In this video
What you'll learn
Diagnose why AI initiatives stall
Identify the real organizational and feedback bottlenecks that stop pilots from scaling.
Shift from Proof of Concept to Proof of Impact
Use a simple diagnostic model to reframe your projects around measurable business outcomes.
Map your first feedback loop to link data, decisions, and me
Visualize the connection between data, decisions, and results to build systems that learn and improve.
Why this topic matters
Everyone’s under pressure to “do something with AI,” but few can prove it delivers results. This session helps leaders shift from Proof of Concept to Proof of Impact, designing feedback loops that turn experiments into measurable outcomes. You’ll leave with a framework to evaluate pilots and a roadmap to scale what drives impact across any organization.
You'll learn from
Dr. Ramya Ravichandar
PhD in CS | AI & Product Leader | Advisor to Fortune 100 Enterprises
I have a PhD in Computer Science and led AI at the edge before it was mainstream, building a startup in 2013 acquired by Johnson Controls. Recognized with multiple innovation awards, I’ve since helped Fortune 100 enterprises scale digital transformation across complex, data-driven industries. As an adjunct at Duke University, I help leaders move from Proof of Concept to Proof of Impact.
Previously VP at Jones Lang LaSalle
Go deeper with a course
AI Pilots to Profit: Scaling Applied AI in Complex, Data-Driven Organizations
Ramya Ravichandar, Ph.D
Ph.D in CS | AI & Product Leader | Advisor to Fortune 100 Enterprises