“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst, Gartner. “This can blind organisations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology.”
According to a poll conducted by Gartner in January 2025 19% of respondents said their organisation had made significant investments in agentic AI, 42% had made conservative investments, 8% no investments, with the remaining 31% taking a wait and see approach or are unsure.
Vendors are seen as contributing to the hype by engaging in “agent washing”, which is the rebranding of existing products, such as AI assistants, robotic process automation (RPA) and chatbots, without substantial agentic capabilities. In fact, Gartner estimates only about 130 of the thousands of agentic AI vendors are real.
“Most agentic AI propositions lack significant value or return on investment (ROI), as current models don’t have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time,” said Verma. “Many use cases positioned as agentic today don’t require agentic implementations.”
However, despite these initial challenges, the trend toward agentic AI represents a leap forward in AI capabilities and market opportunity. Agentic AI will provide new means to enhance resource efficiency, automate complex tasks and introduce new business innovations, beyond the capabilities of scripted automation bots and virtual assistants.
Gartner predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024. In addition, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.
In this early stage, Gartner is recommending that agentic AI is only pursued where it delivers clear value or ROI. Integrating agents into legacy systems can be technically complex, often disrupting workflows and requiring costly modifications. In many cases, rethinking workflows with agentic AI from the ground up is the ideal path to successful implementation.
“To get real value from agentic AI, organisations must focus on enterprise productivity, rather than just individual task augmentation,” said Verma. “They can start by using AI agents when decisions are needed, automation for routine workflows and assistants for simple retrieval. It’s about driving business value through cost, quality, speed and scale.”
