The biopharma companies getting the most from AI right now are those that first prioritized making their content usable. Before turning on any AI capability, they structured their document environment, classified sensitive information, and established governance.
This sequencing — structure, classify, govern, then AI — is the strategy that separates useful AI from expensive chaos.
Key takeaways:
- 90% of the industry’s data is unstructured and locked inside documents, making it invisible to traditional AI systems
- Biopharma companies must establish a sound content foundation by structuring, classifying, and governing their data before deploying AI
- One Box life sciences customer provides a sound template for how to structure and govern content before applying AI
Eighty percent of your R&D data is invisible to AI
Phase III trials now average 5.9 million data points, reflecting a sustained 11% annual growth rate since 2020. Seven out of ten healthcare and life sciences organizations are now actively using AI, up from 63% in 2025. The same Nvidia survey found that these organizations are using AI for everything from data analytics to clinical decision-making.
These numbers describe an industry under enormous pressure to do more, faster, at lower cost, while handling tremendous amounts of sensitive data.
You might expect that most data in clinical trials and life sciences would be structured data that can easily be searched and applied to AI workflows. Yet roughly 90% of all content generated across the R&D function (and virtually every area of business) is unstructured — meaning it lives in PDFs, forms, email attachments, scanned documents, and other kinds of files that contain valuable information but can't be systematically sorted database-style. Files like investigator CVs, site feasibility questionnaires, clinical protocols, financial disclosure forms, and patient summaries contain valuable information, but accessing it remains a challenge. This is a document handling problem, and it’s the underlying problem inherent in AI architecture.
This is a document handling problem, and it’s the underlying problem inherent in AI architecture.
Biopharma has been investing heavily in AI, but those investments have been almost entirely on structured data applications: drug discovery, genomic analysis, predictive modeling, lab data. These are the right problems to work on, but they aren’t where the expensive delays are accumulating.
The document-heavy operational workflows that support these high-stakes efforts — study startup, site qualification, sponsor-CRO collaboration, investigator file management, and audit preparation — have been largely untouched by AI, and these workflows are where the real time and effort are expended, as manual processes compound across hundreds of sites and thousands of documents.
Bolting AI onto ungoverned content creates new risk
The initial reaction, when you see how much time is being lost to manual document handling, is to move quickly: find an AI tool, point it at your content, and see what happens. It’s an understandable instinct, but it rarely ends well.
Typically, one of two things happens.
First, the outputs are unreliable, because AI can't distinguish a current approved protocol from a draft, or a document intended for an external partner from one that was never meant to leave the building. Worse, you’ve now created a compliance problem you didn't have before.
Without classification and organization in place, sensitive documents that were previously protected by the friction of manual search become instantly accessible to anyone who asks the right question. AI makes it possible to surface almost anything with the right prompt, regardless of how deeply buried it is within folders.
In a GxP environment, that's a real compliance exposure. An unauditable AI interaction, content shared with the wrong external partner, or an AI response grounded in an outdated version of a protocol creates significant risk. The speed gained from quickly deploying AI is easily consumed by the subsequent risk management.
How one biopharma does it right
One Box customer, a clinical-stage biopharma now moving into commercial operations, has a single IT expert leading both technology and security. This is a dual role increasingly common at clinical-stage companies, where IT and information security are often managed by the same small team under budget pressure.
This particular company works with external contract manufacturing organizations and third-party partners, which makes content governance particularly consequential. A document shared with the wrong external stakeholder is a breach of a commercial relationship and a compliance issue in regulated industries, rather than just an internal problem.
Wisely, the team built a content foundation first, instead of rashly deploying AI and managing the consequences after the fact. Their approach had three stages.
The first stage was understanding the data. Before any classification or AI conversation, the team mapped what content existed, where it lived, and how sensitive it was. Box became their central collaborative repository for clinical documents, regulatory files, SOPs, marketing materials, and cross-functional communications. The work began with a clear picture of that environment.
The second stage was classification, applied as a trust mechanism rather than a restriction. The goal was to establish a model where content could be shared confidently with specific external partners because each document’s sensitivity was understood and labeled, instead of creating gates that slowed work. The right person could access the right documents, while unauthorized parties were kept out.
The third stage was distributing ownership. Rather than having IT manage classification decisions centrally, the framework gave legal, compliance, marketing, and operations teams the ability to adjust policies as their work evolved. Classification didn't require constant IT intervention to stay current; the people who understood the content owned the decisions about it.
The result was a content environment that was organized, governed, and ready. The actual AI model selection was largely beside the point; what mattered was that the company built an end-to-end process that solved the content problem first.
What the right AI foundation makes possible
With classified, governed content as the base layer, three things become newly possible for this biopharma company.
Speed. Automated metadata extraction compresses document review cycles from weeks to days. A study coordinator who previously scanned a site feasibility questionnaire line by line to extract investigator credentials, enrollment rates, and geographic details can now pull the data automatically and structure it into a dashboard with the help of AI. With a few hundred sites typical per trial, that compression multiplies across the entire program — earlier site activation, earlier patient enrollment, and earlier data readouts.
Insights. AI can now answer questions that previously required a full manual audit:
- Which sites have investigator licenses expiring in the next 60 days?
- Which document types are causing the longest review delays?
- What does a clinical protocol reveal about patient population eligibility for a specific site?
These questions were previously unanswerable because the data was locked inside documents that couldn't be queried, rather than because the questions themselves were difficult.
Compliance. Automated metadata extraction creates an always-current audit trail. AI-extracted and human-validated data maintains GxP integrity without adding manual burden. Inspection readiness becomes a continuous state, replacing the six-week scramble before an audit. The practical effect on the team: People reach the insights they need without sifting through pages of documentation. In a regulatory environment where those pages can number in the thousands, the difference between searching and knowing is the difference between weeks and hours.
How to nail the sequence of events
The argument here is specific:
- Structure your content
- Classify its sensitivity
- Establish governance
- Then deploy AI
And do this all in that order. It’s the only path to AI outputs that clinical, regulatory, and quality teams can trust and act on.
An AI pointed at an ungoverned content environment is a liability. An AI that surfaces the right document, in the right version, to the right person, within a governed and auditable workflow, is a genuinely useful tool. That’s why it’s important to start with your content foundation, not the AI model.
Know where your sensitive content is, and where it’s being shared and stored. Everything after that — the AI search, the metadata extraction, the automated workflows — gets dramatically easier when the content layer underneath it is sound.
Watch the on-demand keynotes from Box Vision 2026, including the keynote “Accelerating study startup: Streamlining clinical workflows.”
