Much of the recent conversation around the FDA’s move to incorporate AI-assisted reviews has focused on what it means for approval timelines and submission requirements. But what’s equally important – and what we see increasingly across the industry – is how it’s forcing biopharma companies to rethink their own regulatory operations.
The reality is, submission volume and complexity have been rising for years. Many companies have quietly struggled with outdated, document-centric workflows that simply weren’t built to keep pace with today’s data demands. Now, with the FDA accelerating its digital transformation, organizations have no choice but to modernize – not just to match agency expectations, but to resolve long-standing inefficiencies in data management, submission preparation, and review readiness.
AI’s role inside biopharma regulatory operations
We’re already seeing biopharma regulatory teams deploy AI tools internally to improve how submissions are prepared, validated, and managed. In our view, this isn’t about defensive compliance; it’s about operational speed, consistency, and resilience in an environment that’s becoming more data-driven by the day.
Early AI applications – automating content structuring, tagging, and issue flagging – have already proven their value. More advanced capabilities like predictive analytics and scenario modeling are helping teams anticipate regulatory queries and adjust strategies in advance. The result: regulatory professionals can spend less time on repetitive, manual tasks and more time on higher-value strategic decisions.
The FDA’s AI adoption is raising the bar
The FDA’s AI review initiatives, on track for completion by June 30, are setting a new baseline. Submissions that once allowed for minor inconsistencies or ambiguous records now face AI systems with no tolerance for incomplete, misaligned, or unstructured data. Deviations won’t be set aside for clarification; they’ll trigger immediate flags and stall reviews.
And while the FDA has yet to release detailed AI protocols, the direction is clear. What we’re advising clients, and what forward-looking companies are already doing, is recalibrating their operational processes now to improve submission quality, reduce delays, and stay ahead of the curve.
Standardize and clarify submission content
The biggest operational bottleneck we see today comes from legacy workflows designed for human readers, not data-driven systems. Narrative-heavy documents and scattered spreadsheets slow down both internal reviews and automated processing. Teams should focus on presenting essential data – adverse event rates, safety summaries, manufacturing metrics – in clear, consistently labeled tables and structured summaries. This improves internal review efficiency and minimizes the risk of errors and inconsistencies that could trigger downstream issues.
Modernize submission workflow infrastructure
Even the best-prepared content loses value when it’s trapped in disconnected systems. In our experience, many regulatory functions still rely on fragmented document management environments, making it difficult to track, update, and synchronize submission components across multi-study filings. Investing in systems that support seamless data access, integrated validation tools, and efficient version control can materially improve operational speed, accuracy, and review readiness.
Use AI to validate submissions before regulators do
One of the smartest operational shifts we’ve seen is regulatory teams proactively running AI-driven validation checks on draft submissions. These tools flag data gaps, inconsistencies, and formatting issues before filing, based on patterns from past outcomes and agency trends. It’s essentially an internal QA process designed to preempt external delays – and it works.
Establish governance protocols for AI-assisted workflows
As AI tools become more embedded in regulatory functions, governance is critical. What we recommend – and what leading companies are already implementing – are governance frameworks that clearly define how AI systems are selected, trained, validated, and monitored. These protocols should outline decision thresholds requiring human oversight and maintain auditable records of system outputs and interventions. Well-managed AI workflows support internal compliance, increase operational transparency, and build trust with regulators.
Plan for what’s coming next
It’s important to recognize that AI-assisted review is only the starting point in a much broader shift toward regulatory digitalization. Regulatory frameworks are evolving toward real-time labeling updates, adaptive trial approvals, and more dynamic submission models – all of which will demand operational agility and well-managed data infrastructure. The companies investing in those capabilities now will be better positioned to navigate emerging requirements and avoid future operational bottlenecks.
Final thoughts
The transition we’re seeing in regulatory operations isn’t theoretical – it’s happening now. AI-assisted review is already reshaping how submissions are prepared, validated, and reviewed. And while much of the industry conversation has focused on how regulators will use these tools, the more immediate operational test lies within biopharma companies themselves.
In our experience, the organizations taking decisive action now to improve data clarity, modernize infrastructure, and strengthen validation processes aren’t just preparing for compliance. They’re positioning themselves to lead in a regulatory environment that’s becoming faster, more digital, and more demanding.
The opportunity is clear – and so is the risk of falling behind.
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.
By Lindsay Mateo, Chief Commercial Officer at Weave Bio
