Adopt a human-centric approach to automation while keeping cultural and local workforce realities in mind.
The race is on to secure America’s place as the world leader in advanced manufacturing. Yet in the battle for first place, U.S. manufacturers are running before they can walk when it comes to technology. They are at risk of over-prioritizing technology without ensuring the right foundations are in place, and there are ongoing challenges to tackle.
First, the disconnect between innovation and production is exacerbating the problem of inadequate workforce development, where training and skills development initiatives are not aligned with industry needs.
Additionally, U.S. manufacturers aren’t just struggling with a talent shortage; they’re also having problems retaining that much-needed talent to embed and finesse the necessary practices for scalable automation in advanced manufacturing.
Finally, SME manufacturers are still playing catch-up in the automation race. Without technologically empowered SMEs, the wider U.S. manufacturing industry will fail to progress at the same rate as Asian manufacturers.
That being said, there are lessons to be learned from Asian manufacturers in adopting a human-centric approach to automation. However, it’s important to adopt these learnings while keeping cultural and local workforce realities in mind.
Lessons from Asia
Many Asian manufacturers are familiar with the fact that know-how is just as important as the technology itself. The workforce has spent decades persistently refining skills alongside technology adoption.
Countries like Japan are renowned for their extremely loyal workforce and ongoing strong belief in the benefits of lifetime employment. This is why approaches like Kaizen have proven to be effective in Asian manufacturing organizations. The cultural outlook on lifetime employment within an organization has created the foundations for deep skills development and close knowledge transfer between mentors and trainees. There’s a significant emphasis on talent retention and technology integration that is carefully centralized around the human role.
Arguably, because of this, Asian manufacturers are keenly aware that automation isn’t necessarily always the answer. It’s important to know what to automate and where it makes sense to do so.
These systems have helped Asian manufacturers map out where to upskill and have been a key driver in many of them leading the way for innovation and automation in the manufacturing industry. In fact, a 2023 report from financial institution Citigroup estimates Asia tends to adopt technology eight to 12 years faster than their Western counterparts.
Why this doesn’t directly translate to the U.S.
The U.S. workforce behaves very differently. Turnover tends to be much higher, especially as younger workers who make up most of the workforce typically stay in a job for a handful of years. Additionally, career growth is more valued among U.S. workers than loyalty, especially given that long-term loyalty often goes unrecognized—unlike in Japan—making it a prime motivator to change companies.
The disconnect between training and real-world industry needs is slowing technology adoption. Many companies don’t have the skilled talent to effectively implement automation at scale, dealing with poor data use and inconsistent processes. For example, installing computer vision sensors without trained staff to interpret the data often results in wasted investment and limited impact.
This makes it more difficult for organizations to devote the time and energy to in-depth knowledge sharing and skills development alongside technology adoption.
Propelling training and growth
It’s vital for manufacturers to build a bank of easily accessible knowledge that can be captured and transferred seamlessly. Technology has a powerful role to play in facilitating knowledge transfer and accelerated innovation via on-the-job training.
On-the-fly manuals based on real-time data capture via IoT tools can facilitate direct and smooth sharing of instructions while making sure workers are sticking to a clear procedure. Built-in sensors and other embodied AI features help monitor actions and can be useful in alerting teams to any risks or errors.
Agentic AI has a powerful role to play in multiple areas, whether it’s ensuring the continued transfer of valuable knowledge from prior experience or answering on-the-job questions that can guide less experienced employees. Voice communication with real-time video feed has a dual benefit of increasing both operator and supervisor productivity.
These data-powered solutions also mean that U.S. manufacturers can build knowledge banks that remain within their organizations after people leave. It slashes the burden on already limited human resources while still facilitating in-depth, hands-on training. Organizations can also use AI-powered platforms to design training plans that not only set clear paths of growth for employees but also ensure they align with specific business and wider industry needs.
These training methods should also promote accountability when using technology. Workers can be familiarized with data handling and hygiene and know what to look out for when data isn’t being processed properly. Easily accessible manuals also ensure teams are sticking to security protocols and understand key steps when a breach happens.
Innovating traditional methods with technology
While emerging tools like agentic AI are becoming increasingly more autonomous, the human touch can’t be completely eliminated from the picture, especially for embedding best practices around technology and scaling these. The ultimate responsibility for actioning decisions and strategies sits on people’s shoulders, not AI tools.
Yet that’s not to say that technology doesn’t have a role in empowering manufacturing teams to make better, more informed decisions—and act on these more effectively. For instance, integrating embodied AI like robots and live sensors can help streamline data capture, a fundamental component of informed decision-making and strategizing. These embodied AI tools can be used alongside powerful algorithms and ML solutions to produce accessible insights from a huge swathe of data, which speeds up the decision-making process.
AI can also boost safety on the factory floor. Robots and computer vision can monitor spaces and alert teams to risks or incidents when they occur, while reminding them of safety protocols. They can also notify staff when a failure occurs or there’s an issue with a product. This can be extremely cost- and time-effective for manufacturers. Tools like these, working alongside the human eye, can reduce the number of defective products.
For the U.S. to rival Asian manufacturers in the automation race, it needs to embrace the human role in manufacturing and facilitate faster knowledge-sharing by reinvigorating training and reskilling with technology. Ultimately, it’s not about replacing people across the board with automation, but achieving synergy between the two for accelerated yet responsible innovation at scale.
Shin Nakamura, is a Japanese manufacturing leader, President of one to ONE Holdings and President of Daiwa Steel Tube Industries, which has facilities in Japan, Vietnam, India and the U.S.
