FeatureWhy Agentic AI Projects Fail

Agentic AI projects are facing an unprecedented failure rate, with Gartner predicting that nearly 40% will fail by 2027. As autonomous artificial intelligence (AI) systems become more sophisticated, businesses are discovering that implementing AI agents successfully requires far more than adopting cutting-edge technology.

Agentic AI refers to autonomous AI systems that make decisions and take actions independently, without constant human oversight. Unlike traditional AI that provides recommendations, AI agents execute tasks, interact with systems and make business decisions autonomously.

Gartner's forecast identifies three drivers behind agentic AI project failures: high implementation costs exceeding budget projections, ambiguous return on investment with unclear business value, and inadequate risk management for autonomous AI systems.

Industry leaders point to five barriers that explain why so many AI agent initiatives fail — and how organizations can overcome them.

Table of Contents

• 1. Infrastructure and Data Foundation Gaps
• 2. Misaligned Expectations and ROI Challenges
• 3. Agent Washing and Vendor Hype
• 4. Organizational Change Management Deficits
• 5. Risk Management and Governance Inadequacies
• The Path Forward for Agentic AI

1. Infrastructure and Data Foundation Gaps

One of the most fundamental barriers to agentic AI success lies in inadequate infrastructure and data foundations. Companies are rushing to deploy sophisticated AI agents without establishing the necessary groundwork for autonomous AI systems to operate effectively.

"If you think of Agentic AI — AI that performs tasks — as a car, then you can imagine Generative AI as the engine, and data as the fuel," Lucidworks CEO Mike Sinoway told Reworked. "Our report finds that too many companies are trying to build Formula One racers around go-kart engines — and they might not even have enough gas to fill their tanks.”

Lucidworks' research found that 65% of companies lack the foundation to build useful agentic AI. This infrastructure gap creates problems for organizations implementing AI agents without addressing basic requirements.

This problem transcends industries. "Companies are racing to launch agents, but they're doing it on top of brittle infrastructure and siloed data," said Jeremiah Stone, CTO of SnapLogic. "Add in rushed implementations and limited visibility, and it's no surprise when these systems stall or fail to deliver."

The solution involves pairing AI agent initiatives with investments in core infrastructure: semantic search capabilities, clean structured data catalogs, proper API integration and hybrid search functionality.

2. Misaligned Expectations and ROI Challenges

Perhaps the most pervasive issue contributing to agentic AI failures is the fundamental misalignment between organizational expectations and implementation reality. Companies consistently underestimate the time, resources and effort required for successful AI agent deployment.

"I am seeing companies hoping for instant results. Instead, AI and workflow automation are taking more time and money," noted Yvette Kanouff, pioneer and partner at JC2 Ventures. "Therefore, companies are questioning if they have the right partner or if they are going about it the right way."

This expectation gap is particularly pronounced in measuring return on investment (ROI) for agentic AI initiatives. Many organizations focus on the wrong metrics, according to Raj Balasundaram, global vice president of AI innovations for customers at Verint.

"Companies are misjudging the ROI of AI agents in everyday workflows when they focus on 'internal' or 'AI-centric' metrics," Balasundaram told Reworked. "Instead, they should focus on measuring the outcomes a company aims to achieve."

The complexity of agentic AI implementation often catches organizations off guard. Kanouff pointed to "the lack of time commitment and unexpected manual effort needed to get the automation going" as frequent causes of failure.

To address these challenges, Stone recommended a disciplined approach: "Identify target business impact, quickly build and deploy an end-to-end 'thin slice,' measure outcomes then iterate relentlessly," he said. This approach forces organizations to clearly define what they're automating and how quickly they can prove value.

3. Agent Washing and Vendor Hype

The rapid emergence of agentic AI has created a phenomenon experts call "agent washing" — vendors rebranding existing automation tools as advanced AI agents without substantial new functionality. This misleading marketing contributes to agentic AI project failures and organizational disillusionment.

"It's a shame to see companies take the big leap to implement workflow automation and AI-driven interfaces only to see that the product was called AI and is really a manual automation," warned Kanouff. She recommended organizations look at reference implementations, technology architecture, assessing the interface automation (and proven time to automate) and doing a proof of concept before diving into a commitment.

This is part of a broader pattern of inflated promises, said Jesse Murray, SVP of Employee Experience and AI at Rightpoint. "Where we saw early promises of 40% efficiency gains with generative AI fail to deliver, those grand promises are being replicated and even amplified under the agentic AI banner," he said.

The challenge for organizations is distinguishing genuine agentic AI capabilities from repackaged automation tools. Balasundaram suggests focusing on tangible business outcomes: "Digital workplace leaders can distinguish real AI agents from 'agent washing' by focusing on the tangible business outcomes delivered by the AI-powered solutions,” he said.

4. Organizational Change Management Deficits

While people focus on the technical aspects of agentic AI implementation, experts consistently point to organizational and cultural factors that determine success or failure. Many organizations approach AI agents as purely technical deployments rather than transformative change initiatives.

"As much as agentic AI is about technology, implementing it is fundamentally a human endeavor within organizations," Murray told Reworked. "Even the best-run initiatives and most capable technologies will struggle to realize value absent the buy-in of the people responsible for driving change and success."

Cultural requirements for successful agentic AI implementation are specific and demanding. "Agentic AI projects thrive in organizations that are collaborative, flexible and led by engaged, visionary leaders,” Murray said. “They falter in environments that are siloed, fearful, or lack committed sponsorship."

"Culture is the multiplier,” agreed Amol Ajgaonkar, CTO at Insight. “You can have the best tools in the world, but if your teams don't trust them—or don't know how to use them—you won't see results."

The human element extends beyond simple change management to fundamental questions about workforce adaptation. Organizations must address how to structure workforces around expanding autonomous AI agents and how employees adapt to managing AI agents as part of their responsibilities.

"AI and workflow automation can still be seen as job takers and somewhat scary,” Kanouff said. Organizations need transparent communication about new job opportunities and robust reskilling programs to address these concerns effectively.

5. Risk Management and Governance Inadequacies

The autonomous nature of agentic AI introduces categories of risk that many organizations are unprepared to manage. Unlike traditional AI systems that provide recommendations for human decision-making, AI agents take actions independently, creating cascading risks requiring sophisticated governance frameworks.

"With Agentic AI, the risk of a bad decision is codified into the workflow and process, causing not only single decision error risk but also downstream cascading risks," explained Murray. This fundamental shift in risk profile demands new approaches to oversight and control.

Governance challenges are particularly acute because of the autonomous nature of these systems. Balasundaram identified two big risks:

1. The potential for biased or inaccurate results if the AI is not properly governed and trained on the company's specific data, and
2. The risk of overreliance on AI, which can lead to a lack of human oversight and the potential for errors to go unnoticed.

Ajgaonkar emphasizes the need for granular security controls: "Organizations need enterprise-specific policies that define how agents are built, what permissions they have, and how they're monitored." He advocated for detailed permission structures where every agent action is logged and auditable.

The complexity of multi-agent systems compounds these risks. "Assuming 5% for argument's sake, what happens when a 5% error rate cascades into multi-agent, creating a compounding failure?” Murray asked.

The Path Forward for Agentic AI

Despite these challenges, agentic AI shows substantial potential. Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024. This projection underscores the transformative potential for organizations that navigate implementation challenges.

Success requires a disciplined, holistic approach addressing technical, organizational and governance challenges simultaneously. Organizations must invest in proper infrastructure, set realistic expectations, carefully evaluate vendor claims, manage cultural change and implement robust risk controls.

Companies that will succeed are those treating agentic AI not as a simple technology deployment but as a comprehensive transformation initiative requiring planning, realistic expectations and sustained commitment to both technical and human success factors. The 40% failure rate predicted by Gartner is not inevitable. It's a warning that organizations willing to approach agentic AI with the rigor and preparation it demands can heed.

Editor's Note: Read more of our coverage of agentic AI below:

• What Real AI Agents Are – and Aren't — The rise of agent washing shows why AI fluency matters at the leadership level.
• Will Your Next Hire Be an AI Agent? — Autonomous AI agents are making their way into every corner of the workplace. A look at where we are and where we're headed.
• Who Watches the AI? Why Agentic AI Needs Observability Platforms — Agentic AI has greater potential but also higher risks than traditional LLMs. Observability platforms play a part in making sure things don't go off the rails.