AI Agents and AI-Ready Data Lead Gartner's 2025 Hype Cycle for AI

AI agents and AI-ready data are the two fastest advancing technologies on the 2025 Gartner Hype Cycle for AI, positioning them at the Peak of Inflated Expectations. “With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence,” says Gartner’s Haritha Khandabattu. “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 agents.” Among the AI innovations Gartner expects will reach mainstream adoption within the next five years, multimodal AI and AI trust, risk, and security management (TRiSM) dominate the Peak of Inflated Expectations.

Hype Cycle for Artificial Intelligence 2025

Source: Gartner (August 2025) “Despite the enormous potential business value of AI, it isn’t going to materialize spontaneously,” says Khandabattu. “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 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 large language models (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,” says 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 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. 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 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, says Gartner. 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,” says Khandabattu. “Organizations must evaluate and implement layered AI TRiSM technology to continuously support and enforce policies across all AI entities in use.”

Frequently Asked Questions (FAQ)

What are AI agents?

AI agents are autonomous or semi-autonomous software entities that leverage AI techniques to perceive their environment, make decisions, take actions, and achieve specific goals.

What is AI-ready data?

AI-ready data refers to datasets that are optimized for AI applications, ensuring they are accurate, efficient, and fit for their intended use cases, often determined contextually.

What is multimodal AI?

Multimodal AI models are trained on diverse data types simultaneously, such as text, images, and audio, allowing them to understand complex situations more effectively than single-data-type models.

What is AI TRiSM?

AI TRiSM stands for AI Trust, Risk, and Security Management. It encompasses technical capabilities that ensure ethical and secure AI deployment, covering governance, trustworthiness, fairness, safety, reliability, security, and data protection.

What are the key takeaways from Gartner's 2025 Hype Cycle for AI?

Gartner's 2025 Hype Cycle for AI highlights AI agents and AI-ready data as the fastest advancing technologies, both reaching the Peak of Inflated Expectations. It emphasizes a shift towards foundational AI enablers for operational scalability and real-time intelligence, with multimodal AI and AI TRiSM also prominent.

Crypto Market AI's Take

The Gartner Hype Cycle report underscores the maturation of AI in business strategy, moving beyond the initial excitement of generative AI towards practical applications. For the cryptocurrency industry, this signals a growing demand for robust, scalable, and secure AI solutions. As AI agents become more sophisticated, they can be leveraged for advanced market analysis, automated trading strategies, and enhanced risk management within crypto portfolios. Ensuring AI-readiness in data is crucial for the accuracy and reliability of these AI-driven insights, which aligns with our mission at Crypto Market AI to provide intelligent, data-backed solutions for navigating the digital asset landscape. Our platform's focus on AI-powered trading bots and AI analysts directly addresses the need for advanced AI capabilities in finance.

AI Agents and A-ready data top Gartner Hype-Cycle