A new report from Gartner predicts that over 40% of agentic AI initiatives will be abandoned by the end of 2027. This projection highlights a significant disconnect between the current hype surrounding agentic AI and the practical realities of its implementation. While interest in AI systems capable of autonomous decision-making and proactive task completion is surging, many organizations are finding it difficult to translate this enthusiasm into tangible results.
Gartner defines "agentic AI" as a category of artificial intelligence models that can autonomously perform complex tasks such as planning, learning, and goal achievement. These systems go beyond traditional generative AI by not just producing content but by taking action on behalf of users or systems. Examples include AI agents for scheduling, autonomous software workflows, and self-directed digital workers.
The research indicates that many enterprises, driven by competitive pressure and the fear of missing out (FOMO), are rushing to adopt agentic AI solutions without establishing clear return on investment (ROI) metrics or well-defined deployment roadmaps. This lack of strategic planning leads to projects being paused, restructured, or entirely canceled. Several factors contribute to this high failure rate, including rising costs, unclear business value, and insufficient risk management measures.
One major issue is the "agent washing" phenomenon, where vendors rebrand existing AI solutions, such as AI assistants, robotic process automation (RPA) tools, and chatbots, as agentic AI without substantially enhancing their agentic capabilities. Gartner estimates that only a small fraction of the thousands of vendors claiming to offer agentic AI solutions—around 130—actually provide genuine capabilities. This makes it difficult for organizations to identify and invest in truly effective solutions.
Anushree Verma, Senior Director Analyst at Gartner, notes that most agentic AI projects are in early-stage experiments or proofs of concept, often driven by hype and misapplication. Many current models lack the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time, leading to limited value or ROI.
Despite these challenges, the long-term outlook for agentic AI remains promising. Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, a significant increase from 0% in 2024. Additionally, the proportion of enterprise software applications incorporating agentic AI is expected to rise to 33% by 2028, up from less than 1% currently.
To avoid becoming part of the predicted cancellation statistic, organizations need to approach agentic AI with realistic expectations and a strategic focus. This includes establishing clear objectives, defining measurable outcomes, and carefully evaluating the capabilities of potential vendors. Businesses should prioritize value-driven outcomes and ensure that their AI initiatives align with specific business goals rather than being driven by hype. Gartner advises organizations to focus on redesigning workflows and focusing on tangible ROI to navigate implementation challenges successfully. They suggest starting with AI agents where decisions are needed, automation for routine workflows, and assistants for simple retrieval to drive business value through cost, quality, speed, and scale. By taking a measured and strategic approach, enterprises can harness the potential of agentic AI while avoiding the pitfalls that lead to project cancellation.
