Why AI Fails in Manufacturing Maintenance Without Frontline Knowledge

How many times have you seen a veteran technician share a quick fix that wasn’t anywhere in your documentation? Or flag an issue just based on a whining noise or a strange smell?

Probably hundreds. That’s because technicians spend countless hours interacting with equipment, troubleshooting problems you won’t find in a manual and building up their knowledge of an asset.

This knowledge is what makes experienced frontline workers critical to getting the full potential from emerging technologies, especially AI. It’s the kind of data AI thrives on: reliable, contextual, and most importantly, can’t be inferred or collected from other sources.

Read on to see why frontline workers should be at the center of your AI strategy, and how the right technology can make a tangible difference to your maintenance operations.

‍Why labor shortages in maintenance are the biggest threat to AI success for manufacturers

On average, industrial employees, including maintenance pros, stay in their jobs 25% longer than other private-sector workers, accumulating vital experience every day. More time spent in the field means they have a deeper understanding of your equipments’ quirks and nuances.

However, the depth of knowledge that exists in maintenance can be both a blessing and curse. If it isn’t preserved and passed on, decades-worth of knowledge can be lost in a single day.

With 40% of the sector’s workforce set to retire by 2030, the loss of institutional knowledge is a massive threat manufacturers will soon face. Some are already feeling the impact of labor shortages—the aviation industry in North America loses 11.5% of its maintenance workforce every year to retirement.

Losing this knowledge brings risks to efficiency and safety for manufacturers. It can also limit the return they see on their investments in AI, since the most accurate and helpful AI outputs are driven by data that is:

• Deep: There’s a large sample size to draw from
• Accurate: Errors and outliers are few and far between
• Contextual: Specific to the equipment, workflows, and scenarios you encounter

Every piece of institutional knowledge that walks out the door means AI has fewer data points that power it, making it less and less effective.

How to embed AI in existing technician workflows

Manufacturers need better ways to preserve maintenance professionals’ know-how. Legacy IT systems, spreadsheets, or paper filing systems just won’t work. They force maintenance technicians to retain knowledge for hours before logging it, which often means the information is never captured accurately. 

Instead, organizations need to embrace mobile devices and AI technologies that fit the technician’s workflow and can capture insights on the factory floor. These tools can continuously gather data from work orders, voice memos, and technician notes. Over time, this evolves into a record of how assets work, and what to do when they don’t.

You can then feed this information into AI technology that helps all technicians complete day-to-day work safely and efficiently. For example, maintenance workers can use AI copilots to surface work orders, checklists, technical documentation, and standard operating procedures.

Such tools can be both intuitive and accessible, allowing technicians to ask questions and get the information they need in seconds.

How manufacturing leaders build an AI strategy with maintenance at the center

Many AI strategies never translate successfully from a slide deck to the real world. Maintenance offers industrial companies the opportunity to avoid this trap and drive improvements with AI, as long as they do a couple of key things:

1. Remember that every digital transformation starts with a single step

The vision described here isn’t something manufacturers can build out all at once. Instead, creating an AI program should be approached as a series of small steps. For example, you might start by adding standard operating procedures and technical documentation into a digital framework. This incremental progress quickly builds up, encouraging both technicians and plant managers to lean into the technology to get more value.

2. Foster a culture that genuinely values maintenance insights

If your company sees technicians only as guys with wrenches, you’ll struggle to activate their knowledge. But once managers fully grasp the strategic power of maintenance data and its benefits across operations, it gets a whole lot easier to back up investments that help collect technician insights.

The power of maintenance data

Manufacturers that use technicians' expertise to train algorithms, power advanced analytics, and develop new AI solutions will be the ones that gain a competitive advantage. And it won’t take a decade to determine the winners and losers—that will be determined in just months. 

Ultimately, the industry's future depends on combining human expertise with AI innovations. By activating maintenance data, industrial organizations have an opportunity to preserve vital knowledge while gaining powerful new efficiencies across their operations.

This post is adapted from an article written by MaintainX CEO Chris Turlica and originally published by the Forbes Technology Council.