., list or hierarchy of headings) or invoices or contracts, to unstructured formats (i.e., free-flowing, such as an email). Layouts that fall between structured and unstructured, or mixing the two, are often referred to as semistructured.
Gartner defines robotic process automation (RPA) as software that automates tasks within business and IT processes using software scripts that emulate human interaction with the application UI. RPA enables a manual task to be recorded or programmed into a software script, which users can develop through programming or by using the RPA platform’s low-code and no-code GUIs. This script can then be deployed and executed into different runtimes. The runtime executable of the deployed script is referred to as a bot or robot.
Gartner defines task mining as a combination of techniques to infer useful information from low-level event data available in UI logs derived from the underlying operating system or through observing application UI interactions. This data comes from individual users or a cohort in the form of screen recordings, keystrokes, mouse clicks and data entries. Additional mining capabilities interpret the data by applying natural language processing (NLP), optical character recognition (OCR) and artificial intelligence (AI) techniques to correlate data in different ways. Task mining helps an enterprise identify inefficiencies and automation opportunities, increase worker productivity, and enhance the employee experience.