AI Is Coming for Every Industry. But Unstructured Data Might Stop It in Its Tracks.

80–90% of enterprise data is unstructured. Until that’s fixed, your AI strategy is stuck in neutral.

Across industries (from life sciences and energy to manufacturing) leaders are racing to unlock value from artificial intelligence. AI promises faster decisions, streamlined operations, and company-changing insights.

But there’s a hidden bottleneck derailing even the most ambitious AI initiatives: unstructured data.

And it’s not just a small hurdle. It’s the obstacle.

Getting ready to lift the heavyweight of Unstructured Data: How AI helps you lift it off the ground

Is Your ECM Smart Enough for AI?‍

The Unspoken Truth About AI Readiness: Your Data Isn't Ready

AI doesn't fail because the models are bad. It fails because the inputs are messy.

In highly regulated industries, most data lives in the shadows: scanned documents, CAD drawings, handwritten notes, emails, PDFs with inconsistent formatting, and contracts full of embedded objects and tables.

Here's what enterprises are dealing with:

• 80–90% of enterprise data is unstructured, and less than 20% is actively managed​
• Manual workflows introduce 12–18% error rates, driving up operational risk​
• Compliance failures cost global enterprises millions annually in fines and delays​
• OCR and capture tools fail to extract meaningful information from 30–40% of documents​

Meanwhile, AI initiatives demand clean, contextual, structured data. Not chaos.

So while IT and innovation teams are building GenAI pilots and exploring RAG-based search, their models are struggling. Why?

Because they’re working with noisy, inconsistent, non-standardized data. And that’s a recipe for hallucinations, bias, and untrustworthy outputs.

Unstructured Data: The Biggest Culprit Behind AI Hallucinations (The Important Stuff)

And here is why:

1. Messy Inputs = Messy Outputs

Large Language Models (LLMs) like GPT-4 or Claude don’t actually “understand” data. They generate outputs based on the context they’re given. If that input is noisy, incomplete, or inconsistent (like most unstructured data), the model is forced to guess.

And when AI guesses, it hallucinates.

2. Unstructured Data Lacks Consistency

Most unstructured documents (emails, PDFs, handwritten notes, CAD drawings, scanned forms) have:

• Inconsistent formatting
• Non-standard terminology
• Embedded images or objects
• OCR errors or missing context

When LLMs ingest this content without preprocessing, they can misinterpret layout, skip over key information, or misclassify data points leading to fabricated or incorrect answers.

3. Context Is Fragmented or Lost

LLMs rely heavily on surrounding context to generate accurate responses. Unstructured documents often scatter critical details across paragraphs, tables, and attachments. Without intelligent chunking or structure, models miss context so they fill in the blanks.

For example:

• A missing label on a table might cause an LLM to misinterpret a dosage value in a clinical report.
• A contract missing page numbers might cause the model to misattribute a clause.

4. Low-Quality Inputs Increase Token Use and Decrease Precision

When documents aren’t preprocessed, LLMs consume more tokens to "understand" the content, often without clarity. This not only drives up costs, but increases the chance the model pulls the wrong info into its response.

More data ≠ better context if the data is disorganized.

5. In Regulated Industries, This Is a Non-Starter

In highly regulated sectors, hallucinations are dangerous.

• In pharma, a hallucinated adverse event in a clinical trial summary can derail an approval.
• In finance, hallucinating a clause in a loan document can trigger legal and compliance action.
• In energy, hallucinating maintenance data could put safety at risk.

Without a preprocessing layer to structure, validate, and enrich unstructured content, LLMs are left to make risky assumptions and the business pays the price.

Regulated Industries Have A Lot at Stake

If you're in pharma, finance, government, or manufacturing, the consequences of flawed AI outputs can be potentially catastrophic.

• Life sciences firms risk failed audits, compliance flags, and FDA resubmission costs.
• Energy companies must adhere to strict reporting standards across environmental, safety, and maintenance records.
• Insurance carriers face increasing volumes of documents with shrinking teams and rising regulatory scrutiny.
• Government agencies need to digitize decades of records while redacting sensitive content—without compromising transparency.

In every case, unstructured data is the blocker that delays innovation, slows automation, and undermines trust in AI.

Adlib Solves The Unstructured Data Challenge

Adlib sits between your unstructured content and your AI engine, cleaning, structuring, and validating everything before the model even gets called.

Adlib is the AI-enabled document automation platform trusted by the world’s most compliance-conscious organizations. It takes your chaotic, unstructured document ecosystem and transforms it into a high-quality, AI-ready data pipeline.

How?

Watch our Product Manager walk you through Adlib's process of AI-Enabled Data Extraction from Structured and Unstructured Documents >

Enterprise-Grade Preprocessing: Adlib supports 300+ file types (from CAD to email to Facebook comment) and uses multi-layer OCR, image cleanup, object separation, and intelligent chunking to prepare documents for downstream LLM processing.

Structured, Validated Outputs: Whether you’re feeding content into RAG pipelines or extracting key data from contracts and forms, Adlib ensures it’s clean, accurate, and validated, minimizing hallucinations and maximizing reliability.

Automated Workflows at Scale: With drag-and-drop workflow builders, you can route documents through classification, extraction, human-in-the-loop validation, and final delivery, automatically and compliantly.

Regulatory-Ready Architecture: Watermarking. Redaction. Audit trails. PDF/A conversion. Adlib is built from the ground up to meet SOC2, HIPAA, FDA, GDPR, and other global compliance mandates.

AI Interoperability On Your Terms: Adlib integrates with any LLM (OpenAI, Claude, Gemini, private models) allowing you to select the engine (or multiple) that meets your security and performance needs.

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Join the Webinar Series: AI-Ready Documents at Scale

If you’re serious about AI transformation, you can’t ignore the elephant in the room: your unstructured data problem.

That’s why we’re hosting a new webinar series: AI-Ready Documents at Scale. We’ll show how organizations across life sciences, insurance, government, and manufacturing are using Adlib to:

• Convert millions of unstructured files into structured, AI-consumable assets
• Feed context-rich content into LLMs for Intelligent Data Extraction
• Automate document workflows end-to-end: from capture to classification to routing
• Reduce hallucinations and risk while boosting AI model performance

This series is for leaders who understand AI is only as good as the data it’s fed.

👉 Reserve your seat now and learn how to turn your document chaos into AI-ready intelligence.

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AI Needs Clean Fuel. Adlib Delivers It.

Your enterprise doesn’t have a model problem. It has a content problem.

Fix that, and AI becomes your most powerful advantage.

Let’s get your data (and your organization) ready for it.