The key to unlock AI’s full potential in pharmacovigilance 

The integration of artificial intelligence (AI) into pharmacovigilance (PV) is reshaping the life sciences industry by ushering in a new era of strategic innovation and operational efficiency.

Consider the numbers. According to a 2025 McKinsey survey of leaders in the pharma and MedTech industries, 32% of life sciences organizations are spending $5 million or more on generative AI budgets. The proportion of respondents planning to invest over $10 million grew by 7%, while those targeting between $5 and $10 million rose by 5%. In parallel, a recent Arnold & Porter survey found that approximately 80% of companies intend to incorporate AI into their research and development efforts.

These findings underscore a growing recognition of AI’s value to the industry. However, while the vision of AI’s value is becoming clearer, it is far from perfect. In a rush to remain competitive, many organizations mistakenly treat AI as a simple plug-and-play solution or another layer that can be added onto existing workflows, approaches that foster complications and frustration.

To unlock AI’s full potential, organizations must holistically rethink how their PV processes are designed. This means conducting a comprehensive gap analysis to identify processes, as well as the roles and responsibilities that can truly benefit from AI integration, while emphasizing the importance of human oversight.

Myth: AI is simply plug-and-play

One of the first missteps made by organizations initially embarking on their journey with AI is their attempt to integrate modern technologies into outdated legacy systems and processes with the goal of boosting efficiency and sparking innovation. However, without first addressing the inefficiencies within legacy systems, organizations risk neutralizing the very benefits AI is meant to deliver. 

Legacy systems were designed in a time of numerous technological limitations resulting in inflexible, inefficient workflows and structures that no longer meet the demands of today’s dynamic environment. By relying on the plug-and-play approach or simply layering AI on top of outdated infrastructure without addressing the root cause of inefficiency, AI can create waves of negative effects and further exacerbate existing issues. Instead of delivering clarity, this approach often leads to more fragmented and disjointed digital environments, ultimately with adverse impacts on compliance efforts. 

Positive impact with AI starts by avoiding common pitfalls. Organizations must ground their approach to technology, not outdated assumptions. This means instead of retrofitting AI into legacy systems, organizations should reimagine processes from the ground up to be AI-compatible, adaptive, scalable and future-ready.  

The four principles core to workflow transformation 

Rethinking an organization’s technological ecosystem should be guided by key principles, including: 

• Breaking away from legacy templates: Processes built for legacy systems often fall short in an AI-driven environment. To fully realize AI’s potential, organizations must be willing to overhaul outdated procedures, from standard operating protocols to data verification flows, including both online and off-line activities. Processes of the past may hinder today’s progress.
• Redesign workflows around AI strengths: While avoiding the plug-and-play method, organizations should identify high-impact tasks where AI can deliver the most value. For example, natural language processing can extract key data from free text fields in safety reports, reducing manual data entry efforts and increasing efficiency. AI strategies should be built around these targeted, outcome-driven opportunities. 
• Establish measurable, outcome-based goals: Before deployment, organizations need to define specific, quantifiable objectives, such as increased compliance, reduced error rates or lowered cost-per-case. These metrics provide a clear benchmark for evaluating AI performance and help shape both tool selection and implementation planning.
• Prioritize human-AI collaboration: Rather than replacing expertise, AI should amplify it, freeing skilled professionals to focus on activities that demand critical thinking and domain knowledge. AI can handle repetitive, low-value tasks; however, human oversight remains critical, especially in edge cases and complex evaluations. A human-in-the-loop approach ensures that context, judgment and accountability remain a central focus. 

Building responsible AI in pharmacovigilance

No two AI implementations or organizations’ transformation journeys are the same. Each one will have its unique attributes that lead to success. However, there are similarities within key checkpoints that organizations should aim to achieve when updating and enhancing their workflows:

• Address the human factor: Change can be unsettling, especially when it involves new technology. To ease this transition, organizations must proactively address employee concerns by positioning AI as a tool to enhance, and not replace, human expertise. Demonstrating how automation can reduce administrative burdens and help prevent burnout fosters trust. In fact, a recent American Medical Association survey found 57% of physicians identified reducing administrative burden as the greatest opportunity for AI in healthcare. These burdens include but are not limited to time-consuming, non-clinical tasks that take away crucial time from patients such as data entry, documentation, and coordination of car plans. Framing AI as a supportive asset not only helps smooth adoption but also helps to build long-term confidence in its value.

• Conduct deep process mapping: Another milestone during the AI integration process involves thoroughly mapping existing workflows to uncover operational pain points, such as bottlenecks, duplicated data entry, manual handoffs and disparate systems. These are telltale signs of legacy infrastructure and common sources of inefficiency and error. Gaining a detailed view of the current landscape is essential to designing AI solutions that tackle root causes, not just surface-level symptoms.

• Prioritize organizational change management: Successful AI integration depends on a proactive, well-structured change management and communication strategy. This includes clear communication and targeted training across all levels of the organization. Appointing dedicated change leaders helps maintain alignment, accountability and momentum across every phase of the transformation.

Defining and measuring success

Organizations must look deeper than surface-level efficiency gains and focus on more meaningful metrics of success to understand the full impact of AI within the PV setting. Out-dated benchmarks like processing speed are narrow-sighted and may miss AI’s broader impact on safety operations. Instead, user groups should establish a comprehensive framework that includes both performance and quality. 

Examples of what this can include would be the time it takes to move a case from initial intake to submission, the accuracy of AI-generated case classifications and the proportion of cases that require human review or correction. At the center of this digital transformation should be quality assurance, with internal audit scores and AI-driven performance analysis that shifts the focus from traditional in-line quality control toward a more comprehensive approach emphasizing consistency, compliance and process reliability.

Equal to digital metrics are the human-centric metrics that encapsulate the experience of those interacting with AI systems. For example, employee satisfaction can reflect how seamlessly AI tools are being integrated into daily activities, while patient satisfaction shares insights into how effectively AI supports timely and accurate communication about safety events. When merged, these insights can provide a more complete view of AI’s true impact. By anchoring success in both quantitative and qualitative outcomes, organizations can ensure that AI enhances the PV industry, not only in speed, but also in safety, reliability and trust.

A long-term investment

As PV organizations begin their journey toward AI-powered platforms, it’s important to recognize this as a long-term investment that establishes the foundation for quality improvements and future scalability. However, to unlock meaningful value, traditional processes must be thoughtfully reengineered from the ground up.

At the same time, human oversight should remain a core principle to ensure evolving technologies function as intended. What changes is how expertise is applied within modernized, AI-enhanced workflows, that support scale. To avoid friction and maximize impact, AI should not be treated as a bolt-on solution. Instead, organizations must integrate technology in ways that align with these new workflows and evolving roles.

Transformation goes beyond technical implementation. It also requires cultural shifts, cross-functional alignment and a willingness to rethink how work gets done. Organizations that embrace complexity and intentionally design for human-AI collaboration will be best positioned to meet rising case volumes and regulatory demands with greater speed, accuracy and agility.