The FDA Has an AI Tool. What About Everyone Else?

AI Is Transforming the FDA. The Rest of Biotech Isn’t Ready

The FDA’s recent announcement of Elsa, its in-house generative AI tool, is a pivotal moment for regulatory innovation. Elsa is already being used to “accelerate clinical protocol reviews, shorten the time needed for scientific evaluations, and identify high-priority inspection targets” [1]. In other words, the agency is serious about upgrading its own workflows, and it’s doing so with custom-built AI.

But this creates a new imbalance. While the FDA is accelerating its review processes, most of the industry isn’t equipped to keep pace with faster expectations for submission readiness. Many biotech companies, especially early-stage teams, are still stuck in manual, fragmented workflows. Delays begin long before the FDA ever sees a document.

The North American clinical documentation market is only just starting to modernize and is projected to grow to $13.8 billion by 2028, largely driven by the shift to AI-native platforms. Still, most early-stage biotechs have not even begun that transition.

A Faster FDA Won’t Fix a Broken Pipeline

The real problem isn’t at the point of review. It’s upstream. While the FDA is investing in automation to maintain capacity with a smaller workforce, most biotech companies are still navigating fragmented, outdated systems that slow them down long before anything reaches a regulator. There is mounting pressure across the industry to cut steps and eliminate friction throughout the drug development process, but most biotech operations are not built for that. They still rely on disconnected tools: protocols in Word, development reports in Google Docs, assay data in Excel, and different templates for each partner. Teams spend weeks reconciling versions and formatting files to meet regulatory standards.

To put it plainly, it’s a mess. And it’s expensive. In a recent pilot discussion with Cytodyme, a biotech company shared that they had considered hiring consultants just to unify and validate their submission documents. The cost was so high that they paused the effort, delaying their Investigational New Drug (IND) application by months. This application is required to obtain FDA approval for the initiation of clinical trials. Without it, companies can’t start testing new therapies in people.

Delays like this aren’t uncommon, and they are rarely due to negligence. Instead, they reflect the complexity of managing scientific data across fragmented tools and teams. For scientists preparing an IND, even small issues like misaligned assay data, missing traceability, or untracked protocol changes can create weeks of rework. Each new touchpoint adds friction, slows progress, and increases costs.

These are the upstream inefficiencies that FDA-level AI cannot solve. Elsa helps the FDA move faster and operate with fewer resources, but the system only works if the rest of the industry can keep up. When the organizations submitting to the FDA are equipped to generate structured, submission-ready documents, the entire development pipeline moves more efficiently. Regulatory innovation like Elsa is a critical step, but its impact depends on an industry-wide shift toward automation, interoperability, and smarter documentation practices.

Why This Matters Now

Clinical development is already under strain. There are more than 6,000 drug candidates in global pipelines, each requiring massive volumes of documentation [2]. Trial complexity, recruitment delays, and regulatory friction all stretch timelines and inflate costs, even before a single file reaches a reviewer.

Meanwhile, the market is beginning to respond. The Clinical Documentation Integrity (CDI) market is projected to reach $13.8 billion by 2028, growing at 16.5 percent annually [3]. Larger companies are investing in document intelligence platforms that embed regulatory logic and reduce rework. At the same time, many biotech teams are still formatting Word files by hand. Even enterprise CDMOs continue to manage critical data and workflows in spreadsheets.

Building Infrastructure for Automation

This isn’t about making better templates. It is about changing the way documentation works from the ground up.

At Cytodyme, we are not building another editing tool. We are building an AI-powered documentation platform designed for biotech teams that need to move fast and stay compliant. Rather than replacing systems like LIMS (Lab Information Management Systems) or ELNs (Electronic Lab Notebooks), Cytodyme integrates across them to generate structured, submission-ready documents without manual rework.

That means:

• Structuring documents from the outset, not retrofitting them later
  
  

• Enforcing regulatory logic across every step of authoring
  
  

• Automated versioning for contributors and external partners
  
  

• Generating outputs that are ready to submit, not ready to fix

This includes protocols, method validations, development reports, and tech transfer packages, formatted in machine-readable structures like Markdown, XML, or HTML. These formats retain context and logic, making it easier for both humans and AI to review, validate, and update. 

Research shows that large language models perform more accurately and consistently when working with well-structured formats like these. For example, the FOFO benchmark (short for Format-Following) evaluates how well LLMs understand and generate structured content. It found that models are significantly more reliable when inputs follow clear, standardized formats [4]. The result is documentation with the level of accuracy and traceability that regulatory reviewers expect. That’s why Cytodyme is building documentation infrastructure that supports these formats and meets the technical and regulatory demands of modern biotech.

The Regulator is Evolving. The Rest of the System Has to Follow.

The FDA is using AI to accelerate its workflows, but unless biotech companies, CDMOs, and CROs modernize alongside it, the full potential of AI-driven transformation will go unrealized.

Elsa helps regulators interpret documents more efficiently. Cytodyme ensures those documents are structured for AI, compliant, and ready for review.

Approvals are only one step. Collaboration drives innovation; tomorrow’s breakthroughs start with smart documentation.

Sources

[1] FDA (2025). FDA launches agency-wide AI tool to optimize performance for the American people. Link

[2] Frost & Sullivan (2025a). Growth Opportunities in Global Biotech Investment. [Private Database]

[3] Frost & Sullivan (2025b). Frost Radar™: Clinical Documentation Integrity in North America, 2025. [Private Database]

[4] Xia, J. et al. (2024). FOFO: A Benchmark for Format-Following in Large Language Models. Link