From Quality Data to Quality Intelligence: The AI-Enabled Transformation

Quality management professionals face critical challenges in a quickly evolving life sciences landscape. Despite significant investments in quality management systems, many organizations struggle to extract meaningful insights from their quality data. Digitally advanced companies accumulate extensive audit trails, deviation reports, and compliance documentation but often lack the tools to transform this data into proactive quality intelligence. Meanwhile, organizations with manual processes face mounting compliance risks as quality information remains siloed on paper or in disparate systems. And regardless of digital maturity, many struggle to balance pressures to innovate and adopt AI into their processes, with concerns and fears about regulatory compliance for safe and effective AI implementation.

AI for life sciences presents unique opportunities and challenges. While the benefits across the product lifecycle are promising -- time savings, cost savings, acceleration of product delivery -- few organizations have yet to see true ROI on AI. Adoption of AI takes a strategic approach, evaluating current digitization maturity, data quality and data management, change management, employee upskilling, governance, regulatory compliance and more. In this complex, evolving regulatory landscape, quality professionals must also keep up with and navigate new frameworks. These include FDA's draft guidance on AI/ML (January 2025), ISO 42001 standard for artificial intelligence management systems, and ISO 42005 offers critical methodologies for impact assessment and risk mitigation when implementing AI technologies.

Join MasterControl product experts hosting this webinar to hear guidance for quality professionals at any stage of digital maturity to take the right steps to ultimately be able to use the latest frameworks to effectively assess and leverage AI capabilities. From identifying low-risk, simple areas for getting started, to finding opportunities to utilize AI to automate routine tasks to implementing sophisticated predictive quality analytics. while maintaining the compliance, validation, data integrity, human oversight and governance requirements essential in regulated environments.