At the manufacturing conferences I’ve attended in recent months, two topics dominated nearly every conversation: AI and the labor shortage.
One promises seemingly unlimited potential. The other threatens to severely limit growth.
But some manufacturing leaders are making progress on both fronts—and their advantage isn’t technology alone. It’s how they’re pairing AI with human expertise to make better decisions, empower workers, and reduce downtime.
Here are four ways that manufacturers can learn from these leaders and use AI to help solve problems that improve output and boost worker retention.
1. Shorten the distance between problem and resolution
In many manufacturing environments, solving an issue means a worker must talk to five or more people before they’re able to address the actual problem. Each escalation adds delay, downtime, and disruption.
If you could cut the number of people they needed to consult in half, you’d save workers a significant amount of time.
Imagine this: The hydraulic press an employee is working on rattles then stops functioning. They don’t know why, so they ask more experienced operators for advice until they find someone who knows the right fix. That leads to a lot of unplanned downtime.
Instead of manually escalating issues through company ranks, AI can surface the right expertise instantly. For example, technology exists that lets experienced operators share their knowledge via voice recording and makes the information searchable.
This data source could be incorporated into an organization’s unified data foundation, which workers could then query via an AI-powered interface. The response could offer enough information for the employee to address the issue themselves or to go directly to the person best equipped to solve the problem.
With the right tools, you’ll empower operators to take action more quickly, solving problems while they’re small. That leads to fewer mistakes, less equipment damage, higher-quality output, and less unplanned downtime.
2. Build a data foundation to enable better decisions
Disorganized, siloed data prevents workers from having all the information they need to make smart decisions. But a foundation of clean, unified data enables AI to uncover patterns and opportunities.
One of our rail and fleet clients, a railcar service provider, learned the power of a strong data foundation first hand. The servicer was managing yard switching manually, moving magnets around on a white board to track car location. Technicians asked the same questions over and over to plan their days:
Which cars are at what stage of servicing?
Which parts are in stock?
What’s the optimal configuration of the rail yard?
While the process was effective, it was outdated, inefficient, and prone to errors. Switching tracks is labor-intensive, and moving cars takes time. If the servicer could make the process more precise, they could reduce cars’ idle time in the yard, complete service faster, and therefore have capacity to service additional cars—thus increasing revenue.
We identified an opportunity to digitize the whiteboard by creating a heat map of the yard and combining it with other AI-ready system data. With an AI layer added to this data foundation, the digital version of the railyard can determine the most efficient layout each day and forecast “what if” scenarios, like how to pivot in case of a material shortage.
3. Design AI that increases ownership, not oversight
AI is often portrayed as a job-replacement technology, but it can actually support something critical for retention: growth. Today’s manufacturing worker sees learning and using new technologies as an opportunity. In fact, 75 percent say that more up-to-date technology would increase their engagement.
Skills development is especially important among younger workers. Gen Z are 68 percent more likely to prioritize learning and development benefits when looking for a job than their older counterparts. And 46 percent of that cohort said they’d quit a job with limited growth opportunities.
I’m reminded of my daughter, who uses Snapchat’s AI feature to take a photo of her homework and generate more problems for additional practice. Similarly, manufacturers can give their employees access to AI technology and let them experiment.
Tech-native workers will likely find surprising ways to apply AI to their work, which both gives them a sense of ownership and can lead to gains across the organization.
By training workers to use new technologies and AI tools, you’re investing in them and helping them grow their skillsets.
4. Improve workforce flexibility by expanding skillsets
One challenge of short worker tenures is that training workers to do more than one type of work can be challenging. This, in turn, can lead to a less-flexible workforce and increase the likelihood of staffing-related downtime.
At the same time, finding ways to diversify training might help improve both productivity and retention. When workers feel that they’re being invested in, they may be more likely to stay.
Here’s a recent example from our Modern Industrialist podcast. The president and CEO of HABCO Industries, which specializes in manufacturing support and test equipment for aerospace, used AI technology to shorten the training process for stenciling work, which had previously been a source of bottlenecks in the production line.
By incorporating an augmented reality (AR) headset into the stenciling process, workers were able to learn the skill and complete individual tasks faster. This reduced stenciling time from three hours to 30 minutes for a large production run, thus making the operation economically feasible.
HABCO’s workers learned more skills, and the stenciling process no longer risked bottlenecking production. While this particular application may have limited relevance for other workplaces, the principle is invaluable: find “digital centaur” applications where AI empowers workers to get better at the things only they can do.
AI isn’t a one-way tool for empowering workers
The key to establishing an effective AI-human connection is to think about AI expansively. Rather than acting simply as a digital instruction manual, AI tools can evolve as workers use them and discover new applications.
When operators can feed observations, context, and expertise back into AI systems, those systems improve. Problems are resolved faster. Decisions are grounded in reality. Ultimately, workers gain ownership rather than oversight.
While there’s no one-size-fits-all formula for making AI work in manufacturing, these principles can help leaders strategize applications that improve both productivity and employee experience.
About the author
Jason Hehman is the industrials vertical lead at TXI, a boutique digital consultancy for modern industrial leaders. TXI co-creates intelligent products that reduce risk, activate data, and empower the workforce — delivering outcomes that last. Hehman is also the founder of the Modern Industrialist Xchange (MIX), a curated space where leaders in manufacturing, supply chain, and industrial innovation connect through gatherings and shared insights.
