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.

“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.”