There is an unwavering dedication to patient safety across the pharmaceutical and biopharmaceutical industry. This focus is embedded in every stage, whether in reliable manufacturing of established treatments or in the development of new therapies. Effective monitoring, detection, and resolution of safety concerns address not only regulatory compliance but also commercial success.

Inaccurate or inconsistent reporting, overlooked signals, or delays can result in costly setbacks that ripple across development timelines to delay market access and erode long-term trust. Executives tasked with commercialization should keep safety top of mind. It is a strategic imperative, one that shapes cost efficiency, safeguards reputation, and, most importantly, creates a lasting competitive advantage.

The data challenge in modern pharmacovigilance

The constant stream, scale, and diversity of safety-related data has surpassed the reach of traditional monitoring methods. Everything from call center transcripts, social media posts, chatbot interactions, adverse event reports, and feedback from third-party vendors is directed toward global safety operations. Yet, without advanced tools, much of this critical data remains unanalyzed. The results of these missed insights could mean delays in intervention, increases in risk exposure, and additional costs across the commercial lifecycle.

The importance and complexities of clinical data challenges come at a time when development and commercialization costs are reaching unprecedented levels. According to Deloitte industry estimates, the average cost to develop a drug was $2.2 billion in 2024, an increase from the previous year.¹ To mitigate these financial risks, commercial leaders are increasingly familiarizing themselves with how the economics of safety operations is inseparable from the economics of the business.

Regulatory momentum for innovation

While industry leaders have long recognized that traditional approaches are falling behind, regulators are now beginning to respond. To help close this widening gap, the FDA created the Emerging Drug Safety Technology Program² to address how artificial intelligence (AI) can help address the challenges both industry and regulators face in collecting, processing, and evaluating the rapidly expanding volumes of patient safety data.

Executives should take note of this regulatory shift as a clear indicator to invest in systems that not only ensure compliance but also safeguard long-term viability in a market defined by constant and intensifying scrutiny.

AI as a multiplier of value

Pharmaceutical organizations are increasingly investing time and resources toward AI, noted for its ability to enhance both safety and cost-effectiveness in commercialization. McKinsey estimates the economic potential of AI in pharmaceuticals and medical products at $60 billion to $110 billion annually,³ driven largely by efficiencies in discovery, development, and operations.

In clinical development alone, AI could generate $13 billion to $25 billion each year through improvements in trial design, accelerated recruitment, and more efficient data analysis. When AI is applied to safety processes, it can reduce costs by more than 50%, improve decision-making, and free skilled professionals to focus on high-value analysis instead of repetitive data review.

From manual review to intelligent action

Historically, safety teams manually reviewed reports, call logs, and transcripts, a method that was manageable at a small scale but collapses under today’s global demands. AI-enabled technologies, such as natural language processing and voice-to-text, allow organizations to process unstructured data at scale. These tools can identify relevant medical terms, detect adverse event patterns across millions of touch points, and flag potential risks earlier and more consistently than manual review alone.

The real digital transformation, however, lies in the evolution from automation to intelligent action. Beyond presenting lists of potential complications, AI can identify incomplete or inaccurate reports before they reach regulators, trace recurring problems back to their root causes, recommend corrective steps, and prioritize cases based on severity or deadlines. Other implications include:

• Realized ROI. Manual safety review is resource intensive, and skilled professionals are in short supply. By automating triage and reserving human expertise for the most complex cases, organizations reduce overhead without compromising quality. In manufacturing environments, where volumes are high and margins are tight, these savings can be substantial.
• Strengthened trust. Better pharmacovigilance enhances trust with regulators, partners, and patients. Demonstrating that every piece of patient feedback is reviewed through rigorous, standardized processes reassures stakeholders and reduces the risk of reputational damage.
• Simplified partnerships. AI-driven workflows across differing organizations help create auditable, standardized approaches that transcend boundaries, making it easier to prove compliance during inspections and audits.

For global trials and operations, where partners and suppliers are all accountable for contributing to reporting, this intelligence reduces the likelihood of costly missteps while ensuring a consistent standard of care.

Aligning technology with strategy

AI’s integration into overall safety operations is not solely a technological project. It requires teams to be cross-functional, trained, and aligned across safety, quality, manufacturing, and regulatory operations. Strong data governance practices must be established to ensure accuracy and consistency so that AI systems can function as intended. Escalation protocols are equally essential, ensuring that flagged issues reach the right experts quickly. Ultimately, success depends on viewing pharmacovigilance not as a compliance requirement but as a core component of commercial strategy.

Safety is a strategic advantage

The evolution from time-consuming manual reviews to automated detection and now to intelligent action is reimagining the biopharma ecosystem’s approach to drug safety. For supply chain executives, this is a strategic opportunity to reduce costs, preserve organizational reputation, and build lasting relationships with partners and regulators.

Once the benefits of AI are realized in safety, the technology offers the ability to reduce time-to-market for new therapies and indications.

As the volume and complexity of safety data continue to grow, those who harness AI to transform that data into actionable intelligence will be best positioned to safeguard patients while remaining competitive in a crowded market. In a time and industry where trust and time are worth their weight in gold, intelligent pharmacovigilance and its impact across the development lifecycle are becoming not just a compliance necessity but a commercial imperative.

About the Author

Updesh Dosanjh is IQVIA’s practice leader, pharmacovigilance technology solutions, responsible for developing the company’s overarching strategy regarding AI and machine learning as it relates to safety and pharmacovigilance.

References

1. Global Pharma R&D Returns Rise as GLP-1 Drugs Help Drive Forecast Growth. Deloitte. March 25, 2025. https://www.deloitte.com/uk/en/about/press-room/global-pharma-rd-returns-rise-as-one-glp-drugs-help-drive-forecast-growth.html

2. CDER Emerging Drug Safety Technology Program. US Food & Drug Administration. March 25, 2025. https://www.fda.gov/drugs/science-and-research-drugs/cder-emerging-drug-safety-technology-program-edstp

3. Generative AI in the Pharmaceutical Industry: Moving From Hype to Reality. McKinsey & Company. March 25, 2025. https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality