How the top 5% of Enterprises Are Deploying Agentic AI at Scale

Why Agentic AI Is Reshaping Knowledge Management 

It important to ground agentic expectations in a technical reality: AI Agents are only as intelligent as the data they can access. 

Traditional enterprise search provides not much more than links. Simple chatbots generate text. But Agentic AI must plan, reason, and act autonomously toward goals, not just respond to prompts. To achieve that organizations will require more than just an LLM and some prompting.  They will need to build agents with a decision-making logic that is grounded deep, structured, governed access to the enterprise knowledge that exists in their knowledgebase and theirs alone.  

The Reality Check: Most AI Pilots Fail to Scale (It’s a Data Problem) 

Alan Pelz-Sharpe, Founder and Principle Analyst at Deep Analysis said, “AI agents work—often really, really badly.” 

Despite massive investment, 95% of enterprise AI pilots never make it to production. The top concern among enterprises– now even more than cost – is data readiness. A core technical failure across most enterprise AI deployments is the disconnect between LLMs and enterprise knowledge.  

Without robust Retrieval-Augmented Generation (RAG), agents hallucinate. AI on messy data leads to high-confidence hallucinations and unreliable outcomes. Agents may also create security gaps by accessing documents they shouldn’t see or use. And they lack domain grounding, providing generic answers rather than domain-specific ones. 

Enterprises need systems that are accurate, explainable, governed, and aligned with real tasks, not demos that work only under ideal conditions. 

Inside a Real Deployment: Agentic AI in Engineering & R&D 

When a global engineering and manufacturing company set out to scale Agentic AI, they ran into major hurdles. Data was scattered across siloed systems, making it hard for engineers to find what they needed. Early AI pilots often gave inaccurate or generic answers. AI agents sometimes accessed documents they shouldn’t, creating serious security and governance risks. 

To address these challenges, the organization implemented a secure Retrieval-Augmented Generation (RAG) platform that gave AI agents access to trusted, real-time enterprise data. They began with focused, high-value use cases tied closely to business needs and measurable results. Strong governance and data permissions were built in from the start to ensure compliance and trust. The rollout was iterative, moving quickly from prototyping to production with ongoing feedback and collaboration across teams. 

The results were transformative. Engineers spent less time searching for information, leading to faster decisions and more innovation. AI agents helped identify design gaps and compliance risks early, improving quality and reducing errors. 

By using a retrieval-first architecture, the company saw a 7-15x ROI in engineering and gained momentum for wider adoption. By scaling Agentic AI, they set a new standard for intelligent, data-driven operations and pulled ahead of competitors. 

How Enterprises Can Replicate This Success 

Organizations ready to move beyond AI pilots can use this same proven framework, focusing on the workflow, the retrieval foundation, and the governance required for scale. 

1. Start small: choose a focused, high-value, well-defined business problem to tackle before rolling out to the entire organization. 
2. Invest in data readiness: 61% enterprises say their data needs improvement. Clean, connected, trusted data is essential.
3. Build a retrieval platform: RAG is foundational for accuracy, explainability, and security.
4. Apply governance: orchestrate, monitor, and manage agents with clear boundaries and data permissions.
5. Partner for success: organizations that work with experienced partners are twice as likely to succeed. 

Organizations that follow this blueprint are seeing real ROI and setting the pace for the next era of intelligent enterprise performance.