The FDA’s recent decision to cut its workforce by 20 percent has raised concerns across the biopharma industry, with experts warning of inevitable delays in drug development and approvals. Some companies are already reporting setbacks as they seek new approvals for clinical studies and commercial products. Adding to the uncertainty, the agency has announced an ambitious plan to integrate artificial intelligence tools across its operations, targeting full implementation by June 30 of this year.

In response, many biopharma companies are rushing to submit applications ahead of the deadline, hoping to sidestep potential delays during the transition to AI-enabled reviews. But while understandable, this scramble offers only a short-term fix. What lies ahead isn’t a passing disruption but a major shift in how drug and biologics approvals will be managed.

AI-assisted review is here to stay, and the companies best positioned to succeed will be those that adapt their submission strategies, operational workflows, and communication protocols to this evolving reality.

FDA’s AI push is about more than a staffing cut

It’s tempting to interpret the FDA’s 20 percent workforce reduction as a straightforward budget cut. In reality, it signals a broader operational transformation that leverages AI to automate labor-intensive tasks such as summarizing vast datasets, structuring complex content, and extracting key decision points from regulatory submissions.

The expected benefits are clear: faster review cycles, improved accuracy, and greater consistency in decision-making. And while the FDA has yet to release detailed information about its AI implementation partners – reportedly including OpenAI – or its operational frameworks, the direction is evident. Enough public signals have emerged to justify immediate strategic pivots from biopharma companies preparing for a fundamentally different review environment.

Those that act now to streamline processes, improve data organization, and anticipate new review dynamics will be far better positioned to navigate this shift and capture the operational advantages it creates.

What this means for submissions, timelines, and reviews

The operational consequences of this shift are already surfacing. Clinical trial timelines, approval forecasts, and market entry plans are coming under growing pressure as the FDA begins transitioning to AI-driven workflows. Faced with this uncertainty, biopharma companies are adopting one of three approaches.

The first, and most common, is to rush submissions ahead of the June 30 deadline in hopes of avoiding disruption. While this may buy short-term relief, it introduces serious risks, such as incomplete documentation, errors under deadline pressure, and missed opportunities to align submissions with the data structuring standards AI-assisted reviews will soon require.

The second approach is to pause new submissions while awaiting clearer guidance on AI review protocols. This conservative stance may avoid immediate complications but comes at a cost. Every delayed submission means postponed approvals, product launches, and revenue opportunities – an untenable proposition in a sector where each day of delay can translate to millions in lost market value.

The third, and most strategic, approach is to improve submission efficiency and data clarity. These companies are rethinking how regulatory documents are prepared, streamlining workflows, and ensuring content is well-organized, consistent, and easy for both human and AI reviewers to process. The payoff isn’t just smoother approvals, but long-term operational advantages through reduced regulatory friction, faster market access, and better protection of revenue streams.

In short, while waiting for clarity may seem prudent, the smarter course is active preparation. Regulatory timelines are tightening, but so are the tools to navigate them.

Building AI-optimized regulatory strategies

As the FDA integrates AI into its review processes, biopharma companies must fundamentally rethink submission workflows – not necessarily by deploying their own AI tools, but by focusing on efficiency, data consistency, and review readiness.

First, prioritize structure over narrative. AI systems – and human reviewers alike – process discrete, well-organized data far more effectively than lengthy narrative documents. Companies should modularize their content by breaking it down into structured sections, tables, and clearly labeled data fields. For example, safety data summaries are better presented in structured tables than in text-heavy paragraphs, reducing the risk of misinterpretation or missed details.

Second, minimize back-and-forth by anticipating standard review questions. AI-enabled reviews will flag inconsistencies, incomplete data, and atypical findings with little tolerance for ambiguity. Regulatory teams should identify and address likely AI-flagged issues within the initial submission. This might mean including expanded rationales for outlier data, providing additional context for trial deviations, or ensuring that every dataset aligns with expected FDA formats. The result is fewer delays and reduced need for iterative follow-ups.

Third, streamline document preparation and management processes. While submission formats like PDFs and structured reports may remain in place for now, expectations around how information is organized, validated, and delivered are tightening. Companies should focus on standardizing templates for common submission components, implementing consistent data validation checks before submission, and centralizing document management to reduce version control issues. These operational upgrades help regulatory teams move faster, minimize avoidable errors, and respond more efficiently to FDA queries, providing a critical edge as review processes become more data-driven and time-sensitive.

Final thoughts

Change is inevitable, even for an institution as traditionally methodical as the FDA. The agency’s move to integrate AI into its review processes signals a clear shift toward faster, data-driven decision-making. For biopharma companies, this means adapting submission strategies to meet higher expectations for efficiency, consistency, and review readiness.

While some uncertainty remains around implementation details, early adaptation offers the clearest path to maintaining agility and competitiveness in a changing regulatory environment.

By Lindsay Mateo, Chief Commercial Officer at Weave Bio, the only AI-powered, regulatory automation management platform for the entire lifecycle of a therapeutic candidate. Weave's cloud-based software streamlines regulatory workflows, ensuring content is accurate, structured, consistent, and aligned with FDA and global standards.