Sandeep Burugupalli
Head of Data Science, BioPharmaceuticals Research & Development AstraZeneca
Sandeep Burugupalli is Head of Data Science for BioPharmaceuticals R&D at AstraZeneca, where he leads enterprise efforts to integrate real‑world evidence, AI, and advanced analytics to inform clinical development decisions. With previous leadership roles at Novo Nordisk and Pfizer, he has driven RWE strategy, built global data platforms, and pioneered AI‑enabled approaches across the drug development lifecycle. At IMPACCT, Sandeep will speak on “Redesigning Real‑World Evidence in an AI Enabled Era,” exploring how organizations are embedding AI and unifying clinical and real‑world data to transform evidence generation and decision-making.”
Seminars
Thursday 29th October 2026
Why Enterprise Evidence Strategies Still Fail at Scale & What It Takes to Make Them Deliver
8:35 am
- Reframing how RWE, epidemiology, and advanced analytics should be integrated across the full R&D lifecycle to move from fragmented studies to coordinated, decision driven evidence strategies
- Understanding why many enterprise RWE and AI investments fail to influence key development, regulatory, and access decisions despite significant scale, and what differentiates organizations that consistently deliver impact
- Building the infrastructure, partnerships, and governance models required to connect global data, real world evidence, and AI insights into a cohesive system that supports faster, more confident decision making
Thursday 29th October 2026
Panel Discussion: How RWE & AI Are Actually Shaping Research & Development & Evidence Strategy Today From Methodology to Impact
9:05 am
- How is real‑world evidence being used across the full development and post‑launch lifecycle, and how should organizations integrate RWE earlier while leveraging AI and other methods to enhance decision‑making at each stage?
- How is real‑world evidence being used in practice to inform R&D and lifecycle decisions beyond methodological decisions?
- What does real‑world AI adoption look like beyond pilots, and what has been required to move from experimentation to scalable, trusted impact?
- Where are the real credibility risks when integrating AI and RWE into regulated evidence generation, and how are leading organizations managing them?
- How should companies balance speed, innovation, and scientific defensibility as AI becomes embedded across evidence generation workflows?
- What strategic and governance capabilities will distinguish evidence leaders over the next 3–5 years?
