Essential Infrastructure Solutions for Life Sciences Manufacturing and Supply Chain Workflows - with Kartik Pant and Shreyas Becker of Sanofi
Feb 11, 2025
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Kartik Pant and Shreyas Becker from Sanofi dive into the evolving landscape of data and AI in life sciences manufacturing. They discuss the critical need for a unified data architecture and the benefits of cloud adoption to enhance collaboration. The duo emphasizes a problem-driven approach to technology, focusing on specific use cases to optimize processes. They also explore the transformative potential of generative AI and how innovative data management solutions can revolutionize operations across the industry. A must-listen for those looking to harness AI in life sciences!
A unified data architecture is crucial for harmonizing diverse systems in biomanufacturing, enabling effective use of AI and machine learning.
Organizations should adopt a problem-defined approach to technology, focusing on specific challenges to ensure AI investments align with business objectives.
Deep dives
Challenges of Data Standardization in Biomanufacturing
Biomanufacturing faces significant challenges due to the complexity of data management across multiple production sites. Companies often have many different systems, including various versions of ERP and lab systems, leading to a lack of data standardization. This inconsistency makes it difficult for data scientists to utilize machine learning and AI effectively, as the same data means different things in different contexts. To address this, it's critical to harmonize data across locations to ensure that column headers and formatting are consistent, enabling better use of advanced technologies.
Adopting a Problem-Driven Approach to Technology
A problem-defined approach is essential when implementing AI and cloud solutions in life sciences. Organizations often lean towards vendor-driven technology adoption, which may not align with the specific needs of their operations. By focusing on tangible outcomes and identifying specific problems to solve, companies can better assess which technologies will deliver real value. This mindset encourages experimentation and ensures that investments are purposeful, avoiding the pitfalls of adopting solutions that aren't aligned with business objectives.
The Role of Cloud Adoption in Life Sciences
Cloud infrastructure serves as a critical enabler for scaling operations in the life sciences sector. It supports the democratization of data, allowing for increased collaboration and data sharing across various departments. However, merely moving to the cloud isn’t sufficient; organizations must identify problem areas that can be addressed through cloud technology, allowing for a strategic and effective deployment of AI and data solutions. The key is to develop a scalable architecture that prioritizes return on investment through improved efficiency and productivity across the supply chain.
Today’s guests are two senior executives from Sanofi’s manufacturing division, Head of Data & AI for Manufacturing & Supply Chain Kartik Pant and Head of AI & Data Products, Manufacturing & Supply Shreyas Becker, respectively. Together they return to the podcast to cover the challenges and opportunities in adopting data infrastructure and AI solutions in life sciences manufacturing. Throughout the episode they emphasize the need for a unified data architecture, starting small, and articulating a problem-driven approach to technology adoption. Kartik and Shreyas also discuss the importance of understanding specific use cases and leveraging data to identify areas for optimizing processes. If you’re interested in unlocking our AI best practice guides, frameworks for AI ROI, and specific resources for AI consultants, visit emerj.com/p1.
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