AI agents and AI-ready data are the two fastest advancing technologies on the 2025 Gartner Hype Cycle for Artificial Intelligence, according to Gartner, Inc. a business and technology insights company. These technologies are experiencing heightened interest this year, accompanied by ambitious projections and speculative promises, placing them at the Peak of Inflated Expectations.

Gartner Hype Cycles provide a graphic 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 Hype Cycle methodology gives a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of specific business goals.

“With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence,” said Haritha Khandabattu, Senior Director Analyst at Gartner. “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.”

Among the AI innovations Gartner expects will reach mainstream adoption within the next 5 years, multimodal AI and AI trust, risk and security management (TRiSM) have been identified as dominating the Peak of Inflated Expectations (see Figure 1). Together, these developments will enable more robust, innovative and responsible AI applications, transforming how businesses and organizations operate.

“Despite the enormous potential business value of AI, it isn’t going to materialize 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.”

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AI Agents

AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments. Using AI practices and techniques such as LLMs, organizations are creating and deploying AI agents to achieve complex tasks.

“To reap the benefits of AI agents, organizations 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

AI-ready data ensures datasets are optimized for AI applications, enhancing accuracy and efficiency. Readiness is determined through the data’s ability to prove its fitness for use for specific AI use cases. It can only be determined contextually to the AI use case and the AI technique used, which forces new approaches to data management.

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

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Multimodal AI

Multimodal AI models are trained with multiple types of data simultaneously, such as images, video, audio and text. By integrating and analyzing diverse data sources, they can better understand complex situations better than models that use only one type of data. This helps users make sense of the world and opens up new avenues for AI applications.

Multimodal AI will become increasingly integral to capability advancement in every application and software product across all industries over the next five years, according to Gartner research.

AI TRiSM

AI TRiSM plays a crucial role in ensuring ethical and secure AI deployment. It comprises four layers of technical capabilities that support enterprise policies for all AI use cases and help assure AI governance, trustworthiness, fairness, safety, reliability, security, privacy and data protection.

“AI brings new trust, risk and security management challenges that conventional controls don’t address,” said Khandabattu. “Organizations must evaluate and implement layered AI TRiSM technology to continuously support and enforce policies across all AI entities in use.”