AI is everywhere, but turning it into real outcomes in dairy and food processing is where the value is. In this episode, Anna Olsson, co-founder of Intelecy (a no-code platform for industrial AI), cuts through the hype to show what plants can do today: predict failures before they happen, optimize processes in real time, and capture expert know-how so it scales across sites.
If you run operations, engineering, maintenance, or production IT, you will get practical steps to start fast, prove ROI, and avoid pilot purgatory.
In this episode, you’ll discover:
1. The “learn, act, detect” framework for industrial AI
2. Why data quality and coverage beat big promises
3. How to move from pilots to scaled, maintained models
4. Where predictive maintenance ends and process optimization begins
5. How no-code tools bridge the IT–OT gap and protect operator trust
Episode Content
01:57 After ChatGPT – expectations vs industrial reality
03:06 LLMs vs industrial AI and time-series sensor data
04:47 The “learn, act, detect” framework for process optimization
05:26 Predictive maintenance in practice and planning stops instead of reacting
06:40 Predicting future process states and adjusting before quality drifts
10:23 Tacit know-how and “knocking on pumps” vs data-driven models
12:21 Prerequisites for AI: stored sensor data and data quality
15:20 Case: how TINE detects bacterial contamination with AI
17:28 Energy optimization and small savings that add up 24/7
19:37 Why AI projects fail and end up in “pilot purgatory”
21:02 Build vs buy – scaling beyond the first AI model
31:25 Towards Industry 4.0 – closing the loop from prediction to automation
This podcast is brought to you by Au2mate.This podcast is produced by Montanus.