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While AI and automation seem to be the biggest trends in the industry, Intrinsic Chief Technology Officer Brian Gerkey recently shared a striking statistic: 80% of U.S. manufacturing facilities have zero automation.
In spite of discussion about the potential benefits of this technology, it’s still far from widespread in the United States, let alone at the level of fully automated factories as seen in countries like China and Japan.
“There is no doubt interest is high across the board, but execution is where things get difficult,” said Jeff Burnstein, president of the Association for Advancing Automation. The group’s research also shows that while a strong majority of manufacturers believe AI will be critical to their future, only a small percentage say it’s widely deployed today.
Deloitte’s 2025 Smart Manufacturing and Operations Survey showed similar results. An estimated 92% of manufacturers surveyed said they believed smart manufacturing will be the main driver for competitiveness over the next three years.
Yet only about 29% of manufacturers reported already using AI or machine learning at the facility or network level, and only 24% had deployed generative AI. Looking ahead over the next two years, 41% of respondents said they planned to prioritize factory automation investments.
The bottlenecks holding back AI adoption
Manufacturers are still building the foundational capabilities required to scale AI and automation, said Tim Gaus, a principal and smart manufacturing leader at Deloitte.
Despite nearly three in four companies planning to deploy agentic AI within two years, only one in five reported having an equipped model, according to the company’s State of AI in the Enterprise report.
“Many organizations are still working with fragmented legacy systems and data that is not structured for AI use,” said Jasmeet Singh, executive vice president and global head of manufacturing at Infosys.
According to Singh, it often comes down to digital maturity. Manufacturers that have already modernized their core systems, invested in cloud and built strong data foundations are moving faster on AI. Singh said such companies are better positioned to scale beyond pilots because their data is ready to support advanced use cases.
Singh added that a key shift where many manufacturers struggle is the transition from pilot projects to measurable business outcomes, because they want a clear return on investment before committing significant funds.
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“In many cases, earlier proof-of-concept efforts did not translate into enterprise-wide impact, which has slowed broader adoption,” he said.
The disappointment often comes from how AI was implemented rather than the technology itself, something Singh called using “AI for AI’s sake.”
But even these “failed” projects can be valuable, according to Burnstein, because they give “a much clearer understanding of where the real constraints are, which leads to more successful implementations the next time.”
Automation considerations
Most automation today is built for highly standardized, repeatable processes, said Stefan Nusser, chief product and commercial officer at Intrinsic, Google’s robotics software company. But manufacturing often involves variability, customization and evolving processes, which can make automation expensive to test and implement.
Even when they’re established, Nusser said the processes may run for only a short period despite the significant bandwidth required to set them up. This may be because automation is just used for a specific task but not scaled across the whole factory. Companies may also realize the technology is not feasible or profitable for their current business model, or their manufacturing needs change over time and require adaptation to newer processes.
Major bottlenecks are commonly seen with mid-sized manufacturers that have the resources to experiment with AI and automation solutions but tend to wait until the value is clear before making large-scale investments. On the other hand, larger organizations have more resources to pursue aggressive growth, but their scale and legacy systems can slow things down, Singh said.
That’s why Nusser advocates for incremental adoption. He cited the example of autonomous mobile robots that are flexible and less risky to implement, compared to a legacy, conveyor-based solution that requires overhauling the entire structure and workflow. The latter may seem nearly impossible, according to experts, considering that shutting down the system for even a few days could cause losses and supply chain disruptions in the millions.
History repeats itself
This is not the first time the manufacturing industry has experienced a complicated technology transition. It also happened with the adoption of simpler robots.
“The technology faced resistance because of initial high costs and hesitancy around change management, but eventually, sectors like automotive manufacturing saw huge benefits after prioritizing the foundations needed to make it effective,” Gaus said.
AI and advanced automation are following a similar trajectory. Early adopters are demonstrating clear productivity gains, he said, with some companies reporting 10% to 20% improvements in production output and employee productivity.
“As these results become more visible, we can expect adoption to accelerate across the industry.”
According to a recent PwC report, manufacturers could triple their current automation levels by 2030. Some use cases showing the strongest potential for adoption of automation in the next few years include data capture and analysis, demand forecasting and quality control.
A recent report from the International Federation of Robotics shows that trend has already been building in recent years. An estimated 542,000 industrial robots were installed across the world in 2024, more than double the number 10 years prior. U.S. installations reached a peak of 44,000 units in 2023.
Their findings echo Gaus’ predictions: “One thing we consistently see is that once manufacturers observe successful use cases — especially from their peers — adoption tends to accelerate very quickly.”
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