The Rise of AI in Manufacturing

Breakthroughs in how robots can understand the real world, reason and plan actions are fueling the transition from research and development to commercial deployment across sectors, including manufacturing. 

Earlier in 2026 at CES in Las Vegas, Nvidia CEO and co-founder Jensen Huang said the “ChatGPT moment for physical AI is here,” marking an inflection point in the robotics space. Hyundai Motor Group also debuted its Atlas humanoid robot for production settings, with plans to gradually deploy them across its operations in the next two years.

In addition to collaborative applications and robotic arms, manufacturers are also increasingly using AI agents and sensor technologies to enhance their operations. However, as their systems become more digital, they also must take cybersecurity measures to prevent potential attacks.

Here are some of the AI and robotics trends to watch in 2026.

The physical AI hype is real, but the rollout will take some time

A number of manufacturers are already using physical AI, such as robotic arms and cobots, to fill labor shortages and handle repetitive tasks. But as carmakers like Audi and BMW pilot humanoids within their operations and ABB Group sells its robotics division to Softbank, the movement is set to go from niche to mainstream in 2026.

According to a recent Deloitte survey of more than 3,200 global business leaders, about 58% of participants indicated they were currently using physical AI to some extent in their operations for smart monitoring or production alongside humans. That number grew to 80% when asked about their plans in the coming years, with 15% using physical AI extensively and 3% fully integrating it.

“Everyone's getting really excited about it,” said Andy Lonsberry, CEO and co-founder of Path Robotics, a maker of autonomous welding systems. “Everybody wants to start prepping their facilities for this wave. And I think the adoption rate will be very, very fast, but I do think it's gonna be a bit of a slower rollout of making those capabilities go from demo to fully functional.”

Risks that result in unplanned downtime, which can cost companies millions of dollars in lost production, could outweigh the technology’s benefits and affect deployment in the near term. Path Robotics’ intelligent welding arms are designed for applications in the defense, utility, data center and heavy-equipment industries.

“Having a demo that works 70% of the time isn’t really going to cut it for manufacturing,” Lonsberry said. “It’s got to be [effective] like 99-plus percent of the time.”

One of the biggest challenges that robotics makers look to overcome is developing human-like dexterity and pressure control. With the convergence of vision, sensing, cobots and other robots alongside AI developments, Omdia analysts said in a recent outlook to expect more case studies of humans and more mobile robots working together in more flexible environments.

Cost-effective tools like AI agents and sensor technologies will surge

As manufacturers lay the groundwork for their digital transformations, they are turning more to AI agents and sensor technologies, also known as the Internet of Things, to autonomously monitor equipment, anticipate maintenance needs and manage supply chains.

Ed Nabrotzky, CEO of Dot Ai, a provider of tracking sensors and AI software, said the trend is accelerating, in part because the equipment is relatively inexpensive and the technology capabilities have improved in recent years.

Dot Ai’s battery-free labels connect to the internet and allow retailers, logistics companies and manufacturers to digitally track whatever they are attached to, including packages, equipment and other assets. The labels are based on technology developed by Wiliot and can measure things like temperature and light, allowing the viewer to know if a box has been opened, Nabrotzky said. They are about the size of index cards.

“You have a lot of assets trying to achieve an objective, and it used to be you could just ‘black box’ it, and so long as they got the job done, it was okay,” Nabrotzky said. “But we increasingly need to have full transparency of the process to know what’s happening.”

According to a Deloitte survey of 600 manufacturing executives, about 46% of respondents said they are using IoT solutions for enhanced visibility as they prepare their operations for increased automation. In November, Rockwell Automation announced plans to build its largest factory in Wisconsin. The facility will be equipped with advanced automation, robotics and digital systems, allowing the company to showcase its products to customers on-site.

AI agents, software systems such as large language models, are also becoming more ubiquitous across industries. A separate survey from Deloitte found that nearly 3 in 4 companies plan to deploy agentic AI within two years. IoT is playing a key role in accelerating the transition. Nabrotzky said after DotAi provides the labels, it provides supervised learning services to help customers implement agentic AI.

“Humans really can’t keep up very effectively,” he said. “And it’s very clear that algorithmic decision-making is going to bring tremendous efficiency to that market space and tremendous transparency for customers.”

More data means more cybersecurity risk

Not long ago, manufacturers were more immune to cyber threats due to the analog nature of their operations. As companies adopt more advanced technologies and work to understand the huge volumes of data they’re collecting, that is not the case anymore.

Manufacturing has been the most targeted industry for the last four years, according to IBM’s X-Force 2025 Threat Intelligence Index, with a high amount of ransomware attacks such as extortion and data theft. Many of the attacks are coming from hackers exploiting unprotected, outdated systems.

To counter these advanced threats, companies will have to adopt AI tools to enhance their cybersecurity measures. However, as companies navigate this integration they will need to strike a balance between automation and human judgment, according to the World Economic Forum’s 2026 Global Cybersecurity Outlook. While AI is good at repetitive, high-volume tasks, overreliance could create blind spots for hackers to exploit.

In August, Jaguar Land Rover suffered a cyberattack that halted production across its global operations for five weeks, resulting in $260 million in cyber-related costs and a 24% decline in revenue. In October, Japan-based beer maker Asahi also suspended operations following a ransomware attack that forced the company back to pen and paper.

About 59% of respondents from the manufacturing, supply chain and transportation sectors say they are adopting AI to augment cybersecurity capabilities, according to the World Economic Forum’s recent survey of 837 executives, academics and experts.

The number of participants assessing the security of AI tools has nearly doubled to 64% from the previous year, according to the survey. At the same time, 87% of them identified AI-related vulnerabilities as the fastest-growing cyber risk.

Looking ahead, Omdia analysts said 2026 marks a turning point for manufacturers, defined by integration, intelligence and industrial resilience. Cybersecurity and compliance readiness will become mandatory for global and regional market access, they added.

In August, a cyberattack forced automobile manufacturing giant Jaguar Land Rover to shut down production for a month. The company reported around $260 million in cybersecurity costs in addition to $650 million in losses owing to the production disruption, U.S. tariffs on imported cars and the phasing out of older models. 

The unexpected halt of the manufacturer’s highly automated production lines in the UK, which typically work on about 1,000 vehicles a day, also disrupted a broader global supply chain. Unions and officials estimated thousands of workers could be laid off, and smaller suppliers could go bankrupt due to the sudden loss of business. 

Incidents like these are growing as manufacturers increasingly digitize their processes. Experts are raising concerns that cybersecurity measures have become an afterthought as more companies rapidly adopt artificial intelligence and cloud systems for efficiency, profitability and reduced reliance on human labor.

According to the 2025 Deloitte Smart Manufacturing Survey, 57% of the 600 executives surveyed at large U.S. manufacturing companies reported using cloud systems. An estimated 29% said they are using AI and machine learning at the facility or network level. 

In 2024, North America was the largest market for cloud-based manufacturing infrastructure, accounting for almost 50% of the global share, according to a report by Market Research Future.

While this rapid growth seems promising from a production perspective, experts warn that the pace of tech adoption has far surpassed the cybersecurity measures required to keep these systems running safely. 

Are AI and cloud systems making manufacturers vulnerable?

The IBM X-Force Threat Intelligence Report 2025 states that manufacturing has been the most-attacked industry by cybercriminals for four years straight. 

“The biggest cybersecurity risk in manufacturing right now comes from the amount of connectivity being introduced into environments that were never designed for it,” said Nick Nolen, vice president of cybersecurity  strategy and operations at Redpoint Cyber, which works with manufacturers. “The real challenge is the mismatch where manufacturing is modernizing quickly, but the underlying systems and processes weren’t originally built with cybersecurity in mind. That is what creates the openings attackers look for.”

Manufacturers used to fly under the radar when it came to cyberattacks, mostly because their systems weren’t online, said Todd Moore, VP of encryption at Thales. Until recently, he added, systems were designed for performance with no cybersecurity measures as digitizing was not even a consideration.

Now, as companies integrate advanced technology like AI and cloud systems with old infrastructure that isn’t equipped for digital operations, Moore said this has become an issue.

“Security is often bolted on to these systems rather than built with secure-by-design principles, making manufacturers vulnerable to everything from ransomware and malware to phishing and even denial-of-service attacks,” he said.

Adoption of AI and cloud systems amplified these risks by significantly expanding the attack surface area, according to multiple experts. Nolen said manufacturers have a much broader attack surface than most people realize.

“Modern manufacturing relies heavily on third-party integrators, connected machines, vendor-supplied software, and data exchange between business units,” he said. Each of these touchpoints introduces another opportunity for compromise. 

“Once an attacker gains entry into even a small corner of the environment, the interconnected nature of manufacturing systems can allow them to move quickly toward more sensitive areas,” Nolen said. “Attackers are going after whatever piece of the digital environment is easiest to get into, and as more manufacturing data moves into the cloud for AI and automation, those systems become a bigger and more attractive target.”

The biggest risk here, according to Kevin Albano, global head of X-Force Threat Intelligence at IBM, is the possibility of unauthorized access to the sensitive data manufacturers upload to AI and cloud systems.

“To mitigate this, manufacturers need to treat AI datasets as high-value assets,” he said. “That means classifying and protecting sensitive data across cloud, on-premises and hybrid environments, encrypting all personally identifiable information at rest and in transit and implementing strong key management.”

Cybersecurity measures to prevent attacks

Heavily monitoring AI usage can be difficult to manage as many manufacturers don’t fully understand the components their vendors are using behind the scenes, which creates security blind spots, said Ferhat Dikbiyik, chief research and intelligence officer at Black Kite. 

The company’s 2025 Manufacturing Report echoed IBM’s findings, showing manufacturing being the biggest cyberattack target for four years in a row. 

“Manufacturers need visibility into which vendors have access to production data, where AI is being used and how systems could connect back into [operational technology],” Dikbiyik said. This becomes complicated when the people involved in production and supply chain use AI and cloud tools informally, without security teams knowing what’s being uploaded or which models vendors are relying on, he said.

“Once you upload any sort of design or process information into an AI tool, you need to be confident about where that data is going, who can see it, and how it might be used,” Nolen said. “Do you know what your vendor is doing with that data? Do you know where it is stored? How long is it retained? Is it being used to train their own models? Many companies still don’t have clear guidelines on what is safe to upload and what should stay local.”

That’s why encryption is essential, according to Moore.

“Manufacturing organizations can begin with thoroughly classifying data and assessing risk to determine where vulnerabilities lie within hybrid or cloud environments,” he said.

The process, known as data classification, includes labeling information based on its sensitivity so that teams know what needs the highest level of protection.

Another concern is cloud systems being hacked because they “centralize sensitive design files, recipes, production parameters and supplier information, which means a single compromised cloud account can ripple across multiple plants,” Dikbiyik said. That’s why, he said, “companies need proper segmentation between IT, cloud, and operational systems so that a breach in a digital tool can’t cascade into production.”

Significant upfront costs required to set up and maintain security systems are deterring many manufacturers from implementing such measures. However, according to Nolen, "more spending doesn’t equal more security." 

Nolen helps manufacturers calculate the probable financial loss in the event of an attack, which helps them make informed decisions about how much they might need to spend, either for prevention or in the aftermath of an attack.

As part of this process manufacturers have to weigh the cost of investing in cybersecurity programs that must evolve with technological advancement versus the risk of an attack exacerbated by increased digitization. 

“I am never in favor of slowing innovation for the sake of cybersecurity,” Dikbiyik said, “but I always believe that we can make sure the digital factory of the future is built on a security model that matches its complexity.”

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By Katana

AI used to feel like something you’d come back to later, once you had more people and more time. For a growing product business, it was easy to file it under “interesting,” then return to the work that decides whether orders ship on time.

In 2026, that distance is shrinking. A useful pulse check comes from Rootstock’s 2026, State of Manufacturing Technology Survey. Predictive AI adoption was reported at 48%, up 12 points year over year. The same release notes AI investment moving toward supply chain planning, reaching 35%. It’s a signal that AI is being pushed toward day-to-day operations.

The data is one thing. The more interesting part is where it’s showing up in real companies. Some product businesses are already using AI to take pressure off small ops teams and keep the work moving when volume grows.

Business Insider recently described how Spot & Tango, a fresh dog food delivery business, started using an agentic AI tool in its operations.

When the company opened its facility in late 2022, procurement and scheduling depended on manual coordination. The team piloted an agentic AI tool that logs and confirms purchase orders, then builds production schedules based on ingredient availability. The logistics team reviews the decisions. After three months, they expanded its use.

Without a custom AI build or a long IT project, Spot & Tango says about 60% of purchase orders are now fully automated. Their small ops team tested the product in a short window, rolled it out and used the time savings to scale output without hiring at the same pace.

And time savings are only part of what AI is starting to change for small and medium product businesses. SMB-dedicated tools, like Katana Inventory Software, are actively investing in making AI part of the workflow itself.

That means, for example, AI-driven demand forecasting and supply planning.

Businesses using Katana can use AI to pull from live inventory and order data to spot signals that used to surface in weekly reviews, after the window to respond had closed. Things like a product trending faster than forecast, a supplier delay that will tighten stock in two weeks or slow-moving items eating working capital.

Built into the daily workflow, AI can flag risks while there's still time to adjust purchasing or shift production schedules. It reads operational data teams already track and surface what needs attention, when it needs attention. Demand forecasting happens continuously instead of monthly. You spot red flags like stockout risks and supplier delays earlier and make decisions before those issues compound.

For product businesses that managed stock in spreadsheets until recently – or still do and are considering a step-up -, that opens the door to scaling output and complexity without taking on a matching cost or operational strain.

Five years ago, this level of planning capability required enterprise budgets and dedicated data teams. The AI that used to separate enterprise manufacturers from everyone else is now available in tools SMBs already rely on, or that are easily integrated with their tech stack. AI has democratized tech for product businesses: the space between what a growing brand can do and what only large players can afford is narrowing.

Artificial intelligence-powered vehicle design is revolutionizing the automotive industry, helping legacy brand designers match the speed of China-based carmakers in bringing vehicles to market.

That’s the claim being made by Switzerland-based Neural Concept for its AI engineering co-pilot platform that also suggests it can achieve up 30% shorter design cycles and $20 million in savings on 100,000-unit vehicle programs.

At the company’s presentation to CES Las Vegas 2025, it claimed its platform can also cut end-to-end product development times by up to 75%. It also claims to accelerate simulations by up to 10-times and boost auto characteristics, such as efficiency, safety, acoustics and aerodynamics, by up to 30%.

WardsAuto caught up with the company’s co-founder and CEO, Pierre Baqué, to find out more about the technology.

In an interview, Baque told us that the platform can be used in a multitude of ways to save costs, speed up time to market or to focus on bringing the latest technological innovations into exclusive products.

General Motors, Stellantis, Aston Martin and Subaru are among the company’s many clients, in addition to suppliers Mahle and Bosch. In September, Neural Concept announced  announced a significant expansion of auto customers and adoption of its platform including Renault, Leonardo and SPAL Automotive.

Its NC Platform operates by placing a AI layer of design on the existing digital computer-aided engineering (CAD) systems, said Baqué.

“This is helping them [design engineers] design the car, render the assimilations and helping them make decisions on top of the digital layer,” he explained.

Today the automotive industry is being challenged to accelerate the vehicle production development timeline and the NC Platform achieves this by cutting back on repetitive design processes, he said.

For some clients this is a key attraction of the company’s platform. “Some companies are really obsessed with time to market with the idea of competing with China with the ability to put a car on the road in less than two years,” he said.

However, others see efficiency as a priority — being “able to do more with the same amount of people,” Baqué said. “We do this by providing help at every stage of the development process, and particularly at the early stages of the development process.”

On the other hand, some of Neural Concept’s premium brand companies target innovation above cost savings “bringing better products to market with the same type of engineering involved to continue to be on top of their game in terms of the performance of their products,” said Baqué.

Looking to the future, Baqué believes the NC Platform will be able to reimagine the traditional design concepts of the vehicle to respond to the emergence of electrification and autonomous driving.

“I think the companies equipped with our solution will be the ones that will be able to redirect much more quickly through the concepts and, therefore, converge on concepts that make most sense to the consumers as these concepts evolve in the coming years,” he said.

With the prospect of autonomous driving, for example, he suggested that a potential need to make the interior much bigger than the traditional vehicle could spark a whole litany of issues with the rest of the vehicle.

“The companies that can very quickly test engineering concepts, such as ‘If I make the cockpit three times bigger, how will that affect things in terms of aerodynamics?’” 

Panelists at Nvidia GTC on Oct. 29 discussed how the growth of data centers and internet-connected technologies is creating various opportunities for the manufacturing and construction industries.

Between 2010 and 2018, global data center workloads and virtualized computing resources increased sixfold as organizations moved away from smaller data halls toward larger, cloud-efficient data centers, according to a 2020 report in the journal Science.

Furthermore, a 2023 Deloitte report noted that the average U.S. household has 13 device types and 21 connected devices. Items such as thermostats, smart watches, blinds and dishwashers are now connected devices, requiring data storage to house the information the smart devices generate, said Josh Levi, president of the trade group, the Data Center Coalition.

Moreover, the use of artificial intelligence and machine learning has increased data centers’ workload, with some facilities dedicated solely to those systems, which Levi expects will continue to grow and make an economic impact.

Manufacturers have been integrating AI in their operations, including product design, the shop floor and supply chain management, according to a report released by the National Association of Manufacturers, Manufacturing’s Roadmap to AI and Energy Dominance.

For example, prominent companies Belden, Lucid Motors, Taiwan Semiconductor Manufacturing Co., Caterpillar and Foxconn Industrial Internet are using Nvidia’s Omniverse technologies to accelerate AI-powered manufacturing. 

Additionally, Foxconn and Fanuc use technology from the Omniverse libraries to connect to their robotics. Furthermore, Foxconn is using the Omniverse technologies to design, simulate and upgrade its 242,287-square-foot facility in Houston to manufacture Nvidia’s AI infrastructure systems.

At present, approximately 51% of manufacturers use AI, 60% plan to install AI in their operations by 2027, and about 80% said the technology will be vital to grow or maintain their businesses by 2030, according to NAM’s survey results.

“I think a lot of folks don’t think of AI as manufacturing or manufacturing processes,” said Jay Timmons, NAM’s CEO and president. “The truth of the matter is, every form of technology through decades has begun with manufacturers, and we drive it forward.” 

Manufacturing components and retrofit opportunities

Direct employment in the U.S. data center market grew by over 50% from 2017 to 2023, according to the Data Center Coalition’s economic contribution report with consulting firm PwC, released in February. Additionally, the data center industry’s total annual employment contribution increased by 60% over the same period, from 2.9 million jobs to 4.7 million jobs.

Levi added that people building the data centers and manufacturing and installing the equipment inside the facilities represent additional economic benefits.

The electrical equipment manufacturing segment has invested heavily in the data center market, spending approximately $185 billion on upgrading and expanding operations in the U.S. since 2018, according to an infographic from the National Electrical Manufacturers Association.

The electrical industry is also one of the largest markets in the country, valued at $375 billion.

NEMA company members are also trying to manufacture more data center components, such as wiring, cable, motors and transformers, as well as establish supply chains in the U.S., said CEO and President Debra Phillips.

“These are national security assets,” Phillips said. “You think about transformers, you think about critical minerals, these materials will go into the electrical products that are part of the data center space.” 

Don Slaiman, political coordinator for Local 26 of the International Brotherhood of Electrical Workers, said the union has found that nearly 25% of its construction in the data center market involves retrofitting, as its members update and upgrade electrical infrastructure.

“The evolution of what is placed in these data centers changes so quickly,” Slaiman said. “So we got manufacturing, we got maintenance, we got construction, and we got retrofitting. So it’s a gift that keeps on giving for workers.”

What’s at stake

As of August, there are about 409,000 manufacturing job openings, and that number is expected to reach 3.8 million by 2033, according to NAM. 

“What we’re going to see is that the jobs in manufacturing are going to require a different set of skills, and we're going to need folks that understand how to utilize this technology to make us safer, to make us more efficient, to make our operations faster,” Timmons said.

However, that could increase depending on the tax policy, regulatory modernization or the energy infrastructure, which Timmons said. 

Additionally, panelists urged lawmakers to take action to strengthen the U.S.’s AI market and energy infrastructure, as data centers and AI take up around 60% of electricity on average, according to the International Energy Agency. NAM’s report noted members’ push for permitting reform on building out the energy infrastructure and products, as well as to streamline AI, energy and grid projects.

Levi adds that stabilizing the supply chain is important and that ensuring the market has the equipment and raw materials available for manufacturers and others to build and continue innovation.

“We have the manufacturers who are collaboratively working with the end user data centers to continue to innovate and develop new manufacturing capabilities in top data center markets,” Levi said.

The U.S. manufacturing industry lost 78,000 jobs over the past year with 12,000 cuts in August alone, according to the latest federal data. This comes as automation is ramping up in factories, but it’s far from the only factor.

While the September employment data wasn’t released due to the government shutdown, the August 2025 labor turnover survey shows a 12% dip in job openings compared to 2024, with 313,000 job separations in manufacturing.

Experts say this isn’t new. Manufacturing jobs have been declining since February 2023, now reaching the lowest level since the onset of the COVID-19 pandemic.

The numbers have been going down since 1979. Now, factors such as the increasing use of AI and automation, tariff changes, tightening immigration policies, and an aging workforce are leading to further decline. 

AI and automation

Manufacturing companies have been facing a labor shortage for years. Nearly 2 million jobs — half of all new positions created — could be unfilled by the end of the decade, according to data from Deloitte and The Manufacturing Institute. 

Many companies have turned to artificial intelligence and automation to bridge the gap, with some countries like China and Japan running fully automated factories. Some plants in the U.S., like Tesla’s Gigafactories, are also implementing similar models.

Sectors with high-volume production and repetitive tasks, such as automotive, semiconductors, electronics, aerospace and pharmaceuticals, are experiencing the highest adoption of AI and automation. Experts say other divisions will follow suit as implementing this technology becomes more accessible and financially viable. 

While this has helped keep operations running as manufacturers struggle to hire and retain workers, it raises the question of what happens to the labor force replaced by machines. Is technological advancement stealing people’s jobs?

According to the latest SHRM analysis, 15% of jobs in the U.S. across all industries, about 23 million total, are at a high risk of being displaced due to automation. 

Many experts point to reskilling as a solution, saying workers’ responsibilities could change rather than be completely eliminated. 

“Factories will keep getting smarter and more automated, but people will not disappear from the equation,” said Janelle Bieler, U.S. head of tech talent solutions at Akkodis, a digital engineering consulting company that is part of The Adecco Group.

“The larger shift is that there are not just fewer jobs, but different jobs,” she said. “Traditional assembly roles are declining while demand is growing for technicians who can work with robotics, maintain advanced equipment and use data to keep production running smoothly.”

Due to this, even though automation is making some positions redundant it’s also creating new roles that didn’t exist before. 

Several programs, including the Akkodis Academy, are offering resources to support this transition. But many manufacturers aren’t in a position to make drastic changes. 

“This transition will be more gradual as not all companies can afford to invest in automation so there will still be a need for people to support manufacturing, especially for small and medium enterprises where investment capital is scarce,” said Sumeet Kaul, a managing director in FTI Consulting’s corporate finance and restructuring segment.

“Reskilling is a potential solution but we need to remember that this is not a one-to-one replacement,” said Kaul.

This means a lot of jobs will continue to be replaced by machines, said Wafa Hakim Orman, associate dean of the College of Business and associate professor of economics at the University of Alabama in Huntsville, noting that this has happened before.

“In 1870, over half the U.S. labor force worked in agriculture but today, it's less than 2%,” said Orman.

The labor force moved into manufacturing and services as agriculture mechanized. Orman said we should expect to see the same pattern continue in manufacturing, where factories are increasingly automated, and reducing the reliance on human labor.

While some workers are being reskilled to work on these automation systems, the only option for others might be transitioning to a different industry. 

Tariffs and tighter immigration policies

While AI and automation are bringing long-term changes that will change the very core of how the manufacturing industry operates, two other factors have also affected production recently.

First, the uncertainty around tariff policies has put some manufacturers in a “wait-and-see” mode, leading to stalled projects and hiring freezes, industry experts said. 

“Exporters can't forecast demand, importers are uncertain about the price and how much of the tariffs consumers and foreign suppliers will bear, and manufacturers who use imported materials, tools, or components are dealing with large swings in their costs,” Orman said. It is difficult for businesses to plan long-term investments in such a volatile environment, she added. 

Amidst the recent policy changes, some plants have shut down, paused operations, changed locations, consolidated or discontinued certain production lines, all of which contributed to layoffs. 

Second, President Donald Trump’s administration has implemented several changes to tighten immigration policies, making it harder for new foreign workers to enter and for those already here to stay. Some of these changes include a $100,000 fee for new H-1B visas, increased scrutiny of visa applicants’ social media activity, plans to end birthright citizenship, limiting access to health insurance benefits, and additional funding for detention and deportation. 

There’s also been an increase in U.S. Immigration and Customs Enforcement raids at major facilities, such as the Hyundai plant in Georgia, where nearly 500 foreign workers were detained in September. Some factories across the country have reported delayed shipments and reduced production as a result.

The administration’s stated goal is to prioritize domestic hiring. Orman said this could possibly increase hiring, but it could also boost the pace of automation as some firms replace immigrant workers with machines, especially if local workers expect higher wages. 

All of this has made 2025 an unpredictable year for manufacturers, with experts forecasting many more changes ahead.

“In the long run, new technology has typically made us more productive,” Orman said, “but in the short run, it can lead to significant disruption as people need to switch industries or occupations, and frequently retrain and learn new skills.”

Akkodis’ Bieler said this will require ongoing adaptability by all involved.

“The best safeguard for both workers and employers is steady investment in skills, so people are prepared to move into the roles that the market continues to demand,” she said. “Jobs will disappear, yes, but history shows us that new jobs always take their place.”

Cloud and AI software provider IFS said that it’s teaming up with Anthropic, a large language model developer, to scale artificial intelligence that helps frontline workers detect machinery and systems issues they might otherwise miss.

As part of the partnership, IFS is using its industrial program Nexus Black to launch Resolve, a tool that places AI directly in the hands of technicians and field workers across aerospace and defense, manufacturing, energy, utilities and other major industries.

Resolve leverages Anthropic’s Claude LLM to analyze thousands of equipment images, interpret sensor readings and recognize patterns to “catch problems before they become failures,” according to a news release.

As manufacturers look to make the switch from analog to digital operations and processes, they can be limited by the types of technology available to them. Several are ramping up AI adoption for robotics that can do repetitive tasks, but according to IFS, there is untapped demand for specialized software tailored for technicians, field workers and more.

Instead of the generic AI tools used by white-collar workers, industrial companies need tools that can solve a “range of hard problems” to prevent factory downtime, Kriti Sharma, CEO of IFS Nexus Black, said at the software provider’s inaugural Industrial X Unleashed event in New York City on Nov. 13.

Distillery William Grant & Sons, the makers of Grant’s whisky and Hendrick’s gin, has been piloting Resolve at one of its factories in Scotland to read complex plant schematics and predict potential failures before they happen. Using the tool, technicians can diagnose faults by capturing the sound of a rattling pipe, video of a part moving strangely or pressure fluctuations, according to a news release.

The distillery has since cut downtime and boosted output, with estimated cost savings of 8.4 million pounds ($11.1 million) per year at the one location, Sharma said.

IFS has also deployed Resolve at an airport hangar in the Middle East. Sharma said the technology can break down complex documents and highly specialized expertise into a clear sequence of steps for workers to understand.

“[It] gives you step-by-step repair guidance so a junior engineer can act like one with 20 years of experience, and it learns from every job you do with it,” she said.