By Opus Connect
At a recent manufacturing roundtable, private equity professionals, advisors, and dealmakers gathered to discuss the role of AI in the sector. While many acknowledged its transformative potential, small and mid-sized manufacturers were described as cautious—often reluctant to take big bets. But why?
Uncertainty in a Shifting Tech Landscape
One reason for hesitation is the fast-changing technology environment. One of the panelists of the Opus Connect roundtable, Peter Hammond of The Courtney Group, nicely summarized the concern. “As you look at manufacturing companies and the information systems that they have, it’s hard to figure out where the ERP environment is going to be three years from now.” ERP, short for Enterprise Resource Planning, refers to the integrated software systems that manage core business functions such as finance, supply chain, production, and human resources.
This uncertainty carries costs. For firms running on tight margins, they want to avoid the risk of choosing the wrong system, like software that could quickly get outdated and this, observers say, has pushed many companies into short-term thinking. “Making decisions about that has gotten more complicated,” said Hammond, “and so we’re more willing to wait and see and limp along a little more because you could be making a big investment in something that becomes irrelevant.”
Last year, Reuters reported the findings of a recent Lucidworks report, including one suggesting that 58% of manufacturing leaders plan to increase AI spending, but adoption is slower compared with sectors like finance or retail. In fact, nearly 44% of manufacturers cite accuracy concerns, such as AI “hallucinations,” as a barrier to rollout, according to the study.
Limited Opportunities in Automation
Despite caution around large-scale systems, some manufacturers are experimenting with smaller, paper-based efficiencies. Alex Welles of Union Capital Associates saw potential in the mundane but time-consuming back office. “You see the potential for opportunity there,” he said, “especially with all the sort of paper processing that goes around manufacturing, and are there ways to automate some of that? It’s certainly sort of early innings for us, about how we think about that.”
That modest approach mirrors wider small business trends. Last month, the U.S. Chamber of Commerce published a study revealing almost 60% of small businesses now use AI—double the rate from 2023. But the report also showed that much of this adoption is focused on back-office and customer-facing tasks rather than core production.
Financial functions remain a sticking point. Welles explained why his firm has yet to use AI for accounting. “I think it’s still the case that at least internally, the concern about a mistake [is] probably outweighing the desire to have the efficiency, but it’s certainly something we’re thinking about,” he said.
This cautious approach aligns with broader surveys—about 42% of small businesses say they lack the resources or expertise to deploy AI effectively, with most pointing to gaps in skills and knowledge.
Data Access Is Easier, But People Still Make the Calls
Even as AI tools make information retrieval faster, participants stressed that core decisions remain human-led. Hammond pointed to some real improvements. “Information that used to take hours and hours to pull together is available in seconds,” he said. “And that’s really helpful.” But he quickly added that evaluation is still “very much a people-based evaluation process.”
This reliance on judgment and trust underscores why smaller manufacturers are more hesitant than tech-forward industries. As Hammond put it, the stakes are simply higher: “If you’re wrong, you can really set a company back.”
A “Wait and See” Approach
Moderator Swayze Yancey captured the group’s mood with a simple question: “Are there any ways that you guys are seeing AI being used in small manufacturing companies? Anything revolutionary happening, or is it just kind of a wait and see still?”
The consensus was clear: it’s wait and see. AI may be good for paperwork, summaries, or accelerating due diligence, but for now, human experience and caution outweigh the lure of efficiency.
That helps explain why smaller manufacturers are holding back even as others rush ahead. A Salesforce survey found that 91% of small businesses already using AI report revenue boosts, and 71% plan to increase AI investments in the next year. But manufacturing lags behind: at the roundtable, no one could point to a truly revolutionary use case.
