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TCS ADD™
TCS ADD™: A Suite of Modern and Open Technology Platforms
Highlights
- Pharmacovigilance (PV) teams have to manage high-volume, unstructured and time-sensitive safety data efficiently for their day-to-day work. AI agents act as practical force multipliers by assisting teams with this.
- Agentic workflows can support end-to-end case processing, including adverse event detection, data extraction, duplicate checks, coding, routing, and reviewer support.
- The greatest value in pharmacovigilance is through continuous data monitoring, earlier signal detection, and stronger benefit-risk evaluation.
- Human oversight is essential, with validation, audit trails, access controls, explainability, escalation rules, and human-in-the-loop review built into regulated workflows.
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How is PV evolving in the age of AI and data?
Since its genesis in the 1960s, pharmacovigilance (PV) has had one clear mission: to detect, assess, understand, and prevent adverse events and medicine-related issues. But both the available data and the PV operational environment have changed considerably over the years.
How can AI agents help PV teams manage growing data volumes?
The PV teams now manage high volumes of individual case safety reports, medical literature, call-center cases, social media notes, electronic health records, and real-world data. Much of this data and information is unstructured, time-sensitive, and challenging to review on a scale. This is where AI agents can become a practical force multiplier.
What tasks can AI agents perform in PV?
Unlike traditional automation, AI agents can understand the nature of information, prioritize cases, manage multi-step workflows, and use tools within guardrails. In PV, an agent can:
- Monitor call center notes and safety inbox
- Identify adverse events
- Extract case details
- Check for missing information
- Look for duplicates
- Generate a Medical Dictionary for Regulatory Activities (MedDRA) and enable coding of other medical dictionaries
- Route the case to a reviewer
If the case contains a potential signal, agents can detect patterns across large datasets and bring emergent risks to safety experts earlier. The product’s risk profile is a dynamic record that evolves through its market life, shifting as new signals are processed and integrated. AI agents can turn raw data into concreate alerts and label updates, protecting patient safety.
In medical and scientific literature surveillance, agents can screen publications, summarize relevant findings, and prioritize articles that need medical review.
How does agentic deployment in PV drive value?
The greatest value is not just expediting operational and PV processes. AI agents can enable PV transformation from a reactive model to a more proactive one. Continuous monitoring and early detection of safety signals and risk are key advantages of deploying agents for PV. By reducing repetitive and time-consuming manual work, agents can improve consistency and allow safety and PV professionals to focus on medical judgment, causality, clinical assessment, and benefit-risk evaluation.
The measure of success will not be the volume of tasks automated. Success will be determined by the degree to which AI-enabled PV strengthens decision quality, accelerates risk visibility, reduces operational burden, and enables safety teams to act earlier and with greater confidence.
What are the considerations to be kept in mind while deploying AI agents in PV operations?
AI agents should not be considered as autonomous medical decision-makers. In a regulated environment, such as PV, every agentic workflow needs validation, audit trails, access controls, performance monitoring, escalation rules, and human-in-the-loop review. Therefore, traceability and accountability cannot be compromised and are equally important as technical competence in agentic AI deployment.
Recent guidance from major regulatory agencies emphasizes that AI used for safety must be reliable, controlled, explainable, and fit for its intended use. The reflection document on AI to support regulatory decision-making for medical and medicinal products emphasizes a risk-based assessment approach. This includes a clear definition of the context of use, model risk, performance evidence, documentation, and human accountability when AI outputs may influence decisions about safety, effectiveness, or quality.
AI agents should adopt a responsible model, in line with regulatory guidance across development and post-authorization settings, including PV.
What does this mean for PV operations?
This means that AI agents should be implemented with governance, proven performance, traceable outputs, escalation pathways, audit-ready records, and human-in-the-loop review. In practice, regulatory confidence will depend less on the usage of AI and more on the reliability and explainability of the agentic workflows. Organizations should also be able to demonstrate that these agents are monitored and controlled throughout the lifecycle.
How should organizations scale AI agents responsibly in PV?
Pharmacovigilance is entering a critical inflexion point. Beyond automation, the life sciences industry will witness the emergence of intelligent, governed, and human-centered PV ecosystems in which AI agents augment the expertise of PV professionals. By combining AI-driven continuous surveillance with medical judgment and regulatory accountability, AI agents can help organizations move from reactive case management to proactive PV.
For leaders of the life sciences industry, the strategic question is more than just infusing AI into PV workflows. They should deeply ponder how agents can be adopted responsibly, at scale, and with the confidence of regulators, health care providers, and patients. This requires more than technical performance, since in today’s reality, anyone can build an AI agent. Successful agents require a disciplined operating model built on a clear context of use, risk-based validation, explainability, traceability, continuous monitoring, secure data practices, escalation pathways, and auditable human oversight.
The next generation of thinking PV systems, therefore, should be intelligent by design, compliant by default, transparent in operation, and meaningful in their contribution to patient safety.
The future of PV will not be AI replacing humans, but AI agents working alongside qualified safety professionals as trusted partners. This human-AI team should collaborate to protect patients and support intelligence-driven decisions. In this future, AI agents will become not just tools for efficiency but strategic enablers of safer products and more resilient PV organizations.