Session Highlights
Paul Hanson with Takeda will dive into how a cross-functional global investigations team uses AI tools, including Monte Carlo methods for Failure Mode Effect Analysis, to speed up prioritization of root causes. He’ll also discuss using GenAI in Miro for quickly sorting themes of root causes and drafting reports. Attendees will learn how to build ad-hoc AI-workflows or to integrate them into existing processes, improving both speed and quality in investigation-driven environments.
This presentation shows a real world case study in which the Recordati Cork team applied an AI-powered data analytics platform, supported by the Internet of Things and a secure GxP cloud environment. This approach enabled a Recordati team in Cork, Ireland, to improve production performance through a better understanding of process variability, yield increase, and compliance improvement. In just three months they achieved a 1.5 percent yield increase and 2 percent drop in the cost of goods (COGS). Attendees will learn what steps to take to operationalize AI in a regulated manufacturing environment, how to use contextualized data for insight, and how to do it while maintaining data integrity.
This session presents “lab of the future” use cases with a digital twin simulation for optimizing assay scheduling and paperless workflows and an AI application in predictive maintenance for lab equipment. Attendees will learn how these tools can improve throughput and asset utilization, reduce equipment downtime, forecast their failures, and deliver a return on investments (ROI) in lab operations.
Vanessa Fernandes, MD, with Sanofi and Marc Jordà with Aily Labs will present a case study that looks at an AI-powered dashboard tool that aggregates cross-functional data (e.g., finance, manufacturing, quality assurance, regulatory) to give a 360-degree view of operations. They’ll cover features like Quality Maturity Index 2.0, risk exposure, deviations by AI, and complaints by AI. Attendees will learn how to build dashboards and tools that not only show metrics but drive faster informed decisions; how to elevate quality maturely; how to reduce siloed reporting with AI.
Why This Track Matters
These sessions collectively offer something actionable: methods to scale AI initiatives, overcome data fragmentation, and embed digital tools into existing workflows. Then there’s governance, change management, and human-centered innovation—because even the best AI won’t help if people, processes, or culture aren’t aligned.
Author Reflections
What makes this track particularly compelling is how each session moves beyond theoretical ambition and into practical application. From measurable improvements in yield and cost, to smarter investigations and predictive lab operations, these case studies show that AI is no longer a future promise—it’s a present reality in Pharma 4.0.
The Recordati case study demonstrates how AI can be embedded into manufacturing workflows with tangible results. A 1.5 percent yield increase and 2 percent reduction in COGS in just three months is impressive, and it opens up interesting reflections on how such success might be scaled across different sites and organizational contexts. Attendees can explore: What kind of governance, data integrity, and team alignment are needed to replicate this impact?
Takeda’s approach to global investigations offers a convincing example of how AI can support decision-making in complex, high-impact scenarios. The use of Monte Carlo simulations and GenAI tools shows clear potential—but also invites the industry to consider how transparency and traceability can be maintained when AI becomes part of the reporting process. The session poses the question: Is the industry ready to audit algorithms the same way processes are audited?
Gilead’s Lab of the Future introduces innovative concepts like digital twins and predictive maintenance, which could significantly improve lab efficiency and resilience. Yet, as with many digital initiatives, the transition from pilot to scale raises questions about infrastructure, skills and organizational readiness. Attendees may consider: Will labs have the foundations and mindset to adopt these tools sustainably?
Finally, Sanofi’s dashboard represents a strong move toward data democratization. Aggregating insights across quality assurance, regulatory affairs, research and development, and other domains is a powerful step, and it highlights the importance of harmonizing data sources and fostering cross-functional collaboration to ensure clarity and accountability in decision-making. Attendees may contemplate/explore with the speakers: When AI starts suggesting actions, who takes responsibility?
These sessions don’t just showcase AI— they offer inspiration but also challenge us to think critically about how we implement it. The opportunities are clear, and the challenges are part of the journey. And that’s exactly why this track matters: the track sessions encourage attendees to learn from what works, look at what it takes to make AI truly work in pharma, and shape a future where AI is not just smart—but responsible.
Learn more and register for the 2025 ISPE Pharma 4.0™ Conference
Disclaimer:
iSpeak Blog posts provide an opportunity for the dissemination of ideas and opinions on topics impacting the pharmaceutical industry. Ideas and opinions expressed in iSpeak Blog posts are those of the author(s) and publication thereof does not imply endorsement by ISPE.
