According to global researchers at Gartner, the 2025 Hype Cycle for artificial intelligence indicates that the focus is shifting from the hype of Gen AI to building foundational innovations responsibly.
The AI Hype Cycle is Gartner’s graphical representation of the maturity, adoption metrics and business impact of AI technologies (including Gen AI). It is intended to help organisations understand where different AI innovations are on the path to becoming mainstream, why they are where they are and what these innovations mean in the context of the overall AI landscape.
With continued, steady investment in and adoption of AI, organisations have shifted to scaling AI with a focus on foundational innovations. The 2025 Hype Cycle for Artificial Intelligence helps leaders prioritise high-impact, emerging AI techniques, navigate regulatory complexity and scale operations.
According to the researchers, despite ethical and societal concerns, last year’s Hype Cycle for AI highlighted Gen AI as a potentially transformational technology with profound business impacts. This year, Gen AI enters the so-called Trough of Disillusionment, as organisations gain understanding of its potential and limitations.
AI leaders continue to face challenges when it comes to proving Gen AI’s value to the business. Despite an average spend of $1.9 million on Gen AI initiatives in 2024, less than 30% of AI leaders report their CEOs are happy with AI investment return. Low-maturity organisations have trouble identifying suitable use cases and exhibit unrealistic expectations for initiatives. Mature organisations, meanwhile, struggle to find skilled professionals and instill Gen AI literacy.
More broadly, organisations face governance challenges, examples of which described by Gartner are hallucinations, bias and fairness, and government regulations that may impede Gen AI applications for productivity, automation and evolving job roles.
As organisations gradually pivot from Gen AI as the central pillar of their AI programmes, they’re focusing on enabling technologies that support sustainable AI delivery. These technologies help streamline the integration and management of AI systems to make them effective and scalable.
For example, AI engineering, which enables organisations to establish and grow a high-value portfolio of AI solutions consistently and securely, is the foundational discipline for enterprise delivery of AI and Gen AI solutions at scale.
Another key foundational technology — one that is ultimately expected to reach the Plateau of Productivity — is model operationalisation ( or Modelops). With its focus on the end-to-end governance and life cycle management of advanced analytics, AI and decision models, Modelops helps standardise, scale and augment analytics, AI and Gen AI initiatives and move them into production.
Further supporting the shift in focus toward foundational AI technologies, the two biggest movers on this year’s Hype Cycle are AI-ready data and AI agents. Both sit at the Peak of Inflated Expectations.
To scale AI, leaders must evolve data management practices and capabilities to ensure AI-ready data — determined through the data’s ability to prove its fitness for use for specific AI use cases — can cater to existing and upcoming business demands. However, 57% of organisations estimate their data is not AI-ready. Organisations without AI-ready data will fail to deliver business objectives and open themselves up to unnecessary risks.
AI agents are 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. Breakthroughs in AI technology (e.g. evolving Gen AI, multimodal understanding and composite AI) have enabled organisations to use AI agents for complex tasks.
The complexity of AI agents makes them vulnerable to access security, data security and governance issues. Organisations also exhibit a lack of true trust in AI agents’ ability to operate without human oversight and concern about the significant impact of potential errors.
AI-native software engineering, a set of practices and principles optimised for using AI-based tools to develop and deliver software applications, according to Gartner, make their AI Hype Cycle debut this year.
Today’s software engineers can use AI to autonomously or semi-autonomously perform a series of tasks across the software development life cycle. Much of this is limited to AI assistants and testing tools in coding and testing activities. It reads more like AI augmentation than independent AI.
In the future, Gartner suggests AI will be integral and native to most software engineering tasks. This represents a significant evolution in the software development role, as engineers will shift focus to more meaningful tasks that require critical thinking, human ingenuity and empathy.
However, AI outputs are subject to bias, hallucinations and nondeterminism, which means software engineers cannot be overly trusting. Further, multi-agentic workflows create a compounded risk of hallucinations. AI tools also expand the threat surface area, creating new security vulnerabilities for organisations.
