Making unstructured data AI ready: Unlocking value for GenAI and agents

The rise of GenAI and agentic systems has unlocked new possibilities for enterprises; however, most still face a significant blind spot: unstructured data. IDC estimates that 80–90% of enterprise data is unstructured, yet much of it remains untapped. Without enrichment and governance, this content isn’t AI-ready making it difficult for models to retrieve and reason with accuracy. The result is hallucinations, inefficiency and missed opportunities to scale AI with confidence.

What’s new: Unstructured AI

Collibra Unstructured AI makes unstructured content AI-ready by transforming contracts, presentations, transcripts and emails into governed knowledge assets. It uses automated enrichment, semantic tagging and high-accuracy search to convert raw, disconnected files into structured, AI-ready inputs for GenAI and agentic workflows.

With intelligent enrichment pipelines and scalable governance, enterprises can finally operationalize unstructured data. This ensures that AI systems are fueled with accurate, context-aware information—delivering reliable insights and outcomes from day one.

How Unstructured AI helps

Unstructured data is rich with business insights but nearly impossible to mobilize in its raw form. Scattered sources, duplication and lack of metadata create inefficiency and brittle AI systems. Other challenges include:

• Poor discoverability across repositories
• Duplicate and outdated content in AI pipelines
• Lack of semantic metadata for context and reasoning
• Accuracy degradation in enterprise AI search
• Slow, manual preparation for AI projects

How Unstructured AI works

Collibra Unstructured AI introduces three core capabilities to operationalize unstructured data at enterprise scale:

• Smart Discovery: Automated workflows scan large repositories and apply semantic tagging to surface the most relevant content for each AI use case. This reduces manual hunting and accelerates time-to-insight

• Automated Semantic Layer: Manual taxonomy building is replaced with automated enrichment pipelines that generate business-specific metadata structures. This semantic backbone ensures GenAI systems interpret and retrieve content accurately
• High-Accuracy Enterprise AI Search: By enriching files with consistent semantic metadata, Unstructured AI ensures precise search results across thousands of documents—enabling reliable retrieval-augmented generation (RAG) and agentic workflows

Why metadata matters

Metadata elevates AI performance from good to great. It enables intelligent pre-filtering, routing and enhanced embeddings that reduce hallucinations and boost accuracy in RAG and agentic tasks.

Why you should be excited

Unstructured AI empowers diverse roles across the enterprise to harness the full potential of their data. From data scientists to business leaders, it provides tailored solutions that enhance accuracy, streamline workflows and drive confident decision-making:

• Data Scientists: Gain reliable access to enriched, context-rich unstructured data for GenAI and RAG
• AI Engineers: Reduce noise in pipelines by ingesting only metadata-enriched, governed content
• Business Leaders: Scale AI with confidence by turning hidden data into strategic assets
• Knowledge Workers: Trust AI answers backed by accurate retrieval and consistent metadata
• Data Platform Engineers: Automate the ingestion and enrichment of unstructured files at scale, reducing manual tagging and accelerating integration with downstream systems

Key use cases

Unstructured AI helps across numerous use cases, such as:

• AI input governance: Automate the validation and enrichment of unstructured data used in AI pipelines to ensure responsible AI adoption, reduce regulatory risk and enforce metadata standards across the AI lifecycle
• RAG and generative AI optimization: Use semantic metadata to improve Retrieval-Augmented Generation (RAG) systems, enhancing retrieval accuracy through routing strategies and embedding enhancements
• Enterprise search: Build structured datasets from unstructured files to support high-accuracy search applications and prevent knowledge search degradation when scaling to thousands of documents

Key takeaways of Unstructured AI

Collibra Unstructured AI turns the biggest enterprise blind spot into a competitive advantage. By enriching and mobilizing unstructured content with metadata, organizations enable GenAI and agentic systems to perform reliably at scale.

Join Collibra’s Product Premiere to:

• Understand how metadata enrichment boosts AI accuracy and reduces hallucinations
• See how Smart Discovery and semantic tagging accelerate unstructured data readiness
• Learn how metadata-driven enrichment provides a scalable foundation for enterprise AI
  

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  Teresa Rojas

  Teresa Rojas

  Senior Director of Product Management, AI Governance

  Collibra

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  Leonard Platzer

  Leonard Platzer

  Director, Software Engineering

  Collibra

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  David Talaga

  David Talaga

  Product Marketing Director

  Collibra