AI is only as reliable as the data at its foundation. As organizations adopt generative and agentic AI, data leaders must deliver correctness and dependability every time.

Real gains require AI‑ready data. Data that is high‑quality, trustworthy, and accessible, so pilots can scale. It begins with accessing and unifying data across clouds and on‑premises environments. Enterprise data is still underused in traditional LLMs, and up to 90% is unstructured, making readiness harder* . If AI can’t reach the right data, it can’t deliver reliable value.

Why data leaders must start with data access

Scaling AI beyond pilots often fails because the data isn’t AI-ready. The essential first step is holistic access to all enterprise data, structured and unstructured, so AI can draw from complete context.

Structured data is neatly curated and labeled in tables. Unstructured data like videos, reviews, invoices, contracts, and more holds rich signals but is harder to harness; both are vital to performance.

Limited access to unstructured data creates blind spots, duplication, and inconsistent governance. Creating unified, secure, and governed access to your data is a critical first step in building a robust data management strategy.

Connect data where it resides so AI‑ready data can be reused across many use cases. Broader access yields richer insight and greater automation potential across hybrid environments.

The hidden complexity behind data access

Data silos are the biggest barrier to accessing data. Without a centralized view, duplication and context gaps underpower AI. Security limits access, and heterogeneous estates create more silos.

Unstructured data shares those challenges and more. Unique issues include:

• Correlation: Connecting unstructured and structured sources to derive value
• Accuracy: Ensuring AI outputs are correct and unbiased
• Scalability: Preparing and governing unstructured data at scale

Vector‑based retrieval augmented generation (RAG) links models to external knowledge and excels at semantic search. It is a conventional approach to enabling AI with access to enterprise data. Yet conventional RAG is mostly internal and informational, relies on embeddings alone for unstructured retrieval, struggles to combine data types, and often fails to enforce source‑level permissions, creating governance and accuracy gaps.

To support operational, analytical, and external‑facing AI agents, organizations must overcome manual and brittle pipelines, low accuracy on complex semantics, and inconsistent governance. Intelligent integration, a lakehouse with extended data fabric, enrichment, governance, and hybrid retrieval across unstructured and structured data, helps bridge the gap.

Giving AI access to the right data — without starting over

Rushing into new initiatives like gen AI and agentic AI can create new data silos. A thoughtful data management strategy unifies access in a governed, secure, and strategic way, speeding time‑to‑value now and setting stronger outcomes later. Six principles guide making your data accessible and AI‑ready access:

1. Data management by design: Set a clear vision to align incremental decisions.
2. Maximize current investments: Refine rather than replace, integrating new capabilities.
3. Access in place: Increase interoperability instead of centralizing all data.
4. Flexibility: Choose architectures that adapt to varied scenarios.
5. Operational readiness: Keep a human in the loop to review and deploy responsibly.
6. Governance as a necessity: Provide semantic meaning through strong governance and quality.

Add structure to unstructured data

Connect to both structured and unstructured sources. Create structured derivatives from unstructured content and automate their generation to add context and improve accuracy.

Apply access controls and information restrictions rigorously, and ensure rules propagate downstream. Build toward unified management to support AI and analytics alike.

Data lakehouse with extended data fabric

Combine lakehouse access and optimization with data fabric ingestion and governance to ensure end-to-end access control. This approach breaks down silos, and supports multiple workloads with shared data.

Optimize for cost and performance to broaden workload coverage, offer tool choice, and prepare a gen‑AI‑optimized data estate.

Hybrid cloud by design

Most organizations run hybrid environments, so cloud‑only strategies can’t provide complete data access. Bringing analytics and intelligence to where data lives avoids unnecessary work and reduces risk.

An open approach

Taking an open approach to your data management strategy is vital to ensuring data access and AI success. Using open file formats lets applications reach data wherever it lives and avoids vendor lock-in, giving organizations the flexibility to unify data for AI without migrating it.

Conclusion

AI succeeds only when your data is AI‑ready—unified, governed access to structured and unstructured data across your hybrid estate eliminates silos, avoids unnecessary migration, and gives you the reliable foundation to scale. Adopt an open approach built on a lakehouse with extended data fabric to connect data in place, propagate source‑level permissions end‑to‑end, and reuse the same trusted data across workloads for faster, more accurate, lower‑cost outcomes.

Note: This article is a condensed version of the IBM ebook ‘Your AI can’t act on what it can’t access,’ with no new content added.

* Agentic AI has an unstructured data problem: IBM is unveiling a solution, Edward Calvesbert, IBM, 2025

The views and opinions expressed in this article are solely those of IBM, Eightbarcollective’s and do not reflect the views, positions, or policies of CDO Magazine.

About the Author:

Fariya Syed-Ali is the Global Product Marketing Leader for IBM watsonx.data, IBM’s hybrid, open data lakehouse for enterprise AI and analytics. With a decade of experience spanning product management, strategy consulting, and product marketing, Fariya is focused on helping organizations unlock the full value of their data to scale reliable AI agents with enterprise context.