Bryan DeBois is the Director of Industrial AI at RoviSys, one of the world’s largest independent system integrators serving manufacturing and industrial companies. With more than 20 years of experience in operational technology, manufacturing execution systems, and plant-floor data infrastructure, Bryan focuses on operationalizing artificial intelligence to improve productivity and decision-making in industrial environments.
Here’s a glimpse of what you’ll learn:
- [03:15] Why generative AI is often the wrong solution for plant-floor operations.
- [06:50] The difference between knowledge problems and operational problems in manufacturing.
- [07:20] How predictive and analytical AI models improve process performance.
- [09:00] Why companies must document processes and collect data before using AI.
- [13:30] How “machine teaching” captures expert knowledge and trains autonomous AI systems
In this episode:
AI is powerful—but without structured processes and reliable data, it cannot deliver meaningful results.
In this episode, Adi Klevit interviews Bryan DeBois about how artificial intelligence is transforming manufacturing and industrial systems. Bryan explains why the excitement around generative AI often overlooks the unique realities of plant-floor operations, where errors can have serious consequences. In high-stakes environments like manufacturing, organizations must carefully choose the right type of AI for the job.
Adi and Bryan discuss the difference between generative AI, predictive models, and autonomous AI systems. Bryan shares how manufacturers can use predictive analytics to forecast product quality, optimize set points, and improve operational performance. These models are designed to solve operational problems rather than simply generate information.
The conversation also highlights a critical systems principle: AI only works when businesses have already documented their processes and established strong data collection practices. Bryan explains how companies capture expert knowledge through a process called “machine teaching,” allowing AI systems to learn from experienced operators and apply that expertise to improve future performance.
Resources mentioned in this episode:
- Adi Klevit on LinkedIn
- Business Success Consulting Group
- Bryan DeBois on LinkedIn
- RoviSys
- RoviSys AI
Quotable Moments:
- “The stakes on the plant floor are much higher.”
- “Generative AI solves knowledge problems, but manufacturing has operational problems.”
- “AI can’t replace the foundational work of systematizing processes.”
- “You can’t automate what doesn’t already exist as a system.”
- “Machine teaching captures the expertise of your best operators.
Action Steps:
- Evaluate whether your AI initiatives are solving operational problems or simply generating information.
- Document and standardize key processes before introducing automation or AI tools.
- Ensure reliable data collection systems exist on the plant floor.
- Identify expert operators whose knowledge should be captured and transferred.
- Explore predictive and autonomous AI models that improve operational decision-making
Sponsor for this episode:
This episode is brought to you by Business Success Consulting Group.
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