AI agents and AI-ready data are the two fastest-emerging technologies on Gartner’s Hype Cycle for Artificial Intelligence 2025, according to Gartner, Inc., a firm specializing in business and technology research. Both areas have seen rising interest this year, accompanied by ambitious forecasts and speculative promises that place them at the peak of inflated expectations.
Gartner’s Hype Cycles provide a visual representation of the maturity and adoption of technologies and applications, and how they might address real business problems and unlock new opportunities. The Hype Cycle methodology maps how a technology or application is expected to evolve over time, offering a practical framework to guide implementation decisions aligned with specific business objectives.
“As AI investments remain strong this year, there is greater emphasis on using AI for operational scalability and real-time insight,” said Haritha Khandabattu, Senior Director Analyst at Gartner. “That has driven a gradual shift from focusing primarily on generative AI (GenAI) toward foundational enablers that support sustainable AI delivery, such as AI-ready data and AI agents.”
Among the AI innovations Gartner expects to reach mainstream adoption within the next five years, multimodal AI and AI trust, risk and security management (AI TRiSM) stand out as leading areas with high expectations. Together, these developments will enable more robust, innovative, and responsible AI applications, reshaping how organizations operate.
Figure 1: Hype Cycle for Artificial Intelligence 2025
Source: Gartner (August 2025)
“Despite AI’s huge potential to create business value, that value will not materialize on its own,” Khandabattu said. “Success depends on closely aligned business pilots, proactive infrastructure benchmarking, and coordinated efforts between AI and business teams to create tangible outcomes.”
AI agents
AI agents are autonomous or semi-autonomous software entities that use AI techniques to perceive environments, make decisions, take actions, and achieve goals in digital or physical settings. Leveraging AI methods such as large language models (LLMs), organizations design and deploy AI agents to perform complex tasks and automate workflows.
“To benefit from AI agents, organizations must identify the most relevant business contexts and use cases, which is challenging because no two AI agents are identical and every situation is unique,” Khandabattu explained. “While AI agents will continue to grow more capable, they are not universally applicable; their usefulness depends heavily on the specific requirements of each scenario.”
AI-ready data
AI-ready data ensures datasets are optimized for AI workloads, improving accuracy, reliability, and efficiency. Maturity is determined by a dataset’s proven suitability for the intended AI use case and the specific AI technologies involved. That assessment must be made contextually, relative to the use case and model type, prompting new approaches to data management.
Gartner advises that organizations investing in AI at scale must evolve their data management practices and capabilities to support AI needs. This includes meeting current and future business requirements, ensuring trust, avoiding compliance and legal risks, protecting intellectual property, and reducing bias and hallucinations.
Multimodal AI
Multimodal AI models are trained on multiple types of data simultaneously—such as images, video, audio, and text. By integrating and analyzing diverse data sources, these models can better understand complex situations than single-modality models. This broader understanding improves user insight and unlocks new possibilities for AI-driven applications.
Gartner’s research indicates multimodal AI will increasingly become embedded across skill sets, applications, and software products in every industry over the next five years.
AI TRiSM
AI trust, risk and security management (AI TRiSM) is critical for ethical and secure AI deployment. It comprises multiple technical layers that support organizational policies across all AI use cases and help ensure governance, reliability, fairness, security, safety, privacy, and data protection.
“AI introduces new trust, risk, and security challenges that conventional controls do not fully address,” Khandabattu said. “Organizations must evaluate and implement layered AI TRiSM technologies to continuously enforce and maintain policies for all AI systems in use.”
Gartner clients can consult the full Hype Cycle for Artificial Intelligence, 2025 report for additional details.
Learn how to build actionable strategies to align your business, empower teams, and increase revenue in the era of agentic AI with Gartner’s guidance “5 Steps to Capture the Agentic AI Opportunity.”