Gartner recently revealed a new report where it predicted that by the end of 2027, over 40% of agentic AI projects will be canceled. Factors contributing to this decline include escalating costs, unclear business value, and inadequate risk controls.
According to the analyst firm, one trend it is seeing is that vendors are hyping up AI agents by “agent washing,” or rebranding existing products like AI assistants, RPA, or chatbots without actually adding substantial agentic capabilities.
The company estimates that of the thousands of agentic AI vendors out there, only about 130 of them are real and delivering on their promises.
“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 at Gartner. “This can blind organizations 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.”
In January, the company polled 3,412 webinar attendees to see where their company was on their agentic AI journey. The results were that:
- 19% have made significant investments
- 42% made conservative investments
- 8% made no investment
- 31% were taking a “wait and see” approach or were unsure.
Despite its prediction that many current AI agent projects will be canceled over the course of the next several years, Gartner does still believe there’s opportunity. Agentic AI does have the potential to enhance resource efficiency, automate complex tasks, and introduce new business innovations.
It predicts that by 2028, 15% of day-to-day work decisions will be made autonomously and 33% of enterprise software applications will include agentic AI.
During this period where the technology is still young, Gartner recommends only pursuing agentic AI in cases where it delivers clear value or ROI.
It also recommends rethinking workflows from the ground up rather than trying to incorporate agentic AI into legacy systems, which can be a technically complex and expensive process.
“To get real value from agentic AI, organizations 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.”
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