How Automation and AI Transform Pharmacovigilance

Pharmacovigilance (PV) has never been more consequential—or more operationally strained. The traditional model of manual adverse event (AE) case processing—receiving reports by phone or email, hand-keying data into safety databases, performing duplicate data entry, and relying on human reviewers to monitor inboxes—has become unsustainable.

The FDA’s Adverse Event Reporting System (FAERS) logged more than 2.1 million potential safety signals in 2023, a staggering increase from approximately 780,000 in 2011.¹ Globally, the volume of medical literature and real-world data continues to expand exponentially. The World Health Organization estimates that 134 million adverse events occur each year in hospital settings alone, contributing to 2.6 million preventable deaths annually. ² In the United States, recent data analysis suggests that ADEs are now the third leading cause of death, accounting for an estimated 250,000 fatalities each year. ³

Against this backdrop, the case for PV automation is not merely compelling; it is existential. Processing a single AE case manually is resource-intensive, error-prone, and expensive. For large biopharmaceutical companies managing tens of thousands of cases per month, case processing activities can consume up to two-thirds of the overall PV budget. Furthermore, manual surveillance often misses critical safety signals buried in unstructured data.

However, the PV automation market has matured dramatically. What were once isolated pilot programs evolved into enterprise-grade, regulatory-validated platforms. By 2026, cloud-native, AI-centric safety platforms are transforming raw, disparate data into cohesive, actionable insights with unprecedented speed and accuracy.

This article explores the current state of intelligent automation in pharmacovigilance, examining the technologies driving change, the evolving regulatory landscape, and the strategic pillars required for successful implementation.

What Is the Automation Technology Landscape like In 2026?

The PV automation ecosystem spans several principal technology categories, each suited to distinct steps in the AE lifecycle. The global pharmacovigilance automation market, valued at USD 3.03 billion in 2026, is projected to grow at a compound annual growth rate (CAGR) of 13.42%, reaching USD 5.68 billion by 2031.⁴ This growth is fueled by the rapid adoption of artificial intelligence (AI) and machine learning (ML) platforms, which accounted for over 45% of deployments in 2025.⁴

• Natural Language Processing (NLP): is the fastest-growing technology segment in PV automation. It enables the rapid extraction of adverse events from unstructured sources such as medical literature, electronic health records (EHRs), and clinical notes. Medically tuned AI systems have demonstrated the ability to reduce literature review time by 88% to 92%, while boosting accuracy beyond 96%. ⁵
• Machine Learning and Deep Learning: algorithms are increasingly deployed for seriousness assessment, signal detection, duplicate detection, and expectedness coding. By analyzing historical data patterns, ML models can lower false-positive rates and identify potential safety signals months faster than manual methods.
• Robotic Process Automation (RPA): remains highly effective for automating routine, rule-based tasks such as mailbox monitoring, data entry, case booking, and follow-up routing. When combined with NLP, RPA creates a seamless pipeline from unstructured data intake to structured database entry.
• Agentic and Generative AI: represent the most consequential development in PV automation. The integration of large language models (LLMs) enables rapid synthesis of structured AE narratives from unstructured source documents, assists MedDRA coders with suggested preferred terms, and supports medical writing. Agentic AI—multi-agent systems where AI components manage subtasks autonomously—is emerging as a powerful tool for continuous, high-throughput processing of global safety data. ⁶ Nearly 73% of global pharmaceutical organizations are actively planning or deploying agentic AI by 2025–2026. ⁶

Navigating the Regulatory and Ethical Frontier

As AI adoption accelerates, global regulators are actively shaping the framework for its responsible use. The regulatory community's posture is supportive of innovation but demands rigorous validation, transparency, and human oversight.

In January 2026, the FDA and EMA aligned ten guiding principles for the responsible use of AI across the drug development lifecycle. ⁷ These principles emphasize a human-centric, risk-based approach, focusing on data governance, multidisciplinary expertise, and transparent model development. They build upon the FDA's 2025 draft guidance on AI to support regulatory decision-making and the EMA's earlier reflection papers.

Concurrently, the Council for International Organizations of Medical Sciences (CIOMS) Working Group XIV published a comprehensive international framework for AI in pharmacovigilance in December 2025.⁸ The CIOMS report outlines seven core principles: a risk-based approach to oversight; human oversight distinguishing between human-in-the-loop and human-on-the-loop systems; validity and robustness in real-world settings; transparency and explainability; data privacy by design; fairness and equity to mitigate algorithmic bias; and governance and accountability throughout the AI lifecycle.

For PV leaders, this environment dictates that any AI system influencing a regulatory submission must be explainable—not merely accurate. Black-box models that cannot articulate the rationale behind a classification are not fit for regulatory use. Furthermore, new ICH E2D(R1) and M14 guidelines, effective March 2026, mandate structured electronic formats, reinforcing the need for validated, interoperable systems. ⁹

What Are the Three Pillars of Successful Implementation?

Despite the clear business case, translating automation ambition into operational reality requires strategic discipline. Research into successful PV automation programs—such as Sanofi's Project ARTEMIS, which aims to cut operating expenses by 50% by 2027 while managing 700,000 cases annually¹⁰—consistently surfaces three defining pillars.

Pillar 1: Selection and Implementation Strategy

The most consequential decision a PV leadership team makes is defining and prioritizing the specific problem they are solving. Successful organizations begin with a clearly articulated vision connected to enterprise objectives, such as compliance acceleration or cost-to-serve reduction. In 2026, cloud-native, GxP-validated Software-as-a-Service (SaaS) platforms dominate the market. Organizations that prioritize architectural compatibility, API interoperability, and regulatory jurisdiction coverage achieve materially better outcomes than those focused solely on feature checklists. Furthermore, vendor relationships are shifting from transactional procurement to co-innovation partnerships, ensuring alignment with long-term AI integration roadmaps.

Pillar 2: Business Operational Model Transformation

Automation should not simply accelerate existing processes; it should fundamentally change what processes exist. The highest-value implementations begin with rigorous process re-engineering—mapping current-state workflows, identifying non-value-added activities, and eliminating them before applying automation. Process mining tools provide data-driven visibility into actual case-managing patterns, identifying prime automation candidates. Crucially, this transformation redefines the human-AI partnership. As routine tasks are automated, PV case processors migrate from data entry roles toward quality review and AI model oversight. PV scientists and physicians are freed to focus on benefit-risk assessment, complex signal management, and proactive pharmacoepidemiology. This reallocation of intellectual capital is precisely what regulators, and patient advocates have long sought.

Pillar 3: Communication and Training

Technology implementation failures in PV are rarely caused by the technology itself; they stem from inadequate stakeholder alignment and training gaps. Successful organizations establish multi-tiered governance structures and maintain open communication channels from project inception. Proactive regulatory communication is essential. Sharing Computer System Validation (CSV) documentation, audit trail architecture, and human oversight protocols with health authorities reduce inspection risk and builds agency confidence. Training programs must also be redesigned. Staff no longer need to master manual data entry; they must understand AI model logic, recognize potential hallucinations or biases, and confidently manage edge cases. Human experts remain the final authority on regulatory decisions, ensuring scientific integrity.

Measuring What Matters: KPIs for the Automated PV Organization

Realizing the benefits of intelligent pharmacovigilance requires a disciplined measurement framework. Organizations that define key performance indicators (KPIs) before implementation and track them rigorously consistently outperform their peers. Industry benchmarking reveals the profound impact of best-in-class automation platforms on core PV metrics.

Overcoming Barriers to Global Adoption

While the trajectory toward automated pharmacovigilance is clear, several persistent barriers must be addressed to achieve widespread, global adoption. Mid-sized biopharmaceutical companies and regional affiliates often struggle with the initial capital investment required for enterprise-grade AI platforms. Legacy safety databases—many of which have been heavily customized over decades—present significant integration challenges. Migrating historical data into new, structured formats requires meticulous mapping to preserve regulatory history and ensure data integrity.

Furthermore, data privacy and cross-jurisdictional compliance remain complex hurdles. Regulations such as the European Union's General Data Protection Regulation (GDPR) and various national data localization laws complicate the deployment of unified, global cloud solutions. Multilingual natural language processing models must also be continuously trained to mitigate bias and accurately interpret medical terminology across different languages and regional clinical practices.

To overcome these barriers, industry consortia and regulatory bodies are fostering greater collaboration. Initiatives aimed at standardizing data formats, sharing anonymized safety datasets for model training, and developing open-source validation frameworks are lowering the barrier to entry. As technology vendors increasingly offer modular, scalable solutions, even smaller organizations can begin their automation journey with targeted, high-impact use cases before expanding to end-to-end platforms.

What Is the Role of Real-World Evidence in Modern Pharmacovigilance?

The expansion of real-world evidence (RWE) is a major catalyst for intelligent pharmacovigilance. Traditional spontaneous reporting systems capture only a fraction of the drug safety picture. By integrating data from electronic health records, claims databases, patient registries, and even wearable devices, pharmacovigilance teams can construct a more comprehensive and continuous safety profile for marketed products.

AI and machine learning are uniquely suited to analyzing these massive, heterogeneous datasets. For example, ML algorithms can identify subtle, long-term adverse events or rare drug-drug interactions that might not become apparent until a drug has been on the market for years. This capability is particularly critical for advanced therapies, such as cell and gene therapies, where long-term safety monitoring is both a regulatory requirement and a clinical necessity.

What Is the Future of Drug Safety?

The integration of artificial intelligence is a foundational capability for high-performing pharmacovigilance teams. By embracing intelligent automation, the biopharmaceutical industry can directly address the mounting pressures of data complexity, regulatory scrutiny, and operational efficiency. The ultimate goal of these technologies is not merely cost reduction, but the enhancement of global patient safety. As AI systems evolve to fuse multi-modal data—combining EHRs, wearables, and scientific literature—pharmacovigilance will shift from a reactive reporting function to a predictive, precision-driven discipline. Guided by robust governance and human expertise, intelligent pharmacovigilance is launching a new chapter of impact, ensuring that the medicines of tomorrow are monitored with the speed and accuracy they demand.

Partha Anbil is senior vice president, life sciences, at Coforge Limited.

References

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Disclaimer: The views expressed in the article are those of the authors and not of the organizations they represent.