Our Take From Digi-Tech Pharma & AI: If you’re not preparing your documents, you’re not preparing for AI.

Our team just returned from the Digi-Tech Pharma & AI conference, where the conversations were razor-focused on one major theme: data is the lifeblood of pharma, and most of it is still a mess.

Yes, AI is at the top of every strategy deck. But if your data lives in PDFs, scanned documents, or handwritten forms, even the best large language model (LLM) won’t save you.

Let’s break down what we heard, from the mainstage to the 1:1s.

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1. Data Prepping Is the New Data Science

Most attendees weren’t talking about building LLMs, they were concerned about getting clean data into the ones they already have. Data Science leaders at large pharmas like Boehringer Ingleheim, AstraZeneca, and many others, are measured by how well they prepare, manage, and move data downstream.

But here’s the pain point: 80-90% of the content they work with is unstructured. LLMs struggle with effectively working with scanned consent forms, Excel printouts, lab notebooks, and submission components scattered across SharePoint, email, and QMS without proper pre-processing and cleaning of the data within.

And most of them are still processed manually.

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

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2. Manual Document Workflows Are Still the Status Quo

Despite the rise of digital systems like Veeva Vault and MasterControl, we hear time and time again that controlled copy generation, batch documentation, and regulatory submissions still rely on hand-stitching together Word files and PDFs.

One exec put it bluntly:

“We still have 30 people in a room combining PDFs for submission.”

This isn’t just inefficient. It’s risky, non-scalable, and incompatible with any serious AI strategy.

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3. AI Needs Clean Inputs, Not Just Clever Prompts

A recurring theme from speakers and attendees alike: garbage in, garbage out.

AI initiatives are stalling not because the models are bad, but because the data being fed into them is unstructured, inconsistent, or missing key metadata. Teams are spending months refining prompts only to realize the bigger issue: the documents aren’t AI-ready in the first place.

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4. Your Data Pipeline Will Make or Break Your AI Investment

From regulatory affairs to clinical trial operations, the success of AI projects hinges on robust preprocessing and validation of unstructured data. Conversations revealed some consistent obstacles:

• Low OCR accuracy on lab scans and handwritten notes
• Inconsistent formatting across submission docs
• Embedded objects and annotations that models can’t parse
• Lack of real-time validation checks

Teams are now realizing they need an AI-compatible middleware layer, not just to feed documents into LLMs, but to refine, validate, and route that data intelligently.

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

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5. Adlib Is Already Solving These Challenges

Adlib is quiet powerhouse solving the “last mile” in digital transformation:

• Ingesting from various sources and routing structured, clean content into Veeva, MasterControl, and legacy systems
• Assembling and validating for compliance submission-ready documents automatically
• Structuring unstructured documents for LLM ingestion and outputting clean JSON data formats
• Validating extracted content with Human-In-The-Loop workflows and routing it downstream for further approvals

We’re not here to replace your AI, we’re here to make it work by refining the inputs and verifying the outputs.

How Adlib’s Human-in-the-Loop Enhances AI Automation and Builds Trust >>

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Closing Thoughts: The Road Ahead for Life Sciences AI

The takeaway from Digi-Tech Pharma & AI? Pharma companies are ready for AI, but their documents aren’t. And until they fix that, AI won’t scale.

To get there, the winners will be the ones who invest in middleware that:

• Makes data searchable, structured, and clean
• Bridges QMS, RIM, eTMF, and LLM platforms
• Automates compliance while eliminating manual effort

Let’s call it what it is: AI enablement starts with document intelligence.

And Adlib is built for it.

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