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very week, nearly half of all Regulatory Affairs professionals lose two to three days just tracking regulatory changes before a single impact assessment is written, a single dossier is updated, or a single filing decision is made. Multiply that across multiple jurisdictions, each with its own language, cadence, and enforcement posture, and the math stops working. Headcount cannot scale at the rate complexity does.

Yet the answer is not simply layering Generative AI onto existing workflows. GenAI can summarize and draft, but it cannot orchestrate. What RA teams need is a system that detects a change, analyzes its impact, drafts the response, routes it for approval, and logs every step, autonomously, within governed boundaries. That system is agentic AI, and early adopters are already proving it works. Agentic AI enables regulatory intelligence, submissions, quality management systems (QMS), and pharmacovigilance (PV) workflows to operate as a single, orchestrated system, with qualified regulatory affairs, quality assurance, and qualified person (QP) personnel retaining full decision-making authority and accountability at every stage.

The Foundational Layer: GenAI’s Role in Modernizing Regulatory Affairs

The exponential growth in regulatory complexity demands more than traditional digital tools; it requires a fundamental rewiring of the Regulatory Affairs (RA) function.

According to a 2025 Deloitte survey, the majority of life sciences executives cite regulatory complexity as one of their top three operational risks. Across RA teams, workloads remain immense and processes are heavily manual. Surveys over the past two years indicate that nearly 45% of professionals spend two to three days each week just monitoring regulatory changes, a time-intensive activity amplified by shifting requirements, multiple languages, and persistent complexity. The result is resource strain, slower decisions, and missed market opportunities.

As regulatory complexity reaches a critical inflection point, with enforcement of the EU Artificial Intelligence (AI) Act approaching and regulatory guidance expanding globally, RA teams face a clear choice: continue manual monitoring, or adopt AI-driven intelligence. Against this backdrop, Generative AI (GenAI), advanced machine learning models that can generate, summarize, or reframe content from large data sets, has become a foundational capability. It automates intelligence gathering, surfaces emerging trends to preempt risk, and streamlines document review.

Key applications in Regulatory Affairs include:

• Automated Document Summarization
  • Transforming lengthy regulatory updates or guidance into concise intelligence reports for internal teams.
  • Producing initial drafts of SOPs, submission dossiers, and training content, significantly reducing repetitive writing tasks for regulatory teams.
• Predictive Compliance Monitoring
  • Using historical and real-time data to forecast regulatory risks, inspection readiness, and labeling changes.
  • Rapidly analyzing and summarizing complex regulatory documents, answering targeted questions, and comparing requirements across jurisdictions, eliminating time-consuming manual reviews through advanced large language models.
• AI in Quality Management Systems (QMS)
  • Supporting documentation generation and refinement, deviation management, and pharmacovigilance reporting.
  • Evaluating internal policies against new or updated regulations, pinpointing inconsistencies, and even proposing corrective actions to close gaps.
• Global Trade and Supply Chain Compliance
  • Interpreting requirements such as FDA’s Unique Device Identifier (UDI) and EUDAMED, and aligning labeling standards across regions.
  • Supporting Multilingual Compliance: Language models can translate and adapt regulatory content for different markets while preserving legal tone and context critical for global operations.
• Governance and Human Oversight
  • Embedding controls to align with the EU AI Act and other global frameworks that mandate risk-based governance and explainability.
• Predicting Regulatory Outcomes
  • Continuously monitoring global regulatory sources to detect changes and deliver timely alerts to ensure that teams stay ahead of evolving requirements.
  • By analyzing historical approval trends, AI can forecast potential decisions and highlight high-risk areas, enabling proactive planning and resource allocation.

However, GenAI alone is not the final destination. While it provides valuable insight, it cannot independently execute the complex, multistep workflows that define modern RA. To consistently transform insight into compliant action, organizations must progress to agentic AI, where software agents plan, execute, and adapt workflows under human oversight.

The Agentic Leap: From Proactive Insight to Workflow Orchestration

Agentic AI refers to goal-driven software agents operating within defined parameters that can perceive, reason, and act across connected systems. Unlike GenAI, which responds to prompts, agentic AI systematically orchestrates workflows to achieve predefined goals. This represents a fundamental shift from passive automation to proactive execution.

This evolution is not a distant future; it is happening now. Organizations adopting agentic AI are already reducing regulatory risk, improving audit readiness, and accelerating market access. The following real-world pilots illustrate the tangible impact.

Agentic AI Pilots in Regulatory Affairs: From Theory to Practice

The following pilot case studies are illustrative examples based on documented industry trends and published insights into AI-driven regulatory workflows. They reflect common themes reported by leading sources in agentic AI in life sciences, discussions on dossier automation, and multi-agent architectures for regulatory affairs. These examples synthesize observed benefits such as reduced turnaround times, improved accuracy, and enhanced governance, from real-world implementations described in industry reports and thought leadership articles. They also demonstrate how targeted agentic AI deployments are delivering measurable outcomes.

Pilot A: Global Labeling Change Orchestration

• Business Case: A midsize pharmaceutical company struggled with a 10-day average turnaround for impact assessments on labeling changes across 25 markets, resulting in a 12% rate of missed updates.
• Solution: An agentic AI workflow was deployed to monitor health authority websites, identify labeling-related updates, perform an initial impact analysis against the company’s product portfolio, and draft impact memos for human review.
• Guardrails: Role-based access controls, source provenance tracking, and a mandatory bilingual review for all machine-translated content.
• Primary Governing Regulatory / Quality Frameworks: This pilot operated at the intersection of several major regulatory and quality frameworks:
  • ICH Q9(R1) Step 4 document: Quality Risk Management (adopted January 2023): Governs the use of AI-derived risk scores and controls within quality risk management processes, including requirements for objectivity, subjectivity minimization, and risk-based decision-making. The AI impact-scoring logic in this pilot was designed in alignment with its four key improvement areas.
  • EU MDR (Regulation (EU) 2017/745) / EU IVDR (Regulation (EU) 2017/746) + MDCG 2025-6: Where the affected product portfolio included medical devices or IVDs, labeling changes and their impact assessments were subject to MDR/IVDR obligations on technical documentation, Instructions for Use (IFU), and post-market surveillance. The June 2025 MDCG guidance clarified that AI-assisted labeling review tools must meet transparency and human oversight requirements under the dual MDR/IVDR + EU AI Act framework.
  • FDA Labeling Guidance and PCCP: For US-market products, AI-assisted labeling change workflows are governed by FDA’s Predetermined Change Control Plan (PCCP) framework, which requires that any AI-driven modification to device labeling be documented, traceable, and communicated to users. The pilot’s version-control and memo-drafting outputs were structured to be compatible with PCCP disclosure requirements. Reference: FDA PCCP guidance.
• Results (6-month pilot):
  • Mean time to impact memo reduced by 40% (from 10 days to 6 days).
  • Missed updates fell to near zero (<1%).
  • Version-control errors in downstream processes decreased by 50%.

Pilot B: Automating Module 1 Dossier Assembly

• Business Case: A generics firm faced significant delays in assembling Module 1 dossiers (e.g., regional forms, cover letters) for submissions, with rework cycles consuming over 30% of the team’s time.
• Solution: An agent was trained to automatically populate country-specific templates with data from internal systems, assemble the required PDF documents, and package them for electronic submission (RPS/eCTD).
• Guardrails: Strict template validation, mandatory human signoff before finalization, and an exception queue for nonstandard requirements. Results (9-month pilot):
  • Module 1 assembly cycle time was reduced by 35%-45%.
  • Rework caused by administrative errors was cut by 30%.

The Next Evolution of the Regulatory Operating Model: Proactive Execution with AI

Agentic refers to the capacity to act independently, make choices, and control one’s own actions. Agentic AI takes this concept into the digital realm. Regulatory science has begun to formalize what this means in practice: the FDA–EMA Guiding Principles of Good AI Practice in Drug Development (January 2026) define AI systems using the OECD formulation as “machine-based systems designed to operate with varying levels of autonomy that may exhibit adaptiveness after deployment,” a definition that directly encompasses agentic architectures. The EU AI Act further operationalizes this by classifying autonomous AI systems according to risk tier, imposing proportionate obligations on transparency, human oversight, and lifecycle governance. ISO/IEC 42001:2023, the first international AI management system standard, provides the organizational governance scaffolding, covering AI risk assessment, impact assessment, and continuous improvement, within which agentic deployments should be structured. Its goal-driven capabilities are empowered to act on the organization’s behalf, executing actions with defined, human-approved parameters.

Agentic AI goes beyond passive automation; it operates within defined, governed boundaries to plan, execute, and adapt tasks. Here are some ways that it improves the above capabilities:

• Dynamic Workflow Orchestration: Instead of just answering queries, agentic AI can chain tasks: Detect a new regulation → run gap analysis → draft updated SOP → notify stakeholders. Chained agentic workflows that touch regulated outputs (SOPs, submission documents, labeling) are subject to GxP computer system validation (Reference: GAMP 5 | GAMP AI Guide). Any SOP or submission document generated or modified by the agent constitutes a regulated electronic record, triggering FDA 21 CFR Part 11 (US) and EU GMP Annex 11 (EU) requirements.
• Continuous Monitoring and Proactive Action: It can continuously scan regulatory portals, trigger alerts, and even initiate compliance updates without waiting for manual prompts. Autonomous regulatory monitoring that feeds into compliance decisions is squarely within the scope of ICH Q9(R1) Quality Risk Management.
• Context-Aware Drafting: Agentic AI can pull data from multiple internal systems (e.g., clinical trial databases, quality systems) to generate tailored submission drafts. When an agentic AI accesses clinical trial databases to generate submission content, it operates under several overlapping frameworks simultaneously: ICH M8/eCTD v4.0, to govern the structure, metadata, and traceability requirements of any content destined for electronic submission; the EMA Reflection Paper on AI is explicit that generative language models used to draft product information or submission documents must be used under close human supervision, with quality review mechanisms ensuring factual and syntactic correctness before regulatory submission; where clinical or patient-level data is accessed, GDPR (Regulation (EU) 2016/679) and equivalent data protection regimes impose data minimization, purpose limitation, and access governance obligations.
• Multilingual and Market-Specific Adaptation: It can automatically localize documents for different jurisdictions and schedule reviews with regional teams. GAMP 5 validation requirements apply to the localization agent itself as a GxP computerized system.
• Predictive and Prescriptive Insights: Beyond forecasting, agentic AI can recommend next steps (e.g., prioritize certain filings, allocate resources) based on risk scoring. Where AI-generated risk scores and recommendations influence regulatory decisions, including prioritization of submissions, resource allocation against compliance timelines, or signal escalation in pharmacovigilance, ICH Q9(R1) is the primary quality framework. The FDA–EMA Joint Principles of Good AI Practice in Drug Development reinforce this through Principle 2 (risk-based approach) and Principle 8 (lifecycle management), stating that the level of validation, oversight, and safeguards must be matched to the AI’s context of use and associated risk.

This is not just automation; it’s a fundamental shift toward efficiency and strategic value.

The efficiency and strategic gains described above operate within non-negotiable boundaries that organizations must design into every agentic AI deployment from the outset:

• AI agents are used to support regulatory and scientific work, not to replace formal judgment or accountability.
• All AI-generated outputs, including standard operating procedures, labeling, and submission documentation, are subject to documented human review and approval before being shared with health authorities.
• Any predictive analyses produced by agentic AI, such as assessments of approval likelihood or anticipated health authority actions, are intended for decision support only and are not considered definitive or regulator-endorsed predictions.

RA leaders don’t need to wait for enterprise-wide transformation. They can start by identifying one high-volume, repetitive workflow, like horizon scanning, dossier assembly, or labeling change monitoring, and pilot an agentic approach within a single product line or market cluster. Set a 90-day evaluation window with clear metrics: time to action, missed update rate, and team hours reallocated to strategic work. The organizations that embed agentic AI into their regulatory operating model today won’t just keep pace with complexity; they will turn it into competitive advantage. A targeted implementation is only the beginning. Scaling agentic AI demands a maturity roadmap, a multi-agent architecture, and governance designed for regulated environments. (The next article in this series will examine how to build all three.)

This article extends the August 2025 Global Forum discussion on agentic AI beyond clinical operations to the Regulatory Affairs (RA) function, “Next Steps in Artificial Intelligence: Agentic AI.”