AI in Regulatory Affairs: Potential, Promise, and Path to Real Impact
The life sciences sector is no stranger to digital transformation, but few domains have remained as complex, human-intensive, and risk-averse as regulatory affairs. From clinical trial submissions to post-approval updates, regulatory functions are effectively the connective tissue between the science and the patient.
Now, as AI technologies mature, the potential to reshape this critical discipline is coming into sharper focus – at a time when the future of healthcare demands a more personalised and agile approach to medicine leveraging greater technological and scientific innovation.
Even though the social media “data-jungle” fuels a massive flood of data, the promise is clear: faster submissions, higher-quality data, earlier insights, and better compliance whilst positioning regulatory affairs to meet the expectations of next-generation healthcare.
But genuine transformation will depend not just on the technology itself, but on the cultural, regulatory, and organisational shifts that enable it to succeed.
The Opportunity: AI Across the Regulatory Lifecycle
AI can add value across every phase of regulatory activity — before and after product approval. Some illustrative examples:
Pre-approval applications include:
Automated dossier assembly and submission preparation — NLP and document intelligence tools can extract, standardise, and format data for submission to agencies, dramatically reducing manual effort.
Regulatory intelligence — AI systems can continuously monitor global regulatory updates, guidance changes, and precedent decisions, providing near-real-time insights into evolving requirements.
Predictive modelling — Machine learning can identify patterns from historical approvals, helping to anticipate regulator questions, optimise study designs, or flag potential compliance risks earlier in the process.
Post-approval applications include:
Pharmacovigilance and signal detection — AI algorithms can scan vast volumes of real-world data, social media, and patient reports to detect safety signals faster and more accurately than manual review.
Label management and variations — AI can help track and update regulatory changes across multiple jurisdictions, ensuring product information remains consistent and compliant.
Regulatory analytics and performance management — Advanced analytics can provide insights into submission timelines, agency interactions, and internal process efficiencies.
Taken together, such applications could accelerate time-to-market, reduce regulatory risk, and enhance global compliance, allowing regulatory professionals to focus on higher-value strategic work and decision-making.
Why Progress Has Been Slow
Despite this clear potential, adoption has been cautious, and in many organisations, superficial or non-existent. Inertia has been governed by three main types of barriers: cultural, regulatory, and industry-driven.
Cultural barriers
Regulatory affairs has always been built on human judgement, interpretation, and accountability. Many professionals still view AI tools as black boxes that cannot fully capture nuance, context, or precedent — particularly in areas where the cost of error is potentially very high.
There is also a strong “if it isn’t broken, don’t fix it” mindset. Regulatory teams are rightly risk-averse, and the absence of clear validation frameworks for AI systems often reinforces that conservatism.
Regulatory and governance barriers
Ironically, the regulators themselves are still defining their own stance on AI. While the FDA and EMA have begun publishing discussion papers and pilots on AI/ML in drug development and quality assurance, there is limited guidance on AI used within regulatory operations.
Questions remain around:
· How should AI-generated content be validated or audited?
· What constitutes acceptable transparency or explainability?
· How is accountability maintained if an AI-assisted process makes a critical error?
Without regulatory clarity, many companies hesitate to embed AI deeply into official processes.
Industry barriers
The life sciences industry itself remains highly fragmented in its data and systems. Much of the information used for regulatory work sits in disconnected silos — clinical, safety, CMC, quality — often managed in PDFs, legacy systems, or local repositories.
AI thrives on structured, connected data; regulatory affairs, by contrast, is often built on static documents, not data.
That structural mismatch makes it difficult for even the best AI tools to deliver their full potential, without a concerted effort to ensure that data repositories are “AI ready”.
The Path Forward: Building Trust, Structure, and Collaboration
For AI to realise its potential in regulatory affairs, evolution is required:
a) Movement from documents to data.
Organisations will need to invest in data-centric regulatory information management — structured content, interoperable systems, and machine-readable metadata. AI will struggle to deliver value if the inputs remain static documents.
b) Human-in-the-loop design.
AI adoption will succeed only when it augments, not replaces, expert judgement. Designing workflows where regulatory professionals can validate, correct, and learn from AI outputs builds both trust and accountability.
c) Regulatory agency engagement.
Companies should work proactively with agencies to help shape pragmatic frameworks for AI use in submissions and post-market processes. Early pilots, sandbox initiatives, and pre-competitive collaboration, accelerated by seamless data interchange, can accelerate acceptance.
d) Cultural readiness.
Leadership needs to reframe AI not as automation but as augmentation — a tool that enhances regulatory quality and insight, freeing experts from manual document tasks to focus on strategy, interpretation, and innovation.
The Investor’s Perspective
For investors and service providers, this area represents a significant emerging opportunity.
Regulatory affairs accounts for a meaningful share of life-sciences operating cost and lies directly on the path to successful approval and beyond, yet it remains one of the least digitised functions. The companies that can successfully combine domain expertise, regulatory data infrastructure, and AI-driven automation will have strong, defensible positions in a market increasingly desiring efficiency and speed, in order to meet the future needs of an increasingly specialized healthcare industry.
However, progress will need to be evolutionary, not revolutionary. The winners will be those who understand both the promise of the technology and the pragmatism of the culture, balancing innovation with the deep trust and transparency regulators demand.
The Outlook
AI will not replace regulatory professionals, but it will change the nature of their work and enable them to cope with the steadily rising flood of information.
By 2030, we can expect regulatory affairs to look markedly different: data-driven, predictive, and integrated across the product lifecycle, saving cost and meeting the demands of a rapidly evolving healthcare system.
Getting there will require smart capital, thoughtful governance, and sustained change management. But for those willing to navigate the barriers, the potential to transform one of the industry’s most important and complex functions is real — and well worth pursuing.