Manufacturers have spent years debating digital transformation. Lately, AI has been accelerating those conversations, pushing the industry into something faster, more disruptive and more automated than ever before.
While most manufacturers took years to modernize, and some might still be early on in their digital transformation, the adoption of AI is happening at a pace normally unseen in the industry.
In MaintainX’s recent State of Industrial Maintenance Report, a survey of more than 1,300 maintenance and reliability professionals, nearly half said they’re already using AI in some form, which is a significant increase from just 12 months ago. While AI adoption is still sporadic, such as technicians asking AI models to troubleshoot questions, it signals something important. Frontline teams are leaning into the technology not only to secure quick wins and establish preventative maintenance models, but to document vast repositories of veteran expertise before they leave the factory floor for good.
For all its momentum, though, AI adoption isn’t evenly distributed. Some manufacturers are fully committed; others are barely testing the water. The next 24-48 months will only serve to widen that gap dramatically. Here’s what leaders need to understand and what’s coming next for the workers who keep our factories running.
AI on the factory floor
The easiest and quickest wins with AI don’t involve complex integrations. They’re simple, high-frequency tasks that maintenance teams know will eat up valuable time and can clearly be solved by the new technology:
- Drafting work orders and standard operating procedures
- Troubleshooting with uploaded manuals or error codes
- Translating instructions or documentation for frontline teams with global colleagues or customers
But as companies evolve their deployment of AI from enabling reactive to preventative maintenance systems, the technology will become more deeply embedded into the running of a facility. AI is set to become the backbone of operations by predicting failures before they happen, automating parts planning and eliminating hours spent tracking down components during downtime.
We’re starting to see early examples of this next stage with manufacturers who are further along the maturity curve, and it’s a preview of what will soon become standard across the industry.
However, in order to evolve their AI deployments from reactive, to preventative and eventually to predictive maintenance systems, manufacturers must first address two major barriers to successful AI integration.
The biggest barrier is still the basics: Bad data and digital gaps
Despite increasing AI adoption among manufacturers, most still don’t have the most important input that preventative AI models require: clean, structured data. If work orders, inspections and maintenance logs live in binders or emails, AI has nothing to analyze, learn from, or build on. Unsurprisingly, our State of Industrial Maintenance Report shows that despite 71% of companies reporting that they run preventative maintenance programs, less than 35% of them spend most of their time actually performing it. Maintenance teams are stuck chasing fires, not building repeatable systems.
AI can’t put those fires out on its own. Without digitized workflows and clean data, there’s nothing for AI to analyze or act on, effectively stalling AI evolution. Digitizing workflows is the prerequisite that allows companies to move from firefighting to proactive, predictive maintenance models.
In other words, the challenge isn’t a lack of ambition but the absence of a usable foundation. Once teams modernize their workflows and start generating reliable data, AI can finally shift maintenance from reactive chaos to a predictable, preventative system. The path forward becomes clear: the sooner manufacturers build that digital baseline, the faster they turn today’s stalled promise into measurable operational change.
But the barriers aren’t just technical; they’re also cultural. A lingering concern persists that AI is somehow “cheating,” a worry that echoes the early days of search engines. Yet—just as Google taught us nearly 30 years ago—trust, verification and iteration don’t replace human judgment, they strengthen it.
Speed and agility will define manufacturing leaders as they experiment, pilot and pivot their AI implementations. Those who wait for perfect data or perfect models will be waiting forever, hindering their workforce, operations and outputs.
As manufacturers balance digitizing workflows and working through cultural hesitation to AI, they face another pressing challenge: replacing retiring veterans, who know the ins and outs of the facility, with skilled and experienced technicians. AI will become essential for maintaining productivity with fewer people and will allow new technicians to integrate more quickly into the complex machinery of the factory floor.
The aging workforce isn’t a roadblock—It’s a catalyst
As the manufacturing industry faces the second-highest workforce retirement rate among industries, AI implementation is even more critical as a means of recording and gathering irreplaceable knowledge from veterans before they retire. Capturing this tribal knowledge is a race against time, as tens of thousands of veterans carry decades of expertise with them into retirement.
Given the long-running assumption that older technicians will be the most resistant to incorporating AI into their workstreams, many manufacturing leaders are surprised to find that it's actually senior workers, who are just shy of retirement, that are the AI power users.
Why? Because AI unlocks quick, clear wins. A technician doesn’t need long training modules or complex software. They simply ask AI a question they already know how to articulate: “What does this error code mean?” “What’s the standard diagnostic for this symptom?”. The value of AI becomes obvious immediately to anyone.
Retiring workers give manufacturers a rare opportunity to capture sometimes 40 or 50 years of hands-on knowledge and convert it into training materials, troubleshooting logic and maintenance suggestions. These technicians also tell us they appreciate how AI makes them feel smarter, by recapping their thoughts in a clear, polished way. This becomes a powerful motivator for adopting AI, which helps engage younger teams early for a smooth transition.
With the retiring and the next generation of maintenance engineers working together, and sharing an understanding of how to use the technology, expect a reduction in any cultural hesitation. Everyone benefits when information that once took hours–finding a manual, calling three different former technicians, digging up paper-based notes–is reduced to mere minutes. But younger engineers will feel more comfortable that the AI deployment is backed by the vast expertise of their industry veterans.
Building the future now
AI isn’t coming to manufacturing as a distant, futuristic promise, it’s been reshaping how frontline teams work, learn and solve problems. But the manufacturers who benefit most won’t be the ones with the flashiest pilots or the biggest budgets. They’ll be the ones who focus on the investing in the fundamentals:
- Digitize workflows. AI can’t consume paper.
- Instrument assets where it matters. Sensors and clean data unlock predictive maintenance.
- Train workers to trust, verify and iterate. AI is only as good as the humans guiding it, and the workforce should be empowered to use AI as confidently as any other tool in their kit.
As the workforce shifts and veterans exit the floor, AI becomes the connective tissue that keeps tribal knowledge flowing and performance consistent. Manufacturers that embrace this moment—thoughtfully, urgently and with their frontline teams at the center—will be the ones that turn today’s digital transformation into tomorrow’s competitive advantage.
