Leveraging AI in Regulatory Affairs

While the integration of AI in Pharma Regulatory Affairs isn’t new, it’s becoming increasingly vital in how companies operate, particularly in strategy, decision making support and process automation.

These technologies are far from just being buzzwords. Some of the most common use cases revolve around:

• Optimizing Regulatory Strategies: Predictive analytics can be used to refine regulatory and labeling strategies, improving the chances of regulatory approval. By analyzing data from past submissions, companies can identify the most effective approaches and tailor their strategies accordingly.

• Monitoring Regulatory Changes: AI can continuously monitor regulatory changes across different regions, ensuring that companies stay up to date with the latest requirements. This proactive approach helps avoid compliance issues and ensures that all processes align with current regulations.

• Enhancing Decision-Making: Predictive analytics can help optimize regulatory strategies by analyzing data from past submissions. This data-driven decision-making enhances the likelihood of successful regulatory approvals.

• Automating Routine Compliance Tasks: AI can automate routine tasks such as the preparation, drafting, and review of regulatory documents. This not only reduces the time and effort required but also allows regulatory professionals to focus on more strategic and value-adding activities.

And many others …

While these may seem straightforward, implementing them often requires more investment and organizational discipline than one might think.

A common thread is the need for quality data. Often, it’s not even about having big datasets but rather having access to clean, accurate, and well-labeled data that can be used to train models, derive insights, or more broadly generate reliable outputs.

Maintaining high data quality is crucial for successful AI initiatives. Effective scaling of AI solutions from pilot stages to production-ready systems requires strong data management, including having data standards definitions, performing data cleaning, regular audits, and continuous monitoring.

These practices will help guarantee data quality, which facilitates faster and more dependable AI deployment. A sustainable AI strategy should be in harmony with a fit for purpose data strategy, extending beyond just the technical teams. It has become an increasingly important business need. Also in regulatory affairs, it is crucial to move past conventional documentation and dossier submission ways of working, and to regard data as a valuable asset.