Schneider Electric uses Databricks as a key platform within its broader data and AI ecosystem, applying Databricks’ , unified data and AI capabilities to ingest and process high volumes of industrial telemetry, power machine learning at scale across multi-cloud environments, and enable natural-language data access through Databricks Genie. Philippe built the company's AI organization from the ground up. His team of 400 is split evenly between embedding intelligence into customer-facing products and improving internal operations at scale.

From Phillipe’s perspective, the companies successfully deploying AI at scale are the ones applying the same product development rigor they would to any other capability they ship, with gate reviews, portfolio management, and teams accountable for production.

Aly McGue: As more organizations incorporate AI into their products, the line between a genuine AI-native application and a traditional product with intelligence layered on top can become blurred. How do you think about that distinction?

Philippe Rambach: The key point is that when AI is native, it is completely part of the application's value proposition. Without AI, the product either has no value or loses most of its value. We are not building something on top of the application. It is core to what the solution delivers.