From Proof to Profit: Scaling AI That Works

Hosted by Dr. Ramya Ravichandar

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

Jones Lang LaSalle
Cisco
Duke University
Johnson Controls
Virginia Tech

Go deeper with a course

AI Pilots to Profit: Scaling Applied AI in Complex, Data-Driven Organizations
Ramya Ravichandar, Ph.D
View syllabus