Why Data Readiness Is the Real Roadblock to Agentic AI Adoption

For years, the conversation around AI adoption has centered on cost: high implementation expenses, complex infrastructure, and the talent required to manage it. But that narrative is shifting. According to a recent study, data readiness has overtaken cost as the leading barrier to deploying Agentic AI.

Agentic AI systems, which can reason, plan, and act autonomously, hold incredible promise. But without a foundation of clean, connected, and trusted data, their potential is out of reach. Even the most advanced models can’t deliver meaningful results if the data they rely on is fragmented, siloed, or outdated. Organizations eager to embrace AI must first look inward, not at their budgets, but at their data.

What Is Agentic AI and Why Does It Matter?

Agentic AI is the next evolution of enterprise AI, moving beyond static automation to dynamic, goal-driven intelligence. Unlike traditional systems that execute predefined commands, agentic AI agents can make decisions, adapt to new information, and autonomously complete complex tasks based on high-level objectives.

This shift has the potential to transform how businesses operate, from streamlining workflows to enabling intelligent decision-making at scale. But to function effectively, these systems require continuous, real-time access to accurate and trusted data. Without a solid data foundation, even the most advanced agentic models will fail to act reliably or generate meaningful outcomes.

New Research: Data Readiness Now Trumps Cost Concerns

A recent study by Sinequa by ChapsVision, based on 100 enterprise decision-makers across the US and UK, found that:

• Only 10% of respondents now cite cost as the primary barrier to Agentic AI
• Instead, data quality and accuracy (19%) is now the top concern
• 61% say their data readiness still needs improvement
• Despite this, 66% expect ROI from Agentic AI within five years

Key insight: Companies feel ready for Agentic AI — but their data infrastructure says otherwise.

Why Data Readiness Is Essential for Agentic AI

For Agentic AI to function reliably, it needs:

• Clean, accurate data
• Secure access to sensitive and distributed sources
• The ability to retrieve information contextually and instantly

This goes far beyond traditional data cleaning. It’s about enabling intelligent, goal-driven systems to act confidently in real time. But the research reveals persistent challenges:

These obstacles make it difficult for AI agents to access the data they need, limiting scalability, accuracy, and real-world impact.

Intelligent Retrieval: A Critical Enabler for Agentic AI

Many enterprises already use enterprise search or intelligent retrieval systems — and these tools may now hold the key to enabling Agentic AI.

These systems are designed to:

• Search across disconnected systems and silos
• Deliver trustworthy, contextual results in real time
• Respect access controls and compliance requirements

The study found that 66% of respondents believe intelligent retrieval capabilities are key to overcoming Agentic AI implementation hurdles.

Additional benefits cited include:

• Improving data accessibility across the organization (46%)
• Enhancing AI model training through better data discovery (43%)
• Scaling to process high-volume data efficiently (42%)

Cost Is No Longer the Excuse

This shift in perception reflects a broader maturity curve in enterprise AI adoption:

• AI is no longer seen as a risky investment – 82% of companies say it already boosts intelligence and productivity
• Organizations now view AI as a long-term competitive advantage, not just a short-term expense
• The bottleneck is no longer budget: it’s infrastructure

As Jeff Evernham, Chief Product Officer at Sinequa by ChapsVision, explains:

“All the hype may be on GenAI, but if the data behind the model isn’t accurate, secure, and accessible, none of it matters. True business transformation requires sustained investment in data readiness and intelligent retrieval systems.”

The Path Forward: Focus on the Foundations

To prepare for Agentic AI, companies need to shift focus from AI models to the ecosystem around them, especially their data infrastructure.

3 Steps to Prepare Your Organization:

1. Audit your data readiness: Identify where data is siloed, outdated, or inaccessible in real time.
2. Invest in intelligent retrieval systems: Empower teams and AI agents to find trusted information instantly without IT bottlenecks.
3. Treat AI as a journey: Long-term ROI depends on continued investment in the foundational layers: data quality, governance, and knowledge discovery.

Final Takeaway

Agentic AI is coming, fast. But without strong data foundations, your AI investments will stall. Enterprise leaders who prioritize data readiness and intelligent retrieval today will be best positioned to unlock the full potential of autonomous AI tomorrow.

Sinequa helps organizations build that foundation with AI-powered search and retrieval, ensuring your data is accessible, trusted, and ready for the agentic future.

Want to learn more about the survey? Read the full Press Release ‘Cost Takes Back Seat For Agentic AI, as Data Readiness Becomes Biggest Hurdle’.