Summary
Internal AI assistants, copilots, and agents promise to boost productivity, but without the right inputs and data platform, they often underdeliver. Making AI genuinely useful, trustworthy, and consistently high-performing for any team requires context-rich data and architectures and processes that can evolve as AI technologies do. In this session, we'll exchange lessons on building platforms that feed AI the right stuff, enforce security, and integrate actions and outputs back into real workflows. Whether you're building AI productivity tools or integrating AI into your organization, you'll leave with ideas to help design roadmaps that maximize AI's ROI every time.
Speakers
Shae leads the Core Data team at Ripple, where she oversees data science, analytics, and products across blockchain, finance, onboarding, compliance, and trading. During her time at Ripple, she built out data science roadmaps and capabilities from zero to one, leading metric frameworks, data foundations, experimentation, and ML modeling initiatives. Prior to Ripple, Shae held Data Science roles at Uber, working on churn modeling, survival analysis, and A/B testing. She also taught a course on survival analysis, available on Datacamp.
on ELC Annual 2025