AI agents and AI-ready data are the two fastest-growing technologies on Gartner’s 2025 Artificial Intelligence Hype Cycle, according to Gartner, Inc., a firm that focuses on business and technology insights. These technologies are seeing a surge in interest this year, along with ambitious forecasts and speculative promises, placing them at the top of inflated expectations.

Gartner's Hype Cycles provides a graphical representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. Gartner's Hype Cycle methodology provides a picture of how a technology or application will evolve over time, providing a sound source of insight for managing its implementation within the context of specific business objectives.

“As AI investment continues to be strong this year, there is a greater focus on using AI for operational scalability and real-time insights,” said Haritha Khandabattu, Senior Director Analyst at Gartner. “This has led to a gradual shift away from generative AI (GenAI) as a central focus, towards the fundamental enablers that support sustainable AI delivery, such as AI-ready data and AI agents.”

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Among the AI innovations that Gartner expects to reach mainstream adoption within the next five years, multimodal AI and AI trust, risk, and security management (TRiSM) have been identified as dominant areas of high expectation (see Figure 1). Together, these developments will enable more robust, innovative, and responsible AI applications, transforming the way businesses and organizations operate.

Figure 1: The Artificial Intelligence Hype Cycle 2025

Source: Gartner (August 2025)

“Despite the enormous potential business value of AI, it will not materialize spontaneously,” Khandabattu said. “Success will depend on tightly business-aligned pilots, proactive infrastructure benchmarking, and coordination between AI and business teams to create tangible business value.”

AI agents
AI agents are autonomous or semi-autonomous software entities that use AI techniques to perceive, make decisions, take actions, and achieve goals in their digital or physical environments. Using AI methods and techniques such as Master of Laws (LLM) programs, organizations create and deploy AI agents to perform complex tasks.

“To take advantage of AI agents, organizations need to determine the most relevant business contexts and use cases, which is challenging because no two AI agents are the same and every situation is unique,” Khandabattu said. “While AI agents will continue to become more powerful, they cannot be used in all cases, so their use will largely depend on the requirements of the current situation.”

AI-ready data
AI-ready data ensures that data sets are optimized for AI applications, improving accuracy and efficiency. Maturity is determined by the ability of the data to demonstrate its suitability for use in specific AI use cases. It can only be determined contextually in relation to The AI use case and the AI technology used, which forces new methods of data management.

According to Gartner, organizations investing in AI at scale need to evolve their data management practices and capabilities to extend them to AI. This will address existing and emerging business requirements, ensure trust, avoid risk and compliance issues, preserve intellectual property, and reduce bias and hallucinations.

Multimodal AI
Multimodal AI models are trained on multiple types of data at the same time, such as images, video, audio, and text. By integrating and analyzing different data sources, they can better understand complex situations than models that use only one type of data. This helps users understand the world and opens up new possibilities for AI applications.

Multimodal AI will become increasingly integrated into skills development across all applications and software products across all industries over the next five years, according to Gartner research.

YOU ARE ANGRY
AI TRiSM plays a critical role in ensuring ethical and secure AI implementation. It consists of four layers of technical capabilities that support enterprise policies for all AI use cases and help ensure AI governance, trustworthiness, fairness, security, reliability, privacy, and data protection.

“AI brings new challenges to trust, risk and security management that conventional controls do not address,” said Khandabattu. “Organizations must evaluate and implement AI TRiSM technology across multiple layers to continuously support and enforce policies across all AI devices in use.”

Gartner clients can read more in the report Hype Cycle for Artificial Intelligence, 2025.

Learn how to build actionable strategies to align your operations, empower your teams, and grow revenue in the agentic AI era in this free Gartner insight.” 5 Steps to Seizing the Agentic AI Opportunity” .