The process industry companies who get most out of technology investment won’t be those who chase the buzzword of the week, but rather those who put AI technology to work where it counts, improving margins, reliability, and control. Imubit CEO Gil Cohen spent some time unpacking this in Control Global magazine—exploring the role of Controllable AI, reinforcement learning, and even GenAI in industrial plant modeling.
It’s time to shift the narrative from possibility to real, plant-specific solutions. To do so, operating companies are embracing AI that’s transparent, aligned with economics, and adaptable to site-specific complexity.
Why Controllable AI Matters Now
Considerations for process teams as AI becomes part of daily operations.
1. Trust > Black Boxes
Engineers don’t just accept black box models, they want to tinker and understand why decisions are made. Controllable AI is about giving users visibility into how decisions are made, and the ability to adjust them with confidence.
2. First Principles Still Matter
The best AI doesn’t ignore your years of engineering experience, it builds on it. Combining plant data, reinforcement learning, and your employees domain knowledge creates a system that’s both smart and grounded in reality.
3. Plant-Wide Optimization Is Becoming Practical
New AI architectures make it possible to stitch together unit models into plant-wide frameworks. This is resulting in better coordination across teams and fewer tradeoffs between throughput, energy, and margin.
4. GenAI Can Help… But Only If It’s Grounded in Plant Context
Large Language Models (LLMs) aren’t plug-and-play for process operations. But when tailored to plant data and embedded in decision workflows, they can reduce onboarding time, improve troubleshooting, and level up team capabilities.
5. It’s All About Empowering Your Experts
This next wave of industrial AI isn’t automating people out of the loop. Instead, it’s putting them at the center of it, with better tools to make faster, smarter decisions.
Read the Control Global article: The next decade of industrial process modeling.
