Introduction
The hidden opportunity in your unstructured data

Every enterprise is racing to build generative AI capabilities. But most are overlooking the very thing AI depends on: high-quality inputs.

The spotlight today is on models—what they can generate, how they perform, which vendor to choose. But behind every LLM, there’s a simple, unglamorous truth: models are only as good as the data they’re trained and grounded on. And most organizations are feeding their models incomplete, unlabeled and siloed information.

This is especially true for unstructured content; in other words, the documents, contracts, emails, PDFs, meeting transcripts, policy memos and the nearly infinite variety of digital files that are part of business operations in today’s digitized world. There’s no way around it: They are carriers of institutional knowledge and, often, invaluable intellectual property. Collectively, they represent the connective tissue that holds the nuance, decision logic and business context that AI systems need to deliver useful outputs. And yet they remain hidden, unindexed and mostly ignored by existing governance processes.

Closing the gap with governance

The paradox is striking. Unstructured data makes up 80 to 90% of enterprise data, but less than 12% of those assets are ever reused in decision-making. That’s according to analysts tracking data productivity. Most of this content sits in disconnected file shares, email archives, SharePoint folders or old content management systems, and they’re completely invisible to the people and systems that could benefit from them most.

This is a strategic gap. In today’s AI-first world, unstructured data is the fuel for the modern AI stack, especially generative AI and retrieval-augmented generation (RAG). When enriched with context and governed at scale, it becomes a powerful source of insight, speed and differentiation.

Treating unstructured content as knowledge products—searchable, secure, explainable and connected—requires a shift in mindset. More than mere storage or archiving. Businesses must make this content usable by AI, discoverable by employees and trusted by regulators. The truth is that it’s the foundation for scaling responsible AI.

And it starts with governance that doesn’t stop at tables, columns and dashboards, but extends to every file, folder and format in your data estate.

What is a knowledge product?

A knowledge product is the equivalent of a data product but in the unstructured world.

Roughly what percentage of your unstructured data can you actually use for AI today?

<5%
5-20%
20-50%
I have no idea