Leading research and advisory firm Gartner has issued a stark warning about the future of autonomous artificial intelligence projects. According to its latest forecast, over 40% of agentic AI initiatives will be abandoned or canceled by the end of 2027 due to high costs, immature technology, and failure to deliver measurable business value.
Agentic AI, a category of artificial intelligence designed to operate autonomously with minimal human oversight, has become a buzzword across industries. These AI “agents” are expected to analyze data, make decisions, and take actions on behalf of humans—ideally streamlining operations, enhancing customer service, and enabling real-time responses.
However, Gartner’s new report, published on June 25, suggests that the reality has not yet caught up with the ambition. According to Anushree Verma, Senior Director Analyst at Gartner, many current agentic AI projects are driven by hype rather than practical application. “Most agentic AI projects right now are early-stage experiments, not mature business applications,” Verma said. “They lack the scalability, reliability, and autonomy to justify the investment.”
Gartner also highlighted a growing trend of “agent washing,” where companies rebrand traditional AI tools or chatbots as autonomous agents without true agentic capabilities. Of thousands of vendors claiming to offer agentic AI, only about 130 have developed systems capable of functioning independently across complex workflows.
While these challenges are significant, Gartner’s forecast is not entirely pessimistic. By 2028, the firm expects agentic AI to play a transformative role in enterprise technology. It predicts that 15% of routine business decisions will be made autonomously by AI agents, and 33% of enterprise software will embed agentic capabilities, up from under 1% in 2024.
This reflects a pattern familiar in the technology sector: early overhype followed by market correction, then eventual maturity. The current phase, Gartner argues, is one of experimentation, learning, and attrition—where projects that are poorly scoped or built on unrealistic expectations will inevitably fail.
Still, agentic AI holds long-term potential. In industries like healthcare, customer service, supply chain management, and finance, autonomous systems could eventually handle tasks ranging from scheduling and diagnostics to risk assessment and fraud detection.
For organizations considering agentic AI, Gartner recommends focusing on narrow use cases with measurable outcomes, such as automating internal processes or improving response times in customer service. Success depends on combining robust data infrastructure, continuous human oversight, and realistic deployment strategies.
In conclusion, while more than 40% of agentic AI projects may not survive the next two years, this should not be interpreted as a failure of the technology itself—but rather as a sign of market recalibration. Enterprises that approach agentic AI with a clear strategy, realistic scope, and agile implementation methods may still unlock significant long-term value.
