Summit Highlights

ISPE’s upcoming 2026 ISPE AI in Life Sciences Summit – Powered by GAMP®, taking place 23–24 June in Boston, Massachusetts, will bring together a diverse group of professionals from across the pharmaceutical and tech industry to address the nascent opportunities and challenges for applying artificial intelligence in the industry. The use of advanced AI and machine learning models in pharmaceutical manufacturing and quality control has become an important topic for both industry and health authorities due to its potential benefits and the distinct challenges it presents.

As artificial intelligence (AI) continues to accelerate across the life sciences industry, leaders face a critical question: how do we adopt AI at scale without compromising patient safety, data integrity, or regulatory compliance? The 2026 ISPE AI in Life Sciences Summit - Powered by GAMP®, taking place 22–23 June 2026 in Boston, Massachusetts, United States, is designed to address this challenge head-on.

AI is no longer a distant or experimental concept within pharmaceutical and biotechnology organizations. Across laboratories, manufacturing operations, and quality systems, AI-enabled tools are increasingly being piloted to accelerate insights, strengthen decision-making, and improve operational efficiency. Yet despite growing enthusiasm, many organizations encounter a consistent and familiar challenge: moving beyond promising pilots and into compliant, production-ready systems that can withstand regulatory scrutiny and scale sustainably.

Artificial intelligence is no longer confined to innovation labs within life sciences organizations. It is now influencing drug and device development, clinical research, manufacturing operations, supply chain performance, and post-market surveillance. AI systems are accelerating molecule screening, enhancing pharmacovigilance signal detection, optimizing maintenance programs, and supporting documentation workflows.

The life sciences industry stands at a pivotal moment. Artificial intelligence (AI) and generative models are rapidly moving from experimental environments into GxP-regulated workflows. At the same time, regulatory expectations, including those reflected in the EU Annex 22 Draft and recent US Food and Drug Administration guidance, are evolving, with increased scrutiny on data integrity, model transparency, lifecycle governance, and ongoing state of control.