Gartner defines access management (AM) as tools that include authentication, authorization, single sign-on (SSO) and adaptive access capabilities for modern standards-based web applications, classic web applications and APIs. AM’s purpose is to give people (employees, consumers and other users) and machines access to protected applications in a streamlined and consistent way that enhances the user experience. For people, SSO is part of the enhanced experience. AM is also responsible for providing security controls to protect the user session during runtime. It enforces authentication and runtime authorization using adaptive access. Lastly, AM can provide identity context for other cybersecurity tools and reliant applications to enable identity-first security.

Gartner defines account-based marketing (ABM) platforms as software that enables B2B marketing and sales teams to run ABM programs at scale, including account discovery and selection, planning, engagement and reporting. ABM platforms enable the creation of target account lists by unifying first- and third-party data. In addition, they may engage audiences by activating channels such as display advertising, social advertising, email and sales engagement using a mix of native capabilities and integrations.

Advertising technology (ad tech) platforms help digital marketing leaders plan, buy and manage digital advertising campaigns across channels, including, but not limited to, display, video, streaming TV and audio, mobile, social media and search. They provide functions for campaign planning, media buying, advertising analysis and optimization and automation. Ad tech platforms can be used by buy-side and sell-side agents.

Gartner defines the adaptive project management and reporting (APMR) market as technologies that can support multiple delivery models to optimize project management practices and complex resource management needs across an organization. These tools promote continuous collaboration and unification of diverse and distributed teams. To support accelerating rates of change and continuous value delivery, these tools adapt to changing customer needs and governance approaches across multiple organizational designs and operating models. They provide multiple execution approaches that are grounded in value-based decision making and the time-to-value perceptions of their customers. Organizations need tools to support the integration of traditional development practices alongside agile, adaptive and hybrid ways of working while driving high levels of productivity from contributors and team members. The dynamic and complex multiple organizational operating model design of all organizations makes the correct governance approach imperative, yet difficult to achieve. To drive organizationwide outcomes, APMR tools need to support adaptive decision making without incurring additional bureaucracy.

Gartner defines adversarial exposure validation (AEV) as technologies that deliver consistent, continuous and automated evidence of the feasibility of an attack. These technologies confirm how potential attack techniques would successfully exploit an organization and circumvent prevention and detection security controls. They achieve this by performing attack scenarios and modeling or measuring the outcome to prove the existence and exploitability of exposures. AEV is generally delivered as a SaaS solution with or without on-premises agents. AEV as a market category replaces breach and attack simulation (BAS) and automated penetration testing and red teaming technology from the 2023 Gartner Hype Cycle (Hype Cycle for Security Operations, 2023). AEV technologies provide automated execution of both simplified and/or extensible attack scenarios. Results data from an executed attack scenario is used for various outcomes, such as: validating a theoretical exposure as real automating frequent controls testing improving preventive security posture improving detection and response capabilities.

Gartner defines AI code assistants as tools that generate and analyze software code and configuration. They use foundation models like LLMs, program-understanding technology, or both. Developers engage with these assistants to generate, analyze, debug, test, fix, refactor code, search dependencies, update libraries, create documentation, understand code, upgrade versions, translate languages and review commits. They help developers learn and explore codebases and access related information, such as frameworks and tools. AI code assistants integrate with developer environments, code editors, command-line terminals, chat interfaces, project management tools, monitoring, logging and deployment tools. Some are customized to an organization’s specific codebase and documentation. AI code assistants enhance software developers’ experience by boosting their efficiency, accelerating application development, minimizing cognitive overload, amplifying their problem-solving skills, enabling faster learning, fostering creativity and maintaining their state of flow.

Gartner defines the market of AI in communications service provider (CSP) customer and business operations as commercial off-the-shelf (COTS) products. They are either capabilities embedded in CSP-specific operational technology (OT) applications (such as channels, CRM and other business support system [BSS] applications) or industry-agnostic horizontal applications delivering AI/machine learning (ML)-based customer and business operations in CSPs. CSP customer and business operations refer to marketing, sales, customer acquisition, customer journey, billing and revenue management, revenue assurance, and related risk management. The scope of AI products covers data readiness, life cycle management of algorithms and their application to CSP customer and business operations. AI in CSP customer and business operations helps CSPs utilize AI/ML to generate insights and automate business processes across various customer and business operations areas. These areas include marketing, sales, configure-price-quote (CPQ), order management, product management, billing and revenue management, customer journey and care, revenue assurance, and fraud/risk management. Examples include intelligent chatbots utilizing natural language processing for customer interaction use cases to automate call center operations. These products assist with insights and automation and help CSPs manage the life cycle of AI/ML algorithms, gradually enabling ModelOps/AIOps.

AI Writing Assistant Software allows users to create original and high-quality content in different styles and tones. To create such content, it leverages machine learning and natural language processing (NLP) technologies, which provide suggestions based on previously entered material and perform writing-related jobs, including spell check, grammar check, text predictions, and plagiarism detection. In addition, the scope of content ranged from creating long-form content to short-form content, including blogs, landing pages, product descriptions, website headlines, and more. The typical user of the software are copywriters, content marketers, communication leaders, customer service agents, authors, journalists, bloggers, and other wordsmiths.