Ramila Peiris
Global Head of Data Management, Machine Learning, Artificial Intelligence Platform, Manufacturing Science & Technology Sanofi
Ramila Peiris is the Global Head of Process Data Management, Machine Learning, and AI Platform within MSAT at Sanofi, where he defines the strategy and vision for data engineering, and AI solutions across manufacturing operations.
With over 15 years of experience, Ramila has championed the adoption of advanced technologies – including Generative AI, Process Analytical Technology (PAT), IIoT, and prescriptive analytics – successfully scaling initiatives from proof-of-concept to enterprise deployment. Ramila is recognized for bridging technical innovation with operational execution, enabling data-driven decision-making, and improving process performance in GxP-regulated environments. He is a strong advocate for building data- and AI-driven cultures by aligning cross-functional teams around trust, governance, and value realization.
Seminars
- Building a comprehensive data foundation and process monitoring capability at a single site, establishing a lighthouse model for the broader organization
- Navigating stakeholder misalignment and organizational resistance over a multi-year journey to align on a shared vision and secure enterprise-wide mandate and investment
- Driving data & AI-driven culture in GMP manufacturing by aligning cross-functional teams on execution, governance, trust, and ownership
- Which process monitoring technologies will actually deliver measurable ROI in the next 3–5 years, and how do you prioritise investment to avoid over‑promising and under‑delivering?
- How can organizations confidently balance innovation with GMP risk, product sensitivity, cost, and operational complexity to ensure new technologies enable rather than disrupt manufacturing performance?
- In what ways can evolving regulatory guidance be leveraged to accelerate adoption and strengthen control strategies, rather than acting as a barrier?
- What approaches actually work when retrofitting new monitoring tools into legacy systems, and when does it make more sense to invest in greenfield solutions?
