Building your own CMMS with AI?

With today’s code-generation tools, putting together a prototype that lists assets and work orders is a matter of days. That has made the question come up in many companies: if we can build it ourselves, why buy it?

It’s a legitimate question and deserves an honest answer, not a sales pitch. What follows is what’s easy, what isn’t, and in which cases building it yourself actually makes sense.

What AI solves well

Worth acknowledging without hedging, because it’s a lot.

CRUD. Create screens, listings, filters, forms and relationships. What used to be weeks of repetitive work is now quite fast.

The initial structure. A reasonable data model, basic authentication, deployment. A working prototype in no time.

The visual layer. Decent interfaces without needing a designer.

If your need is an orderly record of equipment and jobs for a small, stable operation, it’s a real option. It shouldn’t be dismissed on principle.

What it doesn’t solve, and where the real work is

The problem is that a CMMS isn’t a CRUD. The part that’s hard isn’t the part that gets generated fast.

True offline mode. It’s not about saving screens: it’s a local database, an action queue, conflict resolution and defined behavior when something fails to sync —which can’t mean silently losing a morning’s work—. It’s one of the hardest problems in applied computing, and maintenance happens exactly where there’s no coverage.

Generating preventive maintenance. It looks like a cron job and it isn’t. You have to resolve multiple recurring schedules on the same asset, each with its own count; check holidays and technician availability before generating; resolve the maintenance plan in cascade by asset, model, subfamily and family; and handle triggering by meter reading when a value crosses a threshold. Each of those rules comes from a real case someone discovered the hard way.

Access control. Roles, document visibility by role, what each client and each provider sees. And traceability: who created, modified or deleted each record, with recoverable deletion.

Integrations. Every ERP does things its own way. A connector that works in production isn’t just an API call: it’s error handling, retries, reconciliation and years of edge cases.

History as evidence. For a record to hold up in a claim or an inspection, no one can be able to alter it without leaving a trace, and every job has to be attributed to whoever did it.

The cost that doesn’t get calculated

Initial development is the small part. What gets underestimated is what comes after.

Maintaining the system itself. Security updates, dependencies that go stale, mobile platform changes. It’s ongoing work, not a project.

Support. When something breaks on a Friday afternoon and the person who built it is on vacation or no longer works there.

Dependence on one person. It’s the most serious risk and the least visible one. A system custom-built by a specific person becomes a problem the day that person leaves, because no one else understands why things are the way they are.

Infrastructure. Server, backups, monitoring, availability.

What you don’t know you need. The features that aren’t on your first list because you haven’t yet run into the problem that made them necessary.

And about AI inside the product

Worth separating two things that get mixed up in this conversation: using AI to build the system, and having AI inside the system.

In GMAO Cloud, AI does bounded, verifiable things: an assistant that answers questions about your assets, your plans and your history —also inside the technician app—; document reading, so that the report a supplier sends you as a PDF becomes data without typing it in; help drafting work order text; and translation of your own catalogs.

Two things matter more than the list: it proposes, it doesn’t execute —it doesn’t close work orders or assign work—, and every feature has its own toggle, with most switched off by default. It’s detailed in AI in GMAO Cloud.

And the usual caveat, which applies just as much to a system you build yourself: an assistant on top of an empty history knows nothing. AI doesn’t replace the record, it relies on it.

When building your own actually makes sense

There are cases where the answer is yes, and they’re worth naming.

When your process is genuinely atypical and no product on the market covers it, because that particularity is your business.

When the system is a piece of your product, not an internal tool.

When you have your own stable development team, with the capacity to maintain it for years, and maintaining it is part of your core business.

Outside of those cases, the usual pattern is that the prototype works, gets used for a few months, and gets abandoned when the first real demand shows up: a technician with no coverage, an inspection asking for the file, or a client wanting their own portal.

A middle option that gets overlooked

It’s not all or nothing. If what’s pushing you to build is a specific need that no product covers, there’s the connect route: there’s a public REST API and SQL access to read and write to the system from your own development.

That way you keep the hard part solved —offline mode, preventive maintenance, permissions, traceability— and only build your specific piece on top. It’s almost always the best balance between control and cost.

How to decide

Make a list of what you actually need —not what would be nice— and mark which part is CRUD and which part is the hard stuff. If most of it is the latter, the prototype won’t save you the work: it will just postpone it.

And calculate the cost over three years, not three months, with maintenance and support included.

If you want to check this against your specific case, you can write to us or request a demo to see what part of your list is already solved.