The utilization of AI has permeated life sciences. AI investment in all industries reached more than $250 billion in 2024. By 2030, there are expectations the pharma industry alone will see a staggering $25.7 billion in AI investment. Even with these investments, pharmacovigilance (PV) continues to struggle with a foundational constraint. Critical safety data is dispersed across legacy systems that were never designed with interoperability in mind. Teams rely on deeply fragmented workflows that separate signal detection from the important contextual clues. As a result, many organizations operate from a reactive posture even while investing in advanced analytics.

PV teams constantly generate streams of data from clinical trials, spontaneous reports, literature surveillance and post-market monitoring. Each source holds part of the safety narrative, but few systems integrate them into a continuous view. While these tools can surface trends, their isolation hinders the ability to easily interpret them within a broader context. This gap slows decision-making and limits the true impact of AI.

The Limits of Case-Centric Thinking

Traditional safety operations revolve around case processing. Safety operations include collecting reports, validating fields and reviewing safety reports to determine whether an event meets reporting thresholds. While this approach ensures compliance, it locks professionals into a cycle of data reconciliation rather than data evaluation. 

Signal detection follows a similar pattern. After statistical thresholds are triggered, professionals manually assemble contextual evidence across disconnected systems. Professionals move between literature databases, sales exposure data and regulatory information to understand whether a signal represents an emerging risk. While each step in this process adds value, the handoffs between systems introduce delay. 

PV professionals already know that manual data entry is not the future of their industry. Modernized advances such as automated intake, structured extraction and intelligent routing already reduce administrative burden. The next phase of advancement involves moving toward true decision enablement. Rather than manually aggregating data, PV teams should receive curated packages that support contextual insights and highlight potential concerns.

Teams are turning to agentic AI models that can detect threshold breaches, query relevant databases and assemble comparator information before presenting it to a human reviewer. In this workflow, human professionals can leverage their expertise and remain accountable for the final determination. This changes the primary role of the professional from information gatherer to decision authority. 

Quality as a Prerequisite for Intelligent Automation

In an ideal world, all safety systems would have standard data structures, APIs and reliable interoperability, but this just isn’t the reality for safety professionals. In addition, the structured data in systems only tells a partial story with information hidden in the unstructured data.

Newer analytical systems and AI agents can extract and combine data sources of all types and structure these in the investigation and presentation stage. The onus should be on ensuring the AI agents have sufficient testing and governance that they can reliably and accurately perform their tasks and push the quality and consolidation problem to tools suited to the role.

This approach benefits organizations by removing the time-consuming and ultimately futile exercise of trying to standardize data while allowing a modular approach of adding data sources as needed by the organization.

Continuous Monitoring as the New Standard

When comprehensive data analysis can flow across a product’s full lifecycle, safety teams gain a fundamentally different view of risk. Periodic reviews can evolve into continuously flowing analytics, which can surface trends as soon as thresholds are met. This real-time perspective supports earlier intervention and more informed regulatory engagement.

Another benefit of continuous monitoring is stronger cross-functional collaboration. Teams that oversee quality, regulatory requirements and clinical development can leverage comprehensive insights instead of fragmented reports. This shared visibility transforms how teams interpret patterns, prompting earlier and more informed dialogue. Over time, this transparency builds confidence among regulators and partners who expect robust oversight.

Advancing Safety Without Compromising Control

The life sciences industry stands at a crossroads. Today’s AI-powered tools allow PV professionals to increase efficiency in their workflows. Yet, their value depends on architecture, data quality and thoughtful governance. Environments plagued by data fragmentation will hinder platforms that enable continuous oversight. Clinical research organizations that move deliberately toward modular, connected ecosystems will position themselves to detect risk earlier and act with greater confidence.

Patient safety will remain central to PV teams. By embracing programmatic approaches that unify data and embed analytics into daily workflows, PV teams strengthen their commitment to patients. As these data-driven capabilities mature, organizations can develop a more responsive and resilient safety model for the future of PV operations. The future of PV is not defined by automation alone, but by how intelligently data is unified to support accountable decisions.

Updesh Dosanjh is responsible for developing IQVIA’s overarching strategy regarding AI and machine learning as it relates to safety and pharmacovigilance. He has over 25 years of knowledge and experience in the management, development, implementation and operation of processes and systems within life sciences and other industries.