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June 3, 2026 
Artificial Intelligence in Regulatory Affairs: What’s Actually Changing, and What Still Isn’t
Years in pharma regulatory affairs teach you to ignore the corporate buzzwords and look for real results. “Digital transformation.” “Innovation ecosystem.” “Next-generation compliance.” The industry has heard plenty of these phrases that quietly dissolved into pilot projects and vendor slide decks.
So when artificial intelligence in regulatory affairs started generating serious attention around 2022-2023, a lot of seasoned RA professionals filed it under “wait and see.” Some of those same professionals are now trying to catch up. Because this time, something is genuinely different, and the gap between early movers and late adopters is widening faster than most organizations anticipated.
Here’s a number worth sitting with: a single Marketing Authorization Application or NDA represents anywhere from $800 million to $2.6 billion in accumulated R&D investment. And yet the processes used to compile, validate, and submit that dossier have, for decades, relied heavily on manual monitoring, human checklists, and regulatory writers grinding through brutal timelines with incomplete information.
Preparation for a typical Marketing Authorization Application (MAA) begins 18 to 24 months before the target submission date. Drafting and verifying a single clinical trial application alone can eat through hundreds of hours of coordinated cross-functional work. Layer on top of that a global regulatory landscape that has grown messier every year, more markets, more regional variations, more post-approval commitments, more guidance documents updated with less notice than anyone would prefer.
The professionals managing this are experienced, capable people. They were simply being buried under volume that human capacity alone can’t sustainably handle.
Regulatory affairs automation is addressing a problem that has been quietly grinding teams down for years.
AI for regulatory Submissions: The Work Before the Work
Talk to any regulatory operations team about where their hours go and a familiar story surfaces. A significant chunk of effort goes toward what might be called pre-submission hygiene: formatting documents to CTD structure, validating completeness against regional checklists, reconciling version histories, populating metadata. None of this requires deep regulatory expertise. All of it is time-consuming, error-prone, and mostly demoralizing for people who trained for years to do something more consequential.
AI for regulatory submissions has made this the first and most tractable target for automation. NLP-based tools now handle auto-tagging, metadata population, and semantic search across document libraries that previously required manual effort to navigate. Machine learning models classify submission documents, catch inconsistencies, and surface risk flags before a dossier reaches health authority review.
The more interesting development is what generative AI in regulatory affairs is doing with document authoring itself. Tools trained on CTD structures and historical submission data can now produce working drafts of nonclinical summaries, clinical overviews, and rationale sections. Research has found that LLM-generated clinical document summaries can match physician-written versions in completeness and accuracy while being produced up to 28 times faster.
What early adopters are running right now:
Post-market safety monitoring is, somewhat quietly, where AI in pharmaceutical regulatory affairs may be doing its most consequential work. The challenge here has always been volume. Adverse event reports arrive from dozens of channels simultaneously and the expectation is that safety teams will catch meaningful signals buried in that noise, quickly, and without inconsistency.
Anyone who has worked in pharmacovigilance understands the tension. Missing a signal carries obvious consequences, yet reviewing every report with the same level of scrutiny becomes increasingly difficult as product portfolios expand.
AI is helping organizations manage that reality. It can extract relevant information from documents, and surface potential safety signals for further evaluation. The final assessment still belongs to experienced safety professionals, but the path to finding what matters is becoming considerably shorter.
AI-driven regulatory intelligence means using machine learning to continuously monitor agency outputs – FDA Federal Register notices, EMA reflection papers, ICH guideline updates, health authority communications across 50-plus global markets and automatically surface what matters for a company’s specific portfolio. The alternative is having regulatory professionals manually track the same information across dozens of agency channels, which grows more untenable by the year.
The global AI in regulatory affairs market came in at approximately $16 billion in 2024 and is projected to reach $36.33 billion by 2034, expanding at a CAGR of 8.55%. The regulatory intelligence segment held the largest revenue share in 2024.
The size of the market matters less than what it signals. Organizations are no longer treating AI as an experiment. Budgets are moving from pilots to production systems.
Here is something genuinely worth paying attention to. The FDA launched its own agency-wide generative AI tool, internally called “Elsa,” in June 2025. By December of that year, the agency had deployed agentic AI capabilities to all employees and launched an internal “Agentic AI Challenge” to encourage staff to build their own AI applications. The CDER AI Council, stood up in 2024, now coordinates AI activity across the Center.
On the industry side, roughly 80% of top pharma companies told McKinsey’s 2025 regulatory benchmark that they were actively modernizing their RIMS infrastructure, with a meaningful subset having automated core submission processes well beyond basic writing tools.
Think about what this means practically. The reviewers evaluating your submissions are working with AI tools that may process and analyze content faster than before. Submission quality expectations are not going down just because your team is under more pressure.
The “Will AI Replace Regulatory Professionals?” Question Is a Distraction
Everybody is asking it. It deserves a direct answer: fixating on that question causes organizations to miss the actual strategic decision in front of them.
The areas most exposed to regulatory workflow automation are things like regulatory surveillance, repetitive document formatting, regulatory alerts, and regional submission localization. These are tasks consuming significant hours from people who can actually invest their time on more strategic work. Automating that workload was long overdue.
What AI cannot do is exercise regulatory judgment. Reading a Complete Response Letter and understanding what the agency is implying about their evidentiary threshold, not just what they’re explicitly writing, that’s a human skill developed through experience, institutional knowledge, and years of navigating health authority relationships. The value of a strong regulatory professional is in the interpretation, the strategy, the ability to anticipate what a reviewer will care about. AI gets the team to the starting line faster. It doesn’t replace what they do from there.
The FDA’s own deployment of Elsa ran into a problem that should register with everyone in this space. The agency’s head of AI publicly acknowledged that Elsa, like other large language models, produces false citations and hallucinated data with some regularity. Inside a regulatory submission, a hallucinated reference or a fabricated data point is a serious compliance event.
The EMA addressed this directly in its 2024 AI reflections paper, making clear that explainability and validation are non-negotiable for any AI operating in regulatory processes. Something we’re proudly doing here at Freyr GRI.
Any pharmaceutical regulatory automation platform touching submissions or safety workflows must maintain audit trails, support accountability, and allow for human review at every step that matters. The organizations handling this well treat AI outputs as drafts requiring expert scrutiny, not finished products.
Getting from “we know we need to act” to actual operational value requires some structural discipline:
Most regulatory teams are asking which AI use cases deliver measurable value and which are still overhyped. That’s precisely the problem Freya was built to solve.
If you’re curious what capable AI-driven regulatory intelligence looks like when it’s purpose-built for life sciences professionals, it’s worth spending some time with freya.intelligence.
freya.intelligence is an AI-powered regulatory intelligence platform designed specifically for regulatory teams across the life sciences industry – be it medicinal products, medical devices or consumer healthcare.
Try freya.intelligence with our FREE 14-day trial. A direct look at what well-built AI in regulatory affairs can do when your team actually needs it.
A closing thought worth holding onto: every meaningful operational shift in pharma, from eCTD standardization to risk-based monitoring, faced the same early skepticism from the people it eventually helped most. Artificial intelligence in regulatory affairs is following the same arc. The window for getting ahead of it rather than catching up is still open. Just not indefinitely.
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