According to Gartner, AI Agents and AI Ready Data are the two most rapid technologies of artificial intelligence, the 2025 Gartner Hype Cycle.
These technologies have experienced a growing enthusiasm this year, with ambitious forecasts and speculative promises, and are escalating expectations.
Gartner's hype cycle provides a graphic representation of the maturity and adoption of technology and applications, and how they are potentially related to solving real business problems and exploiting new opportunities. Gartner's hype cycle methodology gives you an opinion on how technology or applications evolve over time, providing a healthy source of insights to manage deployments within the context of specific business goals.
“Because of the strong AI investment this year, the focus is on using AI for operability scalability and real-time intelligence,” said Haritha Khandabattu, senior director analyst at Gartner. “This has generated a step-by-step pivot from Generated AI (Genai) as a central focus towards a basic enabler that supports sustainable AI delivery, such as AI-enabled data and AI agents.”
Among the AI innovations Gartner expects, multimodal AI and AI trust, risk and security management (TRISM) has been identified as dominating the peak of expectations when it reaches mainstream adoption within the next five years.
Together, these developments will enable more robust, innovative and responsible AI applications, changing the way businesses and organizations operate.
Artificial Intelligence 2025 Hype Cycle
Source: Gartner (August 2025)
“Despite the enormous business value of AI, it's not going to happen naturally,” says Khandabattu. “Success relies on tightly business-aligned pilots, proactive infrastructure benchmarks, and coordination between AI and business teams to create concrete business value.”
AI Agent
An AI agent is an autonomous or semi-voluntary software entity using AI techniques to achieve goals in a digital or physical environment, making decisions and carrying out actions. Using AI practices and techniques such as LLMS, organizations create and deploy AI agents to achieve complex tasks.
“To enjoy the benefits of AI agents, organizations need to determine the most relevant business context and use cases. This is challenging given that AI agents are the same and all situations are different,” says Khandabattu. “AI agents continue to be stronger, but they cannot be used in all cases. Therefore, their use is heavily dependent on the requirements of the situation at hand.”
AI-enabled data
AI-enabled data optimizes datasets for AI applications, increasing accuracy and efficiency. Preparation is determined by the data's ability to prove fit for use in a particular AI use case. It can only be contextually determined for AI use cases and the AI techniques used, enforcing a new approach to data management.
According to Gartner, organizations investing at scale in AI will need to evolve their data management practices and their ability to scale to AI. This will meet existing and upcoming business demands, ensure trust, avoid risk and compliance issues, maintain intellectual property, and reduce bias and hallucinations.
Multimodal AI
Multimodal AI models are trained simultaneously on multiple types of data, such as images, video, audio, and text. By integrating and analyzing a wide range of data sources, you can better understand complex situations than models that use only one type of data. This will help users understand the world and open up a new path for AI applications.
According to Gartner's research, multimodal AI will become increasingly essential to improving the capabilities of all applications and software products in all industries over the next five years.
AI Trim
AI Trism plays a key role in ensuring ethical and safe AI deployments. It consists of four layers of technical capabilities that support enterprise policies for all AI use cases and help ensure AI governance, reliability, fairness, safety, reliability, security, privacy and data protection.
“AI poses new trust, risk and security management challenges that traditional controls don't address,” says Khandabattu. “Organisations need to evaluate and implement layered AI TRISM technology to continuously support and enforce policies across all entities in use.”
