AI Can’t Help You Until Your Data Can Help It

AI is everywhere. It’s reshaping workflows and sparking innovation across the enterprise. But for many organizations, AI is still struggling to deliver one thing: results.

Why?

Because the success of AI isn’t about the model you choose. It’s about the data you feed it.

More than 80% of enterprise data is unstructured, locked away in PDFs, handwritten forms, CAD files, and scanned documents. These formats were never built for machine learning. And yet, most AI initiatives today still depend on them to deliver meaningful insights.

Until you solve that, your AI can’t scale.

How Are Unstructured Documents Really Holding Back Enterprise AI?

Right now, unstructured documents are the single biggest obstacle to success.

Let’s break down what that looks like on the ground:

1. AI Models Choke on “Dirty” Data

Documents come in all shapes and formats (and from many different sources, like spreadsheets buried in email attachments). Feeding this raw content directly into an LLM leads to:

• Low accuracy due to garbled or incomplete inputs
• Hallucinated responses that erode trust in AI
• Inconsistent results across different document types and departments

2. Manual Work Slows Everything Down

Most organizations still rely on human staff to classify, extract, and verify document data. That means:

• Long turnaround times for decisions and approvals
• Skyrocketing operational costs and labor waste
• Critical data sitting idle in inboxes or file shares

3. Compliance Risks Multiply

Unstructured documents make regulatory adherence a nightmare:

• No clear audit trail of document handling
• Missed signatures, incorrect formats, or outdated policies
• High risk of violations during inspections or filings (FDA, SEC, GDPR, etc.)

4. Integration Bottlenecks

Even if your AI is world-class, your workflow isn’t if:

• AI tools can’t integrate with your ECM, PLM, or ERP systems
• There’s no automation between document intake and LLM ingestion
• Teams are re-keying data just to bridge system gaps

5. Missed ROI on AI Investments

Here’s the ard truth: many companies spend millions on AI infrastructure, only to see:

• Underwhelming results
• Low user adoption
• Long delays to ROI
  Because the content that AI relies on is still messy, manual, and unstructured.

The Takeaway

Enterprise AI can’t succeed without high-quality, structured data. But most organizations are still drowning in unstructured content. Adlib solves that, before and after the LLM gets involved.

Whether you're just starting your AI journey or trying to scale a stalled initiative, this session will show you how to make AI truly work for your organization.

👉 Reserve your seat now »

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