If you were hoping 2025 would be the year generative AI stole the show again, Gartner has some news: the spotlight is shifting. In its newly released Hype Cycle for Artificial Intelligence, 2025, the research firm says AI agents and AI-ready data are now the fastest-moving stars on the curve, elbowing past headline-grabbing chatbots to sit at the “Peak of Inflated Expectations.”
This peak, in Gartner-speak, is where buzz, big promises, and PowerPoint roadmaps tend to outpace what’s currently possible. But it’s also where tomorrow’s mainstream tools often make their debut—if they survive the inevitable slide into disillusionment.
From GenAI Frenzy to Infrastructure Focus
“With AI investment remaining strong this year, the emphasis is shifting toward operational scalability and real-time intelligence,” said Haritha Khandabattu, Senior Director Analyst at Gartner. Translation: companies are starting to look beyond splashy demos and toward the unglamorous but essential building blocks—like clean, AI-ready datasets and autonomous software agents that can actually execute work.
It’s a notable pivot from the 2023–24 generative AI boom, when large language models (LLMs) dominated roadmaps. Now, the underlying enablers are taking center stage, as enterprises realize sustainable AI isn’t just about generating text or images—it’s about having the right data, systems, and governance in place to do it reliably and securely.
AI Agents: The Digital Workforce in Waiting
AI agents—autonomous or semi-autonomous software that perceives, decides, and acts—are evolving fast. Think of them as AI-powered co-workers that can navigate complex tasks, not just spit out answers. But no two are alike, and deploying them at scale means nailing the right use cases. As Khandabattu warns, “They can’t be used in every case… use will largely depend on the requirements of the situation at hand.”
AI-Ready Data: Garbage In, Garbage Out—But Faster
“AI-ready” isn’t a buzzword; it’s a mandate. Data needs to be clean, compliant, context-specific, and bias-minimized before an AI model can touch it. Gartner says enterprises investing in AI at scale will have to overhaul data management—both to feed more accurate models and to keep regulators happy. Expect new tooling and practices designed to make datasets “fit for purpose” in the AI era.
Rising Peaks: Multimodal AI and AI TRiSM
Two other technologies dominate the 2025 Peak: multimodal AI and AI TRiSM. Multimodal AI, which can process text, images, audio, and video together, is already driving the next generation of applications—from better medical diagnostics to smarter enterprise search. Gartner predicts it’ll be “integral to capability advancement in every application” within five years.
AI TRiSM (Trust, Risk, and Security Management) is less flashy but increasingly critical. It’s the governance layer ensuring AI remains ethical, safe, and secure across use cases. Conventional IT controls aren’t enough, says Gartner—layered, AI-specific safeguards are now table stakes.
The Bigger Picture
Gartner’s Hype Cycle has long been a reality check for both hype-happy vendors and cautious adopters. This year’s takeaway? The real race in AI isn’t just about building bigger models—it’s about making them work in the messy, regulated, business-driven real world.
Or put more bluntly: If GenAI was the fireworks show, AI-ready data and AI agents are the electricians and city planners making sure the lights stay on after the crowd goes home.
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