AI agents and AI-ready data are the two fastest advancing technologies on the 2025 Gartner Hype Cycle for AI, placing 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) have been identified as dominating 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), organisations are creating and deploying AI agents to achieve complex tasks. “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,” 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 optimised 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, organisations 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 analysing 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. “Organisations must evaluate and implement layered AI TRiSM technology to continuously support and enforce policies across all AI entities in use.”

Frequently Asked Questions (FAQ)

Gartner's Hype Cycle for AI

Q: What technologies are at the Peak of Inflated Expectations on Gartner's 2025 Hype Cycle for AI? A: AI agents and AI-ready data are currently at the Peak of Inflated Expectations. Q: Why are AI agents and AI-ready data at the Peak of Inflated Expectations? A: These technologies are identified as the fastest advancing on Gartner's 2025 Hype Cycle for AI, indicating significant current interest and investment, but also potential overenthusiasm and unrealistic expectations. Q: What is the significance of a technology being on the "Peak of Inflated Expectations"? A: This phase suggests that the technology is receiving significant attention and investment, often leading to a rush of implementations and hype. However, many early-stage applications may fail to deliver on promises, leading to a disillusionment phase.

AI Agents

Q: How does Gartner define AI agents? A: Gartner defines AI agents as 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. Q: What are some key AI techniques used in AI agents? A: Large Language Models (LLMs) are a key AI technique utilized in the creation and deployment of AI agents for complex tasks. Q: What is a challenge in using AI agents effectively? A: A significant challenge is determining the most relevant business contexts and use cases, as no two AI agents are the same, and every situation is different. Their applicability also depends heavily on the specific requirements of the situation.

AI-Ready Data

Q: What does it mean for data to be "AI-ready"? A: AI-ready data signifies datasets that are optimized for AI applications, thereby enhancing accuracy and efficiency. The data's fitness for specific AI use cases determines its readiness. Q: How is the readiness of AI data determined? A: Readiness is determined contextually to the specific AI use case and the AI technique employed, necessitating new approaches to data management. Q: Why is evolving data management practices crucial for organizations investing in AI at scale? A: Evolving data management practices is essential to cater to existing and upcoming business demands, ensure trust, avoid risk and compliance issues, preserve intellectual property, and reduce bias and hallucinations in AI applications.

Multimodal AI and AI TRiSM

Q: What is Multimodal AI? A: Multimodal AI models are trained using multiple types of data simultaneously, such as images, video, audio, and text, allowing them to better understand complex situations than single-data-source models. Q: What is the projected impact of Multimodal AI in the coming years? A: Gartner expects multimodal AI to become increasingly integral to capability advancement in all application and software products across all industries over the next five years. Q: What is the role of AI TRiSM? A: AI TRiSM plays a crucial role in ensuring ethical and secure AI deployment by providing technical capabilities that support enterprise policies and assure AI governance, trustworthiness, fairness, safety, reliability, security, privacy, and data protection. Q: What challenges does AI TRiSM address? A: AI TRiSM addresses new trust, risk, and security management challenges that conventional controls do not cover, requiring organizations to implement layered AI TRiSM technology to enforce policies across all AI entities.

Crypto Market AI's Take

The Gartner Hype Cycle report underscores a significant shift in AI's focus from pure generative capabilities to the underlying infrastructure and agents that enable sustained AI delivery. This aligns perfectly with our mission at Crypto Market AI to provide robust, AI-driven tools for the cryptocurrency market. As AI agents become more sophisticated, their potential to analyze market data, identify trends, and even automate trading strategies in the volatile crypto space is immense. Our platform is designed to harness this potential, offering users advanced AI tools for market analysis and trading. The emphasis on AI-ready data also resonates deeply with our data-centric approach to market intelligence, ensuring that our insights are built on a foundation of accuracy and reliability.

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