Practical Strategies to Help Your Maintenance Team Embrace AI

I recently walked through a facility where million-dollar robots operated with microscopic precision, while two bays over, technicians were debating whether an AI-generated work order was worth trusting. This tension between technological capability and human adoption is the defining challenge of maintenance in 2025.

While it’s no secret that organizations are adopting AI at accelerating rates—according to our 2025 State of Industrial Maintenance report, 44% of maintenance teams have already implemented AI in some capacity—frontline workers might not have the same enthusiasm for the technology.

Through my work with maintenance teams across industries, I've seen what separates successful AI implementations from expensive failures. The difference isn't in the sophistication of the technology, it’s in how thoughtfully organizations introduce it to the people who matter most: frontline workers.

I recently joined the Great Question podcast to talk about the challenges facing manufacturing teams as they adopt AI. Read on for actionable takeaways in my conversation with Roshan Satish, lead product manager at MaintainX, and Thomas Wilk, chief editor of Plant Services, then listen to the episode for more.

Key Takeaways:

• Build your AI strategy around conversations with frontline workers
• Think about gamification over ROI calculations
• Start narrow and deep, not broad and shallow
• Embed AI into existing workflows, don't replace them

Get the Right People in the Room

Many maintenance teams have been operating the same way for decades. Some resist change because they've been burned by "revolutionary" technologies that overpromise and underdeliver. Others simply haven't seen compelling evidence that new tools will make their jobs easier.

The root of this resistance? Organizations consistently buy digital tools for the “carpeted side” of the house without meaningful input from workers on the “concrete side” who will actually use them.

But here's the opportunity: With this wave of AI technology, leaders can reset this dynamic entirely.

Start with these conversations:

• "What's the most frustrating part of your day that you think technology could actually help with?"
• "What's one thing you wish you could do faster or more accurately?"
• "What technology have you tried before that didn't work as promised, and why?"

Real example: One facility I worked with discovered their technicians weren't resistant to AI—they were frustrated that previous "smart" systems couldn't handle the nuanced decision-making their experience provided. When they redesigned their AI tool to augment rather than replace that expertise, adoption soared.

The best AI strategies start with the people who will use the tools daily, not the people who will measure the tools’ ROI quarterly.

Put Excitement Before ROI

Here's a counterintuitive truth: Leading with ROI calculations often kills AI adoption before it starts.

While organizations need to justify technology investments, the human element matters more in the early stages. The question isn't "Will this save money?" but "Will this make someone's day better?"

Think gamification, not optimization:

• Can technicians complete routine tasks faster and move on to more interesting work?
• Does the AI tool help them look more competent or knowledgeable?
• Are there small wins that make people feel successful?

Practical example: Instead of leading with "This predictive maintenance algorithm will reduce downtime by 15%," try "This tool can instantly tell you the exact part number and pull up the procedure, so you're not spending 20 minutes hunting through manuals."

When technicians experience that time-saving magic a few times, they start trusting the tool. Then when it suggests replacing a pump that seems fine, they're more likely to listen because the AI has already proven its value in smaller ways.

The progression: Excitement → Trust → Habit → ROI

Start Smaller (With a Specific Framework)

The organizations I see succeeding with AI share a common approach: They resist the temptation to solve everything at once.

The "One Process, One Line" Rule: Instead of implementing AI across all maintenance processes, successful teams pick:

• One specific process (like vibration analysis or work order generation)
• On one production line (where they can control variables and measure results)
• With one team (who can become internal champions)

Why this works:

1. Quality data: Narrow focus means better, more consistent data to train on
2. Clear measurement: Easy to tell if it's working when you're only changing one thing
3. Manageable risk: If it fails, you haven't disrupted everything
4. Proof of concept: Success in one area builds confidence for expansion

Real progression I've seen:

• Month 1-3: AI helps with parts identification on Line 2
• Month 4-6: Expand to work order generation for the same line
• Month 7-9: Apply successful approach to Line 3
• Month 10+: Scale across facility with confidence and lessons learned

Red flag: If your AI implementation plan mentions "comprehensive digital transformation" in the first phase, you're probably trying to do too much too soon.

Build a Habit (With Specific Tactics)

The goal isn't to make people think about AI—it's to make them forget they're using it.

Habit-building strategies that work:

1. Embed AI in existing workflows

• Don't create new processes; make existing ones easier.
• If technicians already log end-of-shift notes, help AI make that faster.
• If they already check equipment readings, let AI suggest optimal timing.

2. Choose internal champions strategically

• Pick respected technicians, not just early adopters.
• Give them extra training so they can help others.
• Celebrate their wins publicly to build social proof.

3. Create gentle nudges

• "Before you clock out, try using the AI to summarize today's work."
• "Next time you need a part number, ask the AI first."
• "When you're stuck on a procedure, see if the AI can pull it up faster."

4. Make it obviously better 

The AI interaction should be faster, easier, or more accurate than the old way—not just "different."

Example of good habit formation: One team I worked with started by having technicians use AI to convert their handwritten notes into digital work orders at the end of each shift. Within two months, they were naturally asking the AI for troubleshooting suggestions during complex repairs because it had already proven useful for the simpler task.

What NOT to Do

❌ Don't lead with cost savings
"This will eliminate two positions" kills adoption faster than anything else.

❌ Don't ignore the skeptics
Your most experienced technicians often have the best insights about why previous tech failed.

❌ Don't make it optional indefinitely
After initial buy-in, gently make AI tools part of standard procedures.

❌ Don't forget about data quality
AI is only as good as the information it's trained on. Garbage in, garbage out.

❌ Don't skip the training
Even intuitive tools need proper onboarding for success.

The Way Forward

The future of maintenance isn't about replacing human expertise with artificial intelligence. It's about amplifying human expertise with better tools.

Your next steps:

1. This week: Have conversations with three frontline technicians about their biggest daily frustrations.
2. This month: Identify one narrow process where AI could provide immediate, obvious value.
3. Next quarter: Run a small pilot with willing participants and measure both efficiency and satisfaction.

The bottom line: AI adoption in maintenance succeeds when it makes people's jobs easier, not when it makes spreadsheets look better. Focus on the human experience first, and the business results will follow.

If you're not thinking about how AI-generated insights will make it into the hands of someone who will act on them—and want to act on them—you're missing the point entirely.

The organizations that crack this code won't just see better maintenance outcomes; they'll have teams that are excited to come to work and tackle complex problems with powerful tools at their disposal.