Tools & Platforms
AI agents & data readiness top Gartner’s 2025 tech priorities
Gartner’s latest Hype Cycle for Artificial Intelligence identifies AI agents and AI-ready data as the most rapidly advancing technologies of 2025, reflecting a shift from generative AI to foundational enablers of sustainable AI deployment.
Focus on operational scalability
According to research by Gartner, interest and activity have concentrated on technologies providing operational scalability and real-time intelligence for businesses. This is driving organisations to prioritise enablers supporting long-term AI success rather than focusing solely on generative AI technologies.
Haritha Khandabattu, Senior Director Analyst at Gartner, said, “With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence. This has led to a gradual pivot from generative AI (GenAI) as a central focus, toward the foundational enablers that support sustainable AI delivery, such as AI-ready data and AI agents.”
AI agents and adoption challenges
AI agents are defined as autonomous or semi-autonomous software entities that leverage artificial intelligence to perceive, decide, and act within digital or physical contexts. Many organisations are now using these agents to complete complex tasks, often utilising large language models and related practices.
“To reap the benefits of AI agents, organisations need to determine the most relevant business contexts and use cases, which is challenging given no AI agent is the same and every situation is different,” said Khandabattu. “Although AI agents will continue to become more powerful, they can’t be used in every case, so use will largely depend on the requirements of the situation at hand.”
AI-ready data strategies
Another central theme identified by Gartner concerns AI-ready data. This refers to datasets specifically optimised for AI usage, in order to enhance both accuracy and efficiency. The determination of data readiness is context-specific, dependent on both the intended AI use case and the technique applied. As a result, businesses are reconsidering their data management approaches.
Gartner’s analysis suggests that organisations scaling up AI must adapt their existing data management frameworks. This is necessary not only to meet evolving business needs but also to ensure trustworthiness, reduce bias, safeguard intellectual property, and minimise risk and compliance problems.
Multimodal AI developments
Multimodal AI models, which integrate and process various forms of data such as text, images, audio, and video, are cited in the Hype Cycle as significant enablers. These models can provide a more nuanced and comprehensive understanding of complex situations compared to models relying on a single data type.
Gartner expects these capabilities to become essential within every application and software product across all industries over the coming five years.
The role of AI TRiSM
AI trust, risk, and security management (TRiSM) also features prominently, with Gartner describing it as integral to responsible and ethical AI deployment. Comprising multiple layers of technical control, AI TRiSM supports enterprise policies to manage governance, safety, reliability, and data protection for AI applications.
“AI brings new trust, risk and security management challenges that conventional controls don’t address,” said Khandabattu. “Organisations must evaluate and implement layered AI TRiSM technology to continuously support and enforce policies across all AI entities in use.”
Trends in business alignment
Gartner’s Hype Cycle methodology, which tracks the stages of technological maturity and adoption, sheds light on the practicalities and potential barriers associated with AI. Khandabattu commented on the need for more tightly coordinated efforts across business and technology teams, highlighting that substantial benefits from AI will not happen spontaneously.
“Despite the enormous potential business value of AI, it isn’t going to materialise spontaneously,” said Khandabattu. “Success will depend on tightly business aligned pilots, proactive infrastructure benchmarking, and coordination between AI and business teams to create tangible business value.”
The Gartner Hype Cycle for Artificial Intelligence, 2025, indicates that the industry is at a stage where expectations are high for technologies such as AI agents, AI-ready data, multimodal AI, and TRiSM, as organisations seek measurable outcomes from their AI investments.
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