In more than two decades of working with global CROs, pharmaceutical companies, and biotech organizations, I have watched pharmacovigilance teams absorb wave after wave of increasing complexity. More products on the market. More data sources to monitor. More regulatory jurisdictions to satisfy. And for most of that time, the response has been the same: hire more people, outsource more work, add more manual steps.
That approach has reached its ceiling. The WHO’s global safety database, VigiBase, now holds more than 40 million individual case safety reports. The FDA’s Adverse Event Reporting System reflects a similar trajectory. Meanwhile, the data sources safety teams must track have expanded well beyond spontaneous reports to include electronic health records, social media, published literature, and real-world evidence from wearables and claims data. Case processing backlogs, inconsistent coding, delayed signal detection, and regulatory submission errors are no longer exceptions. They are the operating reality for organizations still running pharmacovigilance on legacy workflows.
The technology to address this has been available for years. Machine learning, natural language processing, robotic process automation, and other intelligent automation tools have been technically viable in pharmacovigilance for some time. What has changed, and what makes this moment different, is the regulatory environment.
A Regulatory Convergence Without Precedent
Three developments between late 2024 and early 2026 have fundamentally altered the regulatory posture toward AI and intelligent automation in pharmacovigilance.
First, the FDA issued its inaugural draft guidance in January 2025, the agency’s first comprehensive framework addressing AI across the drug lifecycle, including post-marketing safety. The FDA explicitly requested feedback on whether additional guidance is needed for AI in pharmacovigilance, signalling this as a priority area. Final guidance is expected in the second quarter of 2026.
Second, the European Medicines Agency published its Reflection Paper on the Use of AI in the Medicinal Product Lifecycle in late 2024, with practical guidelines for integrating AI into pharmacovigilance. The EMA has since deployed its own tools for automated signal adjudication, literature screening, and adverse reaction data extraction. The regulator is operationalizing intelligent automation, not merely writing about it.
Third, CIOMS published its Working Group XIV report on AI in Pharmacovigilance in December 2025, the first internationally aligned, consensus-based framework for AI in drug safety. CIOMS XIV establishes seven governing principles, including risk-proportionate oversight, human accountability, and lifecycle governance, and provides implementation guidance that treats AI systems in pharmacovigilance with the same rigor applied to medicinal products. In January 2026, the FDA and EMA jointly released ten Guiding Principles of Good AI Practice in Drug Development, reinforcing transatlantic alignment.
For the pharmacovigilance community, this convergence represents something genuinely new: a credible regulatory scaffold for adopting AI and intelligent automation. The ambiguity that once made adoption a compliance risk is being replaced by documented expectations and regulatory precedent.
What Changes When These Tools Work Well
The technology applications in pharmacovigilance are well established at this point. NLP extracts structured data from unstructured adverse event reports. RPA handles data entry, deduplication, and case triage. OCR converts scanned source documents into machine-readable formats. Machine learning identifies disproportionate reporting patterns and flags emerging safety signals. Generative AI is being applied to narrative generation, MedDRA coding, and literature review at scale. These are no longer pilot-stage capabilities.
But what I find is often missing from the conversation is what actually shifts in a safety organization when intelligent automation is implemented well. The answer is not just speed. It is focus. When case processors are not spending their time on data entry and routine triage, they can concentrate on the complex medical assessments and causality evaluations that require human judgment. When signal detection runs continuously across heterogeneous data sources, safety scientists can move from reacting to signals after the fact to identifying risk patterns before they become widespread. When data quality assurance is automated through cleaning, deduplication, and standardization, every downstream function benefits from more reliable inputs. The discipline shifts from firefighting to strategic risk management.
Why Waiting Is No Longer a Neutral Choice
The EU AI Act, which entered into force in August 2024, reflects a broader regulatory trend toward greater oversight of AI in healthcare. As requirements around governance, transparency, risk management, and human oversight continue to take shape, including through the ongoing Digital Omnibus on AI simplification process, organizations that begin laying the foundations for responsible AI adoption today are likely to be better positioned to adapt to evolving expectations in pharmacovigilance.
And adoption is not something that can be rushed. It requires validated workflows, qualified datasets, change management across safety operations, and documented model performance, all of which take time to build properly. The CIOMS XIV framework explicitly recommends a phased, risk-proportionate approach: start with high-volume routine tasks like case intake and literature screening, build governance maturity, then extend to signal detection and predictive analytics.
Where I See Companies Getting Stuck
In my experience, the organizations that struggle with AI adoption in pharmacovigilance rarely fail on the technology. The tools work. Where they stall is in three specific areas.
- First, governance without ownership. Companies build an AI governance framework on paper, but no single person or team has clear accountability for how AI systems perform in production. Safety decisions still require human oversight, and when the line between AI recommendation and human sign-off is blurry, organizations default to manual override on everything, negating the efficiency gains they set out to achieve.
- Second, starting with the wrong use case. The impulse is often to lead with signal detection, which is the most analytically impressive application but also the hardest to validate and the most sensitive from a regulatory perspective. Organizations that succeed tend to start with case intake automation or literature screening, where the volume is high, the risk profile is manageable, and the governance lessons transfer directly to more complex applications.
- Third, treating implementation as a technology project rather than an operations transformation. AI in pharmacovigilance changes workflows, role definitions, and the daily work of safety professionals. Without change management that brings safety teams along from the start, even well-designed systems end up underused or abandoned.
The regulatory window is open. The frameworks are published. The technology has been proven. What remains is organizational commitment, and a realistic view of where the hard work actually lies. For pharmacovigilance leaders, the cost of waiting is now measurable in processing backlogs, in compliance risk, and in missed safety signals that a well-governed system would have surfaced months earlier.
Author Bio
Ranga Gontina serves as the Global Business Unit Head for Life Sciences at Apexon, an AI-powered digital transformation services firm. He focuses on AI-led business growth and human-centric digital experiences. With more than 22 years of dedicated experience in the life sciences sector, Ranga has partnered with global CROs, pharmaceutical, biotech, med-tech and healthcare organizations to drive digital transformation through innovative technology solutions. His core competency lies in understanding complex industry challenges and delivering business solutions by leveraging cutting-edge technology solutions that drive operational efficiency, compliance, enhance research and patient outcomes and accelerate growth for life sciences organizations.
Ranga has built and mentored high-performing teams, led multi-million-dollar engagements, and consistently delivered GTM solutions, partnered with hyper-scalers and SAAS platforms, aligning technology solutions against critical business objectives. He is passionate about leveraging data, cloud platforms, AI and digital health tech to transform how life sciences companies operate and innovate.
Industry Focus: Portfolio leadership in Healthcare & Life Sciences across CROs, Pharma, ISVs, Providers, and Payers.
An executive with hands-on experience, he has fostered long-term partnerships with CXOs, executive leaders, technology partners, and industry alliances. His expertise in Business Transformation and Modernization has been manifested in Connected Health, Decentralized Clinical Trials, Telemedicine, Consumerism and Digital Experience Platforms.
