In the pharmaceutical and biopharmaceutical industry, patient safety is a steadfast non-negotiable. Whether it is in the development of a novel therapy or the manufacturing of an established drug, the responsibility to monitor, detect, and respond to safety issues is paramount for retaining trust and regulatory compliance. However, the complexity of achieving and maintaining this goal within today’s life sciences environment is greatly increasing. This is especially true when manufacturing and clinical trial operations span multiple geographies, partners, and regulatory frameworks.

The reality we face today is that the scale and variety of safety-related data has outpaced traditional monitoring methods. Call center transcripts, social media posts, chatbot interactions, adverse event reports, and even patient feedback collected by third-party vendors are all torrential streams of data that contribute to a vast pool of unstructured information. Buried within these sources are critical safety signals, early indicators of issues that could affect patient health or product quality. Yet without the right tools, much of this data goes under analyzed, delaying intervention and increasing risk.

The challenge is no longer a lack of data, but how to cut through the noise to find and act on the insights that matter.

Escalating costs and increasing complexity

The pressures on safety teams come at a time when drug development costs are also at record highs. The average cost to develop a drug has risen to $2.2 billion in 2024, up from $2.1 billion in 2023. These figures indicate the growing demand for new levels of efficiency at every stage of the value chain, including safety monitoring. Delays or inefficiencies in pharmacovigilance (PV) can trigger a domino effect that includes costly setbacks to an already expensive development process. This is driving demand to adopt tools that streamline the path from detection to resolution.

AI as a value multiplier

To chart a streamlined path forward, PV teams must explore practical ways to leverage artificial intelligence. According to McKinsey, the broader economic potential of AI in the pharmaceutical and medical products sector is estimated at $60–110 billion annually, largely driven from efficiencies in discovery, development, and operations. Within the clinical development space alone, the projected impact is a $13–25 billion per year, enabled by improvements in trial design, accelerated patient recruitment, and more efficient data analysis. For safety teams, capturing even a fraction of this potential means accelerating workflows, eliminating bottlenecks, and enhancing decision-making in ways that directly protect patients while reducing operational costs.

Regulatory momentum

Regulatory agencies are also advocating for innovation in safety monitoring. The U.S. Food and Drug Administration established the Emerging Drug Safety Technology Program (EDSTP) in response to the growing challenges faced by both industry and regulatory authorities in the timely and efficient collection, processing, and evaluation of individual and aggregate patient safety data, which are further compounded by ever-increasing case volumes. This reflects a growing consensus that next-generation tools will be essential for managing the scale and complexity of modern safety demands.

Moving beyond manual monitoring

Historically, pharmacovigilance and product safety teams have relied heavily on manual review processes. Analysts would read through reports, transcribe call logs, and flag items for further investigation. While effective at small scale, these methods buckle under the weight of today’s data volumes. A global portfolio of products could easily generate thousands of safety-relevant interactions each week, more than any human team can reasonably process without automation.

AI-powered technologies, such as natural language processing (NLP) and voice-to-text conversion, suggest a new workflow. These tools can rapidly process unstructured inputs, converting audio calls to text, extracting relevant medical terms, and identifying adverse event patterns across millions of data points. The result is a faster, more consistent method to detect potential safety issues.

For pharmaceutical organizations, this is not just a matter of operational efficiency. It is an opportunity to strengthen client partnerships by offering higher-quality safety monitoring as part of the service portfolio. The ability to assure customers that every piece of patient feedback is analyzed for risk, no matter the language, source, or format, can be a powerful differentiator in a competitive market.

From automation to intelligent action

Automation is only the first step in pharmacovigilance innovation, with the next frontier being intelligence that can act. Modern AI tools in the safety space are evolving from passive assistance to active participation. Instead of solely presenting a list of potential issues for human review, these systems can flag incomplete or inaccurate reports before they ever reach regulators. They can trace recurring problems back to their root causes, suggest corrective actions, and even prioritize cases based on severity or regulatory deadlines.

In a manufacturing or clinical trial environment where multiple stakeholders contribute to safety reporting, this level of intelligence can prevent costly missteps. For example, if a safety report submitted by a regional partner is missing required patient demographics, the AI system can automatically flag it for completion, saving time and avoiding regulatory delays. Similarly, if recurring quality issues are traced to a specific batch or packaging process, AI can identify the pattern and recommend targeted process adjustments.

Supporting regulatory readiness across partnerships

Compliance is a responsibility shared between contract service providers and their clients. Yet the distributed nature of outsourced pharma operations can make regulatory readiness more challenging. When multiple organizations, each with their own systems and processes, are involved in collecting and submitting safety data, inconsistencies are inevitable.

Here, AI-driven workflows can help create a standardized, auditable safety process across the supply chain. By embedding signal detection tools directly into safety operations, companies can ensure that every piece of data passes through the same rigorous checks. This not only reduces the risk of non-compliance but also makes it easier to demonstrate due diligence during audits or inspections.

For decision-makers in outsourcing and supply chain management, this capability is critical. Regulatory scrutiny is intensifying worldwide, and the expectation is that all partners in the value chain are equally accountable for patient safety. Having AI-enabled processes in place demonstrates a proactive approach to meeting these expectations.

Cutting costs without cutting corners

Cost efficiency is another compelling reason for pharma organizations to embrace AI in safety operations. Manual safety review is resource intensive. With skilled pharmacovigilance professionals in short supply, dedicating them to low-value tasks like sorting through non-relevant reports wastes talent and budget.

AI helps by triaging cases, prioritizing those that truly need expert review, and automating the handling of lower-risk or duplicate reports. This enables safety teams to focus their expertise on the complex cases that require human judgment, while reducing the burden of unnecessary manual processing.

In pharmaceutical manufacturing these savings can be significant. More importantly, they can be achieved without compromising patient safety or product quality.

Building the future of safety intelligence

The shift from manual review to automated detection, and now to intelligent action, is reshaping how the pharma supply chain approaches safety. But technology alone is not enough. Successful adoption requires cross-functional alignment between safety, quality, manufacturing, and regulatory teams, both within the contract service provider and across client organizations. It also demands standardized data practices to ensure AI tools have clean, consistent inputs from all sources, as well as clear escalation protocols, so that when AI flags an issue, the right people can respond quickly.

As the volume and complexity of safety data continues to grow, the organizations that can harness AI to transform that data into timely, actionable intelligence will be best positioned to meet regulatory demands, protect patients, and strengthen client trust. For contract pharmaceutical companies, this represents not just a technological evolution but a strategic opportunity. By embedding AI-enabled safety intelligence into core operations, CDMOs can move from being service providers to being trusted safety partners, delivering not only products but also peace of mind.

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.