“What happens if your most tenured technician retires tomorrow?”

According to Nick Haase, co-founder of MaintainX, this issue is already underway.

“Forty to 50% of the maintenance reliability workforce will be at retirement age within the next five years.”

That shift is creating a significant gap in how manufacturers maintain and operate their equipment. While companies generate large amounts of maintenance data, much of the most valuable knowledge still exists only in workers’ experience.

“That's one of the most important assets that's not captured on a balance sheet.”

In many facilities, maintenance data is still managed through a combination of paper records, spreadsheets and legacy systems, making it difficult to access or apply when needed.

“The data exists technically… but it's scattered across systems that don't talk to each other.”

This disconnect becomes most visible at the point of work. When equipment fails, technicians often rely on personal experience or informal communication instead of structured data.

“A technician standing in front of a machine that's down doesn't need a dashboard. He needs the repair history for that specific asset.”

As veteran employees leave, that knowledge gap widens. New workers entering the field may lack both the experience and the resources needed to respond effectively.

“What happens if your most tenured technician retires tomorrow?”

Haase argues that capturing and structuring this knowledge is critical for long-term operational stability.

“The window to capture institutional knowledge is closing.”

At the same time, manufacturers are under pressure to maintain productivity with fewer experienced workers. This is where digital tools and AI are beginning to play a role.

“You can meet the worker where the work happens.”

Mobile-first systems allow technicians to access and record information directly on the shop floor, helping ensure that knowledge is captured in real time rather than lost.

As more structured data becomes available, AI can help surface relevant insights and guide decision-making.

“The real breakthrough isn't that AI models got smarter, it's that we now have enough structured digital maintenance data.”

These tools can help newer workers perform tasks more effectively by providing access to historical data and recommendations based on past performance.

“When a technician asks the question, AI surfaces the answer.”

However, Haase emphasizes that technology alone will not solve the problem. Organizations must also focus on how they capture and use data.

“The hardest part isn't implementing the software… it's doing that change management.”

Without a structured approach, valuable information will continue to be lost as employees retire.

“You can't compress three years of structured work history into three months.”

As the workforce continues to shift, manufacturers that prioritize capturing institutional knowledge will be better positioned to maintain operations and improve performance.