The pharmaceutical industry stands at the threshold of a profound transformation. As AI systems evolve from assistive tools to fully autonomous agents, regulatory operations will fundamentally change. This is the future of regulatory affairs.
For decades, pharmaceutical regulatory affairs has been one of the most labor-intensive, expert-dependent functions in the industry. The preparation of a single regulatory submission can involve hundreds of professionals working for 18-24 months, coordinating across disciplines, geographies, and organizational boundaries.
Today's AI systems are beginning to assist with discrete tasks—extracting data from PDFs, suggesting document classifications, checking compliance against known rules. But this is just the beginning. The next evolution of AI will not merely assist; it will operate autonomously as an agentic system.
This article explores the trajectory from assistive AI to fully agentic AI in pharmaceutical regulatory operations, examining the technological foundations, organizational implications, and transformative potential of this inevitable evolution.
Agentic AI refers to artificial intelligence systems that can pursue complex goals autonomously with minimal human intervention. Unlike traditional AI that responds to individual prompts or queries, agentic AI systems can:
1. Goal-Directed Autonomy
Given a high-level objective, the system determines its own approach, breaks down tasks, and executes multi-step workflows without step-by-step human guidance
2. Dynamic Planning
Ability to create, adjust, and optimize execution plans based on emerging information, obstacles, or changing requirements
3. Tool Use
Capability to invoke external tools, APIs, databases, and systems as needed to accomplish objectives
4. Self-Correction
Recognition of errors or suboptimal approaches and autonomous adjustment of strategy without human intervention
5. Long-Context Coherence
Maintaining consistency and logical coherence across extended workflows spanning days, weeks, or months
In the context of regulatory affairs, an agentic AI system wouldn't just extract a table from a PDF when asked. It would understand that a Module 2.3 Quality Summary requires stability data, locate the relevant stability studies across the organization's document repositories, extract and format the data appropriately, cross-check against ICH guidelines, generate compliant narrative text, create cross-references, validate the output, and integrate it into the submission—all from a single high-level instruction.
The journey toward agentic AI in regulatory affairs progresses through distinct stages, each building on the capabilities of the previous.
Rule-based systems and simple automation
Impact: Minimal - primarily administrative time savings
Machine learning and early LLM applications
Impact: Moderate - 30-50% time savings on specific tasks, human-in-the-loop required
Multi-step workflows with human collaboration
Impact: Substantial - 70-80% time savings, humans focus on oversight and strategy
End-to-end autonomous regulatory operations
Impact: Transformative - 90%+ automation, humans shift to pure strategy and governance
The emergence of agentic AI in regulatory affairs depends on several converging technological capabilities:
Large language models with enhanced reasoning capabilities (e.g., chain-of-thought, tree-of-thoughts, constitutional AI) can break down complex regulatory requirements into actionable steps.
Example Application:
An AI system receives the instruction "Prepare Module 3.2.P.8 for FDA NDA submission." It autonomously: (1) retrieves ICH M4Q guidance, (2) identifies required stability data points, (3) locates relevant stability studies, (4) extracts data in compliance with 21 CFR guidelines, (5) generates narrative following FDA preferences, (6) creates cross-references, (7) validates output, and (8) flags uncertainties for human review.
Modern AI systems can invoke external tools, APIs, and databases dynamically based on task requirements. This capability—known as function calling or tool use—is fundamental to agentic behavior.
Regulatory Applications:
Agentic systems require sophisticated planning algorithms that can decompose high-level goals into executable sub-tasks, manage dependencies, and adapt to changing conditions.
Techniques:
Regulatory submissions span thousands of pages across hundreds of documents. Agentic AI systems must maintain coherent understanding across this vast information space.
Enabling Technologies:
As agentic AI systems mature, they will transform every aspect of regulatory operations:
Complete end-to-end preparation of regulatory submissions with minimal human intervention
Autonomous Workflow:
Projected Impact: 18-24 months → 2-4 weeks for initial draft
Real-time monitoring and proactive adaptation to regulatory landscape changes
Autonomous Workflow:
Projected Impact: Zero latency between regulation change and organizational awareness
Simultaneous optimization of submission content for multiple regulatory agencies
Autonomous Workflow:
Projected Impact: Parallel global submissions instead of sequential regional filings
Anticipating and preventing submission deficiencies before filing
Autonomous Workflow:
Projected Impact: Reduce Refuse-to-File and major deficiency rates by 60-80%
Autonomous management of post-approval variations, supplements, and renewals
Autonomous Workflow:
Projected Impact: Near-zero regulatory staff time for routine variations
While the vision of fully agentic AI in regulatory affairs is compelling, significant challenges remain:
Concerns
Path Forward
Challenges
Mitigation Strategies
Risks
Solutions
Human Factors
Change Strategies
As AI systems become increasingly autonomous, the role of human regulatory professionals will not disappear—it will transform. The future regulatory professional will focus on:
Determining optimal regulatory pathways, agency selection, and market entry strategies based on business objectives rather than document preparation
Establishing validation frameworks, monitoring AI system performance, auditing outputs, and ensuring compliance with evolving AI regulations
Addressing unprecedented regulatory scenarios, first-in-class products, and complex scientific/ethical questions beyond AI training data
Building and maintaining relationships with regulatory authorities, participating in pre-submission meetings, and navigating political/cultural nuances
Analyzing AI performance data, identifying improvement opportunities, and guiding evolution of regulatory AI systems based on real-world outcomes
Aligning regulatory strategy with R&D, commercial, medical affairs, and manufacturing functions to ensure organizational coherence
In the agentic AI future, regulatory professionals will shift from execution to orchestration, from creation to curation, and from documentation to direction. The skills that will matter most are strategic thinking, judgment, creativity, stakeholder management, and ethical reasoning—precisely the capabilities that remain uniquely human.
Based on current technological trajectories and regulatory industry adoption patterns, the evolution toward fully agentic AI in pharmaceutical regulatory affairs will progress through distinct phases:
Assistive AI becomes mainstream
Multi-step autonomous workflows
End-to-end submission automation
Autonomous regulatory operations
The evolution from assistive to agentic AI in pharmaceutical regulatory affairs is not a question of if, but when. The technological foundations are being laid today. The business case is overwhelming. The competitive pressure is mounting.
Organizations that embrace this transformation early will gain substantial advantages: faster time-to-market, lower operational costs, higher quality submissions, and the ability to scale regulatory capacity without proportional increases in headcount. Those that resist will find themselves at a growing disadvantage as agentic AI systems become the industry standard.
The path forward requires thoughtful implementation—balancing innovation with validation, automation with oversight, efficiency with quality. But the destination is clear: a future where AI agents handle the vast majority of regulatory execution, freeing human expertise to focus on strategy, judgment, and the uniquely human elements of regulatory affairs.
Key Implications for the Industry
The future of regulatory affairs is being built today. Stay ahead of the transformation with insights, tools, and technologies designed for the agentic AI era.