From assumed data to trusted decisions.

On True North 🧭, I sat down with Raj Munusamy, Product Portfolio Manager for Data Platforms & Agentic AI at Saint-Gobain, a 350+ year-old industrial giant operating across 1,200+ manufacturing plants worldwide.

Raj is a rare bridge between worlds: automation & control, historians & OT systems, and modern cloud-scale data platforms and AI. In this session, we go deep into what really breaks when industrial data scales, and why data trust, not algorithms, is the limiting factor for AI in manufacturing.

We discuss Saint-Gobain’s Metriks manufacturing data platform, the reality of Bronze / Silver / Gold data layers, and the often-overlooked “historian gap” where data-quality context is lost as signals move from sensors to dashboards and models.

Timeseer.AI engagement 🤝
Timeseer.AI is deployed as the Trust Layer next to the historian, currently live across 70+ Saint-Gobain plants (and scaling) to continuously validate Bronze-layer time-series data, detect issues early, and restore confidence in the data that feeds dashboards and AI.

Key takeaways:
🧠 Data availability ≠ data trust
🧪 The Bronze layer is where trust is won or lost
📉 Missing, stale, drifting data silently kills dashboards and AI
🧩 Historians store data, not confidence
🚫 Auto-fixing data can hide root causes in continuous manufacturing

We also covered:
⚙️ The “historian gap” (Sensors → PLC → SCADA → Historian)
📊 Why teams still spend massive time validating data
🔍 Detect → Score → Resolve → Serve as a trust framework
🛠️ Why Saint-Gobain chose a trust layer instead of building ad-hoc fixes
📈 What it takes to scale data trust across plants, teams, and use cases

No hype. No theory.
Just what needs to be true for industrial analytics, AI, and autonomy to scale.

🎙️ True North Podcast

Building for tomorrow, not just solving today’s problem.

On True North 🧭, I sat down with Robert Feldmann, Director of Manufacturing Industry at Microsoft — formerly Associate Partner at McKinsey and today also an industrial advisor to Timeseer.AI. At Microsoft, Robert helps manufacturers bridge the gap between shop-floor reality and cloud-scale AI, shaping how industrial companies move from automation to autonomy.

Key takeaways:
🧠 AI is a processing engine.
⛽ Data quality is the fuel.
❌ No trusted data, no reliable outcomes (+ why).

We also touched on:
🧩 Fragmented industrial knowledge
🔁 Broken OT–IT workflows
⚡ Energy & compute constraints
🏭 Why factories that don’t adapt may disappear
🚀 AI as a platform shift, not a bubble

No hype. Just what needs to be true for AI to work in industrial reality.

🎙️ True North Podcast

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

On True North 🧭, I sat down with Stijn for a sharp conversation about strategy, AI, and long-term advantage.
We go way back, school friends, and today he’s one of the builders behind a Belgian unicorn in data governance.

What made this conversation special is where our worlds meet: OT and IT data quality and governance.
The point where operational reality meets enterprise decision-making, and where trust in data becomes non-negotiable.

𝗙𝘂𝗻 𝗳𝗮𝗰𝘁: Amazon reportedly uses Wardley Mapping to support strategic decisions, even M&A thinking.

We talked about:
🧭 Wardley Mapping to see what’s truly strategic vs already commoditized
🏗️ Why solving today’s pain often creates tomorrow’s constraints
📊 How OT and IT data architectures converge in the age of AI
🤖 The road from today’s AI toward AGI, and what that means for builders
⚖️ Balancing execution speed with strategic clarity

Big thanks to Hans de Leenheer for educating us on Wardley Mapping, and to Simon Wardley for inventing this powerful strategic lens.
If you’re new to it, start here: https://learnwardleymapping.com