The U.S. is building factories faster than it's educating people to run them. The Manufacturing Institute and Deloitte project a net need for as many as 3.8 million manufacturing jobs in this country between 2024 and 2033, with up to half of those jobs going unfilled if the skills and applicant gaps hold.
The demand for labor is very real. U.S. industrial growth is running at a pace we haven't seen in decades, driven by the return of production for domestic consumption, along with advanced manufacturing, semiconductor production and data center infrastructure. At the same time, many of the people in our critical trades are aging out, and for years there weren't enough apprenticeship programs to bring up the next generation.
Filling the current talent gap is partly a matter of headcount. But on an automated floor, it's mostly a matter of skill. So, a manufacturer’s response to the hiring challenge should have two parts: upskill the workforce you have and partner with education to build the skilled workforce you'll need. Experienced engineers and technicians carry operational knowledge that no software replaces, so we build new capability on top of it, pairing veterans with newer engineers rather than trading one for the other.
You also have to consider that the work of manufacturing changes the moment you design a process to be automated. As a floor automates, the skill set widens past traditional manufacturing into four areas: software, hardware, controls, and design for automation. The last one is where most of the change lives. Knowing how a process works is no longer enough; an engineer has to design it so it can be automated at all.
A traditional industrial engineer took in raw components, learned their specifications and moved them through the plant. The job now is to design for automated receipt and put-away, and for autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) to move through the factory themselves. That is a different way to think about material and product flow.
It runs in a specific order: simplify the process, optimize it, standardize it and then automate. Skip any of those first three and the automation is set up to fail. Say three suppliers ship the same part in three different packaging configurations. A line set up for one breaks when the others arrive, because the equipment cannot make the judgment call a person used to.
There's a shift in how decisions get made as well. Manufacturing is moving from reacting to problems toward predicting them, which puts a premium on people who can work with data and analytics. As data literacy is becoming as important as mechanical knowledge, it changes the engineer's day. They now spend less time reacting to what broke and more time designing systems that keep it from breaking. Proficiency with AI tools is helpful too because engineers need to be comfortable working alongside intelligent systems rather than simply operating equipment.
Maintenance is the clearest example. Most preventive maintenance runs on a clock or cycle count: replace the part every 1,000 actuations, worn or not. Put an AI agent on that machine to watch for a change in vibration, added resistance, or a slowing cycle, and it predicts the failure instead.
You stretch the interval and take less downtime. It saves money on spare parts, too. Data that used to sit in a dashboard now becomes the input for the decision. Tools we deploy now didn't exist in the same form a few years ago, and they'll keep changing. For manufacturers, this makes adaptability maybe the most valuable skill they can develop. The workforce we need combines manufacturing fundamentals, automation knowledge, data literacy and that capacity to keep learning as the field's norms and challenges change.
The partnerships that work put manufacturing professionals in front of students. An engineer who teaches from today's production floor gives a view of the industry that no standard curriculum can. A student who trains on the tools we use and earns an industry-recognized certification arrives ready to contribute.
So far, our success rate has been highest with smaller trade schools and junior colleges across the United States. They're more willing to evaluate their curriculum and collaborate with industry. These schools adapt faster, too; larger institutions tend to run on a standard platform and move more slowly. The industry data tracks with that experience: manufacturers partner with technical colleges more than any other kind of institution, 73%, ahead of universities at 48%. Large universities are starting to shift, too, with supply-chain programs adapting to AI the fastest.
The apprenticeship gap is the sharpest version of the problem. Toolmaking for injection molding is one of the hardest roles we fill, because that workforce is aging out and there were never enough programs feeding it. So we connect with trade schools to provide equipment, curriculum support, and mentorship. In return, they run certification programs that speed talent development and serve the community. Every one of our more than 30 U.S. factories has some level of engagement with a technical school, trade school, or university.
No curriculum can keep pace with every new tool, and none have to. The goal is for industry to help educators see where manufacturing is heading while we guide not only the skills and attributes needed for these shifts, but the adaptive mindsets that let people pick up the next tool on their own.
Questions about skills are actually questions about competitiveness. A machine costs about the same wherever it lands in the world. The difference between plants is the workforce: people who can implement the technology, maintain it, improve it, and use the data it produces, in a lean-running operation. A minimum viable factory today includes automation and automated visual inspection, along with AI-driven predictive maintenance. Without the people to run those systems, the capability never shows up.
China's build-out started about a decade ago with deep investment in automation. Currently, the U.S. is roughly where China was in 2016. Other regions stay more competitive and take work that could've been built domestically unless we close the skills gap. As automation takes over low-value tasks, it opens up labor for higher-value ones. Manufacturers must reinvest that capacity in higher-caliber manufacturing talent that's actually assembling a product.
The U.S. is making a real bet on manufacturing. We're seeing investments in new plants and equipment, in addition to new technology. That bet pays off only when there's enough skilled labor to use and maintain what gets built — not to mention improve it for further efficiencies. Developing that workforce may turn out to be as hard, and as important, as building the factories themselves. The technology will keep advancing on its own. Producing an advantage has a different X factor: people who know how to put tech to work.
