The AI in Business Podcast "Waking Up" Data in Clinical Workflows with AI - with Mathew Paruthickal of Sanofi
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Dec 2, 2025 Mathew Paruthickal, Global Head of Data Architecture, Utilization, and AI Engineering at Sanofi, shares insights on bridging structured and unstructured data in life sciences. He discusses a phased approach to integration, emphasizing the importance of governance from day one. Mathew highlights the need for human oversight in AI workflows due to ethics and patient safety. He also explains the significance of explainable AI and the inclusion of traceability and auditability. Practical applications like auto-extraction of safety reports showcase the potential of AI for continuous improvement.
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Phase Projects To Show Early Value
- Start AI projects in phases: crawl, walk, run to show value early and avoid overreach.
- Involve business stakeholders early and iterate on modular systems to scale effectively.
Co-Author Problems With Business Teams
- Co-author problem statements with business partners to embed product thinking into engineering work.
- Keep engineering aligned with business benefit rather than starting with tech scoping alone.
Build Explainability And Traceability First
- Explainability and auditability must be built into AI systems from day one, not retrofitted later.
- Traceability to source documents lets teams validate that AI outputs are grounded in truth.