Manufacturing strategies have evolved across every organizational level – from corporate, to IT/OT, systems, quality, and product – in order to address today’s challenges and meet market demands. Both legacy and modern systems alike must then evolve in order to bridge the gap. In this presentation we’ll bridge the gap between where MES systems and approaches are today and where they need to be in order to accelerate MES’s impact on life sciences. We’ll outline new industry accelerants led by AI – as well as quality, strategic and economic strategies. In this session, you will learn how to:

• Define simultaneous platform shifts in AI, AR, personalized medicine, and manufacturing.
• Identify common challenges faced by manufacturing teams.
• Ensure seamless integration of AI with legacy MES systems by identifying key steps for a successful and efficient transition.

As pharmaceutical manufacturing accelerates, the ability to anticipate issues before they occur is becoming a critical differentiator. While many organizations are exploring AI to enhance MES, the real value lies in predictive analytics—using real-time data streams to forecast deviations, optimize batch release, and improve overall yield. This presentation will focus specifically on how predictive AI can transform MES from a compliance-driven tool into a proactive driver of operational excellence. Drawing on industry use cases, we will explore the opportunities and challenges of embedding AI-driven insights into day-to-day manufacturing execution. In this session, you will learn how to:

• Leverage MES data streams to build predictive models that anticipate deviations and quality risks.
• Translate predictive insights into actionable interventions for faster, safer batch release.
• Balance regulatory compliance with AI-driven decision-making to ensure trust, transparency, and adoption.

• How can we define and implement simultaneous platform shifts involving AI, AR, personalized medicine, and manufacturing systems?
• In what ways can AI and AR work together to enhance training, troubleshooting, and maintenance processes on the manufacturing floor?
• How can we accelerate the adoption of AI to drive faster batch releases and reduce time-to-market for pharmaceutical products?
• What are the economic, strategic, and quality considerations when implementing AI-driven MES solutions in life sciences manufacturing?
• How can we ensure a seamless integration of AI with legacy MES systems, and what steps are required for a successful transition?

In the Harvesting Session the workshop moderators of the event will summarize the results of their discussions and highlight key findings from the World Cafés.