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 (i.e., 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 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 AEs 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 adverse drug events 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, artificial intelligence (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 PV, 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 life cycle. The global PV automation market, valued at $3.03 billion in 2026, is projected to grow at a compound annual growth rate of 13.42%, reaching $5.68 billion by 2031.⁴ This growth is fueled by the rapid adoption of AI and machine learning (ML) platforms, which accounted for more than 45% of deployments in 2025.⁴

• Natural language processing (NLP): The fastest-growing technology segment in PV automation, NLP enables the rapid extraction of AEs 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
• ML and deep learning (DL): Increasingly deployed for seriousness assessment, signal detection, duplicate detection and expectedness coding, ML and DL algorithms analyze historical data patterns to lower false-positive rates and identify potential safety signals months faster than manual methods.
• Robotic process automation (RPA): Highly effective for automating routine, rule-based tasks such as mailbox monitoring, data entry, case booking and follow-up routing, RPA creates a seamless pipeline from unstructured data intake to structured database entry when combined with NLP.
• Agentic and generative AI: The most consequential development in PV automation, this technology integrates large language models to enable rapid synthesis of structured AE narratives from unstructured source documents, assist MedDRA coders with suggested preferred terms and support medical writing. Agentic AI — multiagent systems in which 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 European Medicines Agency (EMA) aligned 10 guiding principles for the responsible use of AI across the drug development life cycle.⁷ These principles emphasize a human-centric, risk-based approach, focusing on data governance, multidisciplinary expertise and transparent model development. They build on 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 PV 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 life cycle.

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 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use 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 to define and prioritize 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 platforms dominate the market. Organizations that prioritize architectural compatibility, application programming interface 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 road maps.

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 reengineering: 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 multitiered governance structures and maintain open communication channels from project inception. Proactive regulatory communication is essential. Sharing computer system validation documentation, audit trail architecture and human oversight protocols with health authorities reduces 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: Key Performance Indicators for the Automated PV Organization

Realizing the benefits of intelligent PV requires a disciplined measurement framework. Organizations that define key performance indicators 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

Although the trajectory toward automated PV is clear, several persistent barriers must be addressed to achieve widespread, global adoption. Midsized 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 and various national data localization laws complicate the deployment of unified, global cloud solutions. Multilingual NLP 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 PV?

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

AI and ML are uniquely suited to analyzing these massive, heterogeneous datasets. For example, ML algorithms can identify subtle, long-term AEs 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 AI is a foundational capability for high-performing PV 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 multimodal data — combining EHRs, wearables and scientific literature — PV will shift from a reactive reporting function to a predictive, precision-driven discipline. Guided by robust governance and human expertise, intelligent PV is launching a new chapter of impact, ensuring that the medicines of tomorrow are monitored with the speed and accuracy they demand.

Partha Anbil has more than 30 years of experience in life sciences and has held senior leadership roles at WNS, IBM, Booz & Company, Symphony, IQVIA, KPMG and PwC.

References

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