Why Industrial AI Pilots Stall and How to Scale Them

Why Industrial AI Pilots Stall, and What It Takes to Scale Them

Lessons from Bruce Power, Hydro One, and InSource Solutions Group at Cimsoft Innovate

“I’ve been fooled by this before.” — Devon Keane, InSource Solutions Group

It’s not every day you hear someone who leads an Operations Intelligence practice admit he’s been fooled by AI. But that’s how Devon Keane opened our panel at Cimsoft Innovate. Before joining InSource Solutions Group, he saw demos he was sure would transform the building materials and specialty chemicals companies he worked for, and learned the hard way that a great demo doesn’t guarantee a solved problem.

That’s the story of many industrial AI pilots: impressive in the lab, stuck in the real world.

That honesty set the tone for the rest of the conversation. Moderated by Zubin Devar of AVEVA, the panel brought together:

• Devon Keane, Managing Director, Operations Intelligence, InSource Solutions Group
• Aaron Cole, Senior Manager, Grid Technology (Operating Tools), Hydro One
• Tyler Cowie, Electrical EIT, Bruce Power
• Jeff Miller, Director of Smart Manufacturing and Innovation, InSource Solutions Group
• Dan Purcell, Senior Account Manager, Actemium Avanceon

Together, they discussed what it really takes to move Operations Intelligence, the use of AI and analytics on live plant data, from experiments to results. Across very different industries, the same lessons kept coming up.

1. Start with a business problem, not the technology

The most successful AI projects don’t start with technology. They start with a problem urgent enough that the whole organization wants it solved, what Devon calls a “hair on fire” problem. That urgency matters because change is hard. Frontline teams have often worked the same way for decades, and a project driven by one group’s agenda rarely earns the buy-in it needs.

Prioritizing by value and effort helps teams find quick wins that build momentum. Track leading indicators, like how well maintenance is planned and how long repairs take. They show progress long before the downtime numbers move.

2. Find use cases in the patterns nobody can explain

Sometimes the best use case hides in a pattern no one can explain. One manufacturer’s long, continuous line broke far more often on weekends than during the week. With analytics running on AVEVA Historian and AVEVA MES data, the team narrowed the cause to four or five setup parameters and had operators keep them within set limits. Progress was slow at first, but they kept refining until the plant had its first weekend ever without a line break.

3. Build on data your experts trust

For many organizations, the first steps toward AI are cleanup. Years of silos leave the same asset with different names in different systems, and governance and security have to come first. As Aaron Cole of Hydro One put it, “Building on a shaky foundation will give you shaky results.” Experienced experts spot bad data quickly, and once they lose trust, the project stalls.

The good news is that you don’t need to digitize everything at once. Start with the problem and build a roadmap from wherever your data lives today, even a spreadsheet. Open standards like MCP (Model Context Protocol) are also making it easier for AI tools to reach historians, MES, and maintenance systems instead of staying stuck in silos.

4. Test AI pilots on messy, real-world data

Not every problem needs AI. AI shines at recognizing context-dependent patterns and taking on manual analysis that eats up hours or days. Tasks that follow fixed rules are cheaper to solve with rules.

Many pilots never scale because they run on clean, narrow data with no clear ROI target. The ones that make it are tested on messy, real-world data, with success criteria set from day one.

5. Keep guardrails, and keep humans in the loop

Strong governance may slow adoption, but it protects the data that gives a company its competitive edge. A simple test helps decide how much autonomy AI should have: What would a wrong decision cost? How serious would the consequences be? Can it be undone?

No matter how capable AI becomes, people stay accountable for what it produces. Think of a doorman: the job looks easy to automate until you see everything he really does, from reading a situation to knowing which residents need extra help. Much of industrial work is the same, and as AI takes on more of it, human judgment matters more, not less.

Advice for getting started with industrial AI

To close, Zubin asked each panelist for one piece of advice for someone starting to bring AI into live operations.

Dan Purcell: Demystify it. Nobody ever worried that an MES would take over the world, but AI gets lumped in with the fears. Show people where it helps, because the humans aren’t going away.

Devon Keane: Stay intellectually curious and get comfortable being uncomfortable. Walk the plant floor and listen to the pain points of people in other parts of the organization.

Tyler Cowie: Don’t use AI as a hammer looking for a nail. Understand the problem and what it’s costing you, so you can build the business case from day one. And remember that AI isn’t free to run. Usage costs add up.

Aaron Cole: Learn how to surf. The AI waves keep coming, from machine learning to prompt engineering to whatever is next. You can see them as dangerous, or you can line up and ride them with excitement.

Jeff Miller: You don’t need to build the entire data foundation everywhere before you start. Begin with a use case, decide where it’s going, and let your use cases fill in a consistent roadmap over time.

Don’t Get Fooled Again

The panel’s biggest lesson was that technology is often the easier part. Our latest podcast episode picks up where the panel left off, with Rob Bansek and Devon Keane on how manufacturers can get their people, process, and data ready for operational intelligence (AI).

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