GENAI AND INTELLIGENT AGENTS
IN THE ENTERPRISE
San Francisco | November 6, 2025
Ruchi Mangharamani
IT Business System Analyst, Senior Advisor at Elevance Health
I design AI systems that make critical healthcare decisions more transparent, efficient, and scalable. At Elevance Health, I lead initiatives using generative AI, causal modeling, and decision intelligence to automate processes like prior authorization, fraud detection, and risk prediction—domains where trust, interpretability, and real-world constraints matter deeply. My work explores how emerging techniques like neurosymbolic AI, blockchain smart contracts, and synthetic data ecosystems can reshape policy automation in regulated environments. I’ll share practical insights from building these systems at scale—what worked, what didn’t, and where the next breakthroughs might emerge.Watch in-person: November 6
Designing Trustworthy AI for High-Stakes Decisions: Lessons from Healthcare Automation at ScaleWhen AI is deployed in healthcare, the stakes aren’t just high—they’re human. In this talk, I’ll share how we built and deployed trustworthy, scalable AI systems to automate critical decision-making processes like prior authorization, fraud detection, and risk prediction at Elevance Health, one of the nation’s largest healthcare providers. The work blends generative AI, causal inference, neurosymbolic reasoning, and adaptive dashboards to support medical and policy decisions where transparency, accuracy, and compliance are non-negotiable. Rather than chasing model performance alone, we designed AI that clinicians and policy teams could trust—and actually use. I’ll walk through how we identified real-world bottlenecks, embedded explainability into our systems, ran controlled A/B experiments to measure adoption, and reimagined workflows with self-learning components. This talk is for data scientists and leaders building AI in regulated or high-impact environments, where success is not just about prediction but about credibility, integration, and long-term value.