Over 40 percent of agentic AI projects will be canceled by the end of 2027 due to rising costs, unclear business value, or insufficient risk controls, according to Gartner, Inc.
Agentic AI refers to advanced artificial intelligence systems that can autonomously perceive their environment, reason about goals, make decisions, and take actions without constant human supervision. Unlike traditional AI, which typically follows fixed rules or executes predefined tasks, agentic AI adapts dynamically to changing conditions and pursues complex, multi-step objectives by interpreting context and continuously learning from interactions.
“Most agentic AI projects right now are early stage experiments or proofs of concept 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.”
According to a January 2025 Gartner poll of 3,412 webinar attendees, 19 percent said their organization had made significant investments in agentic AI, 42 percent had made conservative investments, 8 percent had made no investments, while the remaining 31 percent were either taking a wait-and-see approach or were uncertain.
Many vendors are fueling the hype by engaging in “agent washing”—the rebranding of existing products, such as AI assistants, robotic process automation (RPA), and chatbots, without substantial agentic capabilities. Gartner estimates that only about 130 of the thousands of agentic AI vendors are legitimate.
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Current limitations in maturity and ROI
“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.”
Despite these initial challenges, the trend toward agentic AI signifies a leap forward in AI capabilities and market opportunities. Agentic AI will provide innovative ways to enhance resource efficiency, automate complex tasks, and introduce new business innovations that surpass the capabilities of scripted automation bots and virtual assistants.
Gartner predicts that at least 15 percent of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0 percent in 2024. Additionally, 33 percent of enterprise software applications will incorporate agentic AI by 2028, compared to less than 1 percent in 2024.
At this early stage, Gartner advises that agentic AI should only be pursued where it offers clear value or ROI. Integrating agents into legacy systems can be technically challenging, often disrupting workflows and requiring costly modifications. In many instances, rethinking workflows with agentic AI from the ground up is the optimal path to successful implementation.
“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.”
