From PI to CONNECT. From data platforms to data trust.

On True North 🧭, I sat down with John Baier, VP Solution Strategy at AVEVA, and one of the architects behind the evolution of the PI System into today’s industrial data platforms.

John has spent decades at the core of industrial data, from Rockwell Automation to OSIsoft, and now AVEVA. He has seen the stack evolve from historians to cloud platforms to AI-driven systems. In this session, we go deep into what actually changed over the last 20 years, what didn’t, and why data reliability is now the limiting factor for scaling industrial AI.

We discuss the transition from PI to CONNECT, the reality of industrial data architectures, and why connecting and contextualizing data is only part of the story if the underlying signals cannot be trusted.

Key takeaways of this episode:

🧠 Industrial data problems didn’t disappear; they scaled

🧱 PI solved trust locally, CONNECT solves scale and collaboration

📉 Data availability ≠ data reliability

🧪 Bronze-layer data determines whether anything downstream works

🤝 Radical collaboration requires data to be graded and understood

🚫 Connecting bad data scales uncertainty

We also covered:

⚙️ Why the industrial data stack is layered, not centralized

🌐 CONNECT vs hyperscalers: complementary, not competitive

📊 Where the medallion architecture breaks in OT

🔍 Why data observability is not enough without validation

🤖 Agentic AI and the risk of scaling bad decisions faster

🧩 MCP and the shift of value from apps to data layers

⚡ AI economics, infrastructure constraints, and what happens next