Is AI in CMMS Actually Improving Maintenance—or Just Repackaging What We Already Do?

AI is starting to show up everywhere in maintenance and facility operations—but what does that actually mean for those of us managing assets, work orders, and day-to-day operations?

Traditionally, a CMMS helps us:

  • Track assets

  • Schedule preventive maintenance (PMs)

  • Manage work orders

Here’s what that looks like in practice:

What AI claims to do:

  • Predict failures before they happen

  • Automatically prioritize work orders

  • Suggest root causes based on past data

  • Optimize spare parts inventory

  • Help techs log work faster (voice, auto-fill, etc.)

What it actually depends on:

  • Clean, consistent data

  • Well-structured assets

  • Detailed work order history

Without that… it’s basically guesswork.

Real talk:
A lot of facilities are still running:

  • Calendar-based PMs

  • Manual scheduling

  • Limited failure tracking

So the question becomes… are we ready for AI, or are we skipping steps?


Discussion:

  • Are you currently using a CMMS? If so, which one?

  • Have you seen any real AI features in action, or is it mostly marketing?

  • What’s your biggest challenge today—data quality, adoption, or something else?

  • If you’re not using a CMMS, what’s stopping you?

  • Do you trust AI to make maintenance decisions, or do you still rely on technician experience?


My take:
AI has potential—but only if the foundation is solid. Otherwise, it’s just another feature that looks good in a demo.

Curious what others are seeing in real operations