Nearly half of agentic AI projects may fail by 2027, warns research firm

Research firm Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. The forecast was announced on June 25, 2025, during analysis of emerging AI trends by Senior Director Analyst Anushree Verma.

According to Verma, "Most agentic AI projects right now are early stage experiments or proof of concepts that are mostly driven by hype and are often misapplied." This trend could prevent many implementations from advancing beyond pilot phases to full production deployment.

Summary

Who: Gartner, Inc., a leading technology research firm, issued predictions about agentic AI project failures. Senior Director Analyst Anushree Verma led the analysis. The forecast affects marketing professionals, enterprise executives, and technology vendors developing autonomous AI systems.

What: Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027. The cancellations result from escalating costs, unclear business value, and inadequate risk controls. Only 130 vendors among thousands offer genuine agentic capabilities, with many engaging in "agent washing" of existing products.

When: The announcement occurred on June 25, 2025, based on January 2025 polling of 3,412 webinar attendees. Implementation challenges are happening now, with failure predictions extending through 2027. However, successful implementations could see 15% of work decisions made autonomously by 2028.

Where: The research covers global markets with particular focus on enterprise software, manufacturing, financial services, and healthcare sectors. Marketing automation and digital advertising face transformation as traditional web-based models become obsolete through agent-mediated interactions.

Why: Technical complexity, integration challenges with legacy systems, and insufficient return on investment drive project failures. Organizations underestimate implementation costs and overestimate current AI model capabilities. Success requires careful use case selection, workflow redesign, and focus on enterprise productivity rather than individual task automation.

Gartner's January 2025 poll of 3,412 webinar attendees revealed uneven investment patterns. While 19% reported significant investments in agentic AI systems, 42% made conservative investments. Only 8% made no investments, with 31% adopting wait-and-see approaches or remaining uncertain about implementation timing.

The research firm estimates only 130 vendors among thousands claiming agentic AI capabilities offer genuine autonomous functionality. Many vendors engage in "agent washing" by rebranding existing products like AI assistants, robotic process automation tools, and chatbots without substantial agentic capabilities.

Technical complexity driving failures

Agentic AI systems differ fundamentally from traditional automation by using sophisticated reasoning and iterative planning to solve complex, multi-step problems autonomously. According to NVIDIA documentation from October 22, 2024, these systems follow a four-step process: perceive data from various sources, reason through large language model orchestration, act via external tool integration, and learn through continuous feedback loops.

Unlike robotic process automation that relies on fixed rules, agentic AI operates independently through decision-making based on continuous learning and analysis of external data. The systems require various AI techniques including natural language processing, machine learning, and computer vision depending on operational environments.

Implementation requires significant infrastructure investment. Retrieval-augmented generation systems access proprietary data sources to deliver relevant outputs, while continuous learning mechanisms create data flywheels where interactions feed back into systems for model refinement. This technical complexity contributes to escalating costs that many organizations underestimate during initial planning phases.

Enterprise deployment challenges

Current agentic AI propositions often lack substantial value or return on investment, according to Gartner analysis. Existing models lack sufficient maturity and agency to autonomously achieve complex business goals or follow nuanced instructions consistently over extended periods.

Integration with legacy systems presents particular technical challenges, often disrupting established workflows and requiring costly modifications. Many organizations discover that retrofitting agents into existing infrastructure proves more complex than anticipated, leading to project delays and budget overruns.

Guardrails built into AI agents help ensure correct task execution within defined parameters. For instance, customer service agents may process claims up to certain amounts while escalating larger claims for human approval. However, implementing these controls across diverse enterprise environments requires extensive customization and testing.

Marketing sector implications

The marketing community faces particular challenges as agentic AI transforms traditional advertising and customer engagement models. Microsoft's recent declaration of the "traditional web" as obsolete signals fundamental shifts in how consumers interact with brands and content.

Context engineering emerges as a crucial discipline for successful agent implementations. Unlike traditional prompt engineering, context engineering requires understanding data architecture, information retrieval systems, and dynamic adaptation mechanisms for optimal agent performance.

Marketing automation platforms integrate agentic capabilities through interconnected AI systems performing complex tasks. Zeta Global announced AI Agent Studio general availability on March 27, 2025, enabling orchestrated agent collaboration rather than isolated operations for campaign management and customer journey optimization.

The transformation eliminates traditional advertising-supported web content models. When agents compile information automatically without human website visits, display advertising, affiliate marketing, and content monetization strategies become obsolete. Web analytics and conversion tracking lose significance as human visitors no longer interact with websites directly.

Real-world implementation examples

Manufacturing and predictive maintenance applications show early success. Siemens AG uses agentic AI to analyze real-time sensor data from industrial equipment, predicting failures before occurrence. Following deployment, the company reduced unplanned downtime by 25%.

Financial services demonstrate sophisticated implementations. JPMorgan Chase developed LOXM, executing high-frequency trades autonomously while adapting to market volatility faster than human traders. These applications benefit from clearly defined parameters and measurable outcomes.

Healthcare applications focus on administrative automation and clinical decision support. Propeller Health integrates agentic AI into smart inhaler technology, collecting real-time patient data on medication usage and environmental factors. The system alerts healthcare providers when necessary and tracks patient patterns for improved care delivery.

Future market predictions

Despite implementation challenges, Gartner predicts significant growth for successful deployments. At least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, increasing from 0% in 2024. Additionally, 33% of enterprise software applications will include agentic AI capabilities by 2028, up from less than 1% in 2024.

The shift toward autonomous decision-making represents a fundamental change in how organizations operate. Organizations must focus on enterprise productivity rather than individual task augmentation to realize substantial value from agentic AI investments.

Revenue opportunities for surviving implementations remain substantial. Perplexity AI's vision of agents as advertising targets suggests new business models where AI systems, rather than humans, become primary audiences for commercial messages.

According to research cited by Wikipedia contributors, reinforcement learning becomes essential for agentic AI decision-making through trial-and-error methods. Combined with deep learning through multi-layered neural networks, these technologies enable agents to adjust dynamically and optimize procedures with minimal human control.

Risk management considerations

Privacy implications require careful attention as agentic systems process vast amounts of personal data. Recent EDPB analysis identifies eleven fundamental privacy risks that organizations must address, including insufficient data protection, adverse impacts on fundamental rights, and lack of human intervention.

Insufficient data protection safeguards may lead to unauthorized access or data breaches, particularly with retrieval-augmented generation systems connecting to external knowledge bases. Organizations must implement robust encryption protocols and access controls to prevent exposure of sensitive information.

Automated decision-making without human review may violate GDPR requirements for human oversight, especially for decisions significantly affecting individuals. Marketing implementations must establish clear protocols for human intervention when necessary.

Industry adaptation strategies

Organizations should pursue agentic AI only where it delivers clear value or return on investment. Starting with AI agents for decision-making scenarios, automation for routine workflows, and assistants for simple retrieval provides a graduated approach to implementation.

Rethinking workflows with agentic AI from the ground up often proves more effective than retrofitting existing processes. This approach avoids integration complications while maximizing the technology's autonomous capabilities.

The technology's potential spans from simple content generation to complex enterprise software orchestration. Success depends on careful selection of use cases matching current technical capabilities with organizational needs and resources.

Timeline

• October 22, 2024: NVIDIA publishes comprehensive explanation of agentic AI capabilities and four-step operational process
• January 2025: Gartner poll reveals uneven investment patterns across 3,412 organizations
• March 27, 2025: Zeta Global announces AI Agent Studio with agentic workflows for marketing automation
• May 21, 2025: Microsoft declares traditional web obsolete as agentic systems emerge
• June 25, 2025: Gartner predicts 40% project failure rate for agentic AI implementations by 2027
• June 22, 2025: Context engineering emerges as crucial discipline for AI agent success