A recent forecast from Gartner has cast a sobering light on the future of agentic AI, predicting that by 2027, more than 40 percent of all projects in this domain will be scrapped before reaching successful deployment. The report attributes this trend to escalating implementation costs, vague return-on-investment metrics, and growing concerns over reliability, governance, and operational risk.

Agentic AI refers to systems capable of autonomous decision-making, learning from their environments, and independently initiating actions to achieve objectives without explicit, step-by-step human instructions. Unlike traditional rule-based automation or static AI models, agentic AI is intended to operate with higher levels of independence and contextual intelligence. Yet, this ambitious vision appears to be outpacing what many enterprises are currently equipped to handle—technically, financially, and strategically.

According to Gartner’s data, only 19 percent of surveyed enterprises have moved beyond experimental phases and invested meaningfully in agentic AI solutions. The majority remain cautious, with over 40 percent still testing use cases in controlled environments or assessing feasibility. More than 30 percent have yet to make a decision, reflecting broader hesitation around the maturity of the technology and the unclear timelines for ROI realization.

A key issue highlighted by the report is the lack of well-defined business models that can accommodate autonomous agents. Many enterprises reportedly underestimated the amount of operational change needed to integrate these systems. Agentic AI often demands a reimagining of workflow architectures, a shift from centralized control to distributed decision-making, and the incorporation of dynamic learning feedback loops—all of which require significant investment in both infrastructure and culture.

Compounding the problem is what Gartner calls “agent-washing”—a growing tendency among technology vendors to rebrand conventional AI tools like chatbots, scripted bots, and simple RPA flows as agentic AI. This has created confusion in the market, with clients expecting intelligent, proactive agents but receiving limited, reactive automation instead. Gartner estimates that fewer than 5 percent of the 3,000+ companies promoting agentic AI today offer truly autonomous systems by their definition. Only about 130 companies currently meet the criteria for building functional, general-purpose agents capable of goal-directed reasoning.

Despite the near-term challenges and projected failures, Gartner maintains that the long-term potential of agentic AI remains significant. By 2028, it estimates that agent-based systems will account for at least 15 percent of routine decision-making processes across major industries—ranging from supply chain management to customer experience, finance operations, and digital marketing. Additionally, agentic capabilities are expected to be embedded in roughly one-third of enterprise-grade software by that time, ushering in a new paradigm for how digital systems are designed and used.

To succeed, however, Gartner advises enterprises to rethink their implementation strategies. This includes redefining KPIs for agent performance, ensuring robust AI ethics and compliance frameworks, investing in talent with experience in multi-agent systems and reinforcement learning, and most importantly, maintaining human oversight. Agentic AI is not a plug-and-play solution—it requires thoughtful design, governance, and continuous monitoring.

The forecast serves as both a warning and a roadmap. While agentic AI holds the promise of revolutionizing how businesses operate, a significant portion of early adopters are expected to falter without clear vision, sustained investment, and disciplined execution. For those who can overcome these hurdles, however, the payoff may be transformative.