Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong
Sudarshan Seshadri
Hugo Bowne-Anderson
Duncan Gilchrist
Sudarshan Seshadri—VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy—joins us to explore what it takes to build high-stakes AI systems that people can actually trust. He shares lessons from deploying machine learning and LLMs in healthcare, where speed, safety, and uncertainty must be carefully balanced. We talk about designing AI to support pharmacist judgment, the shift from bottlenecks to decision backbones, and why great data leaders are really architects of how irreversible decisions get made.
Guest
Sudarshan Seshadri
Alto Pharmacy
Key Takeaways
Data leadership is decision leadership.
Suddu argues that the core job of a data leader isn’t managing models or dashboards—it’s shaping how critical decisions get made, especially when stakes are high and judgment matters. AI in healthcare isn’t about speed—it’s about trust. At Alto, automation doesn’t replace humans; it supports pharmacists with safe, explainable, and regulation-aware systems. Trust is earned through consistency, not cleverness. LLMs are tools for structure, not generation. Rather than generating content, Alto uses LLMs to extract, classify, and interpret clinical data—feeding structured signals into downstream decision systems built for precision. The metrics you track shape the outcomes you get. Suddu shows how shared, causal, and actionable metrics—like Alto’s “perfect prescription score”—can bridge teams and move the needle on both patient experience and operational performance. Full-stack practitioners thrive in strong systems. While the team includes specialists, Alto’s strength comes from people who can carry problems from concept to resolution—and from a culture that supports collective growth. Judgment scales through structure, not speed. With thousands of contextual decisions happening daily, Alto invests in infrastructure to scale pharmacist judgment—not just throughput. Probabilistic reasoning and human-in-the-loop systems are essential. Irreversible decisions demand better tooling. The systems Suddu builds don’t just support workflows—they influence decisions with real consequences. That’s why rigor, feedback loops, and explainability are baked in from the start. The next leap for data leaders is executive. Looking ahead, Suddu sees modern data leaders stepping into broader roles—defined by storytelling, strategic clarity, and long-term decision accuracy, not just technical expertise.
You can read the full transcript here.
00:00 Introduction to Decision Making in Data Leadership
01:08 Challenges in Data Leadership 01:22 Interview with Sudu: Journey and Insights 01:36 Building Trust in AI Systems 01:46 From Bottlenecks to Backbones 02:08 Call to Action 02:20 Introduction to Delphia and High Signal 02:56 Frameworks for Decision Muscle Memory 03:29 Sudu's Career Journey 05:33 Joining Alto Pharmacy 08:03 Building a Data-Driven Culture at Alto 12:49 Evolution of Data Teams: 2012 vs. 2025 16:19 Creating Shared Definitions and Metrics 21:35 Retaining Talent and Building Strong Teams 25:46 Role Design: Full Stack Practitioners vs. Specialists 28:17 The Role of Full Stack Practitioners in Data Teams 28:56 AI and LLMs in Pharmacy Decision Support 29:19 Challenges in Pharmacy Workflow 31:48 Machine Learning Applications at Alto 34:43 AI Pharmacist Assistant: Reducing Burnout 39:41 Using LLMs for Contextual Learning in Healthcare 44:19 Balancing Automation with Safety and Compliance 48:11 Metrics for Measuring AI Systems in Healthcare 51:52 Future of Data Leaders in Executive Roles 55:15 Retrospective Advice for Data Leaders 57:58 Conclusion and Final Thoughts