What If The Real Blocker Is Coordination Not Data w/ Anastasia Christianson

AI is everywhere in pharma right now, yet drug development still feels slow, fragmented, and full of avoidable rework. We sit down with Anastasia Christianson, former SVP and Global Head of AI Data and Analytics at Pfizer and now Managing Principal at EPAM Life Science Consulting, to get specific about what actually blocks progress in clinical trials and what AI can realistically change. We dig into the uncomfortable pattern behind many “successful” AI deployments: a model fixes one bottleneck, then the system simply jams up somewhere else. Anastasia makes the case for end to end thinking across trial design, feasibility, patient recruitment, engagement, site operations, and data flow. We talk about why niche AI products are easier to build, why coordination is harder than modelling, and how digital health’s app explosion offers a warning for today’s AI tooling. Along the way, we unpack digitalisation, data standards, FAIR data, and why “perfect data” is not the same as “useful data”. The most practical segment centres on patients: how AI can help build cohorts, find eligible participants, answer questions, reduce fear, improve convenience, and prevent dropouts that cost time and money. We also turn FDA denials into a quick quiz to highlight a key truth of life sciences innovation: approvals can fail due to control arm choices, surprising trial requirements, or manufacturing site inspection findings even when the science looks promising. If you care about AI in clinical trials, R&D productivity, and how to turn data into faster access to medicines, you’ll get both a reality check and a playbook. Subscribe for more, share this with a colleague in clinical operations or data science, and leave a review telling us: where do you see the biggest bottleneck right now?