Companies that adopt industrial artificial intelligence see productivity losses before longer-term gains, according to new research.

Organizations have long viewed artificial intelligence as a way to achieve productivity gains. But recent research about AI adoption at U.S. manufacturing firms reveals a more nuanced reality: AI introduction frequently leads to a measurable but temporary decline in performance, followed by stronger growth output, revenue, and employment.

This phenomenon, which follows a “J-curve” trajectory, helps explain why the economic effect of AI has been underwhelming at times, despite its transformative potential.

“AI isn’t plug-and-play,” says University of Toronto professor Kristina McElheran, a digital fellow at the MIT Initiative on the Digital Economy and one of the lead authors of a new paper, “The Rise of Industrial AI in America: Microfoundations of the Productivity J-Curve(s).” “It requires systemic change, and that process introduces friction, particularly for established firms.”

Co-authors of the report were: University of Colorado-Boulder professor Mu-Jeung Yang; Zachary Kroff, formerly with the U.S. Census Bureau and currently an analytics specialist at Analysis Group; and Stanford University professor Erik Brynjolfsson.

Working with data from two U.S. Census Bureau surveys covering tens of thousands of manufacturing companies in 2017 and 2021, the researchers found that the AI adoption J-curve varied among businesses that had adopted AI technologies with industrial applications. Short-term losses were greater in older, more established companies. Evidence on young firms showed that losses can be mitigated by certain business strategies. Despite early losses, early AI adopters showed stronger growth over time. 

Here’s a look at what the study indicates about the adopting and applying AI, and the types of firms that outperform others in using new technology. 

AI adoption initially reduces productivity

The study shows that AI adoption tends to hinder productivity in the short term, with firms experiencing a measurable decline in productivity after they begin using AI technologies.  

Even after controlling for size, age, capital stock, IT infrastructure, and other factors, the researchers found that organizations that adopted AI for business functions saw a drop in productivity of 1.33 percentage points. When correcting for selection bias—organizations that expect higher returns are more likely to be early AI adopters—the short-run negative effect was significantly larger, at around 60 percentage points, the researchers write.

This decline isn’t only a matter of growing pains; it points to a deeper misalignment between new digital tools and legacy operational processes, the researchers found. AI systems used for predictive maintenance, quality control, or demand forecasting often also require investments in data infrastructure, staff training, and workflow redesign. Without those complementary pieces in place, even the most advanced technologies can underdeliver or create new bottlenecks.

“Once firms work through the adjustment costs, they tend to experience stronger growth,” says McElheran. “But that initial dip—the downward slope of the J-curve—is very real.”

Short-term losses precede long-term gains.

Despite companies’ early losses, the study found a clear pattern of recovery and eventual improvement. Over a longer period of time—there was a four-year gap in the study data—manufacturing firms that adopted AI tended to outperform their nonadopting peers in both productivity and market share. This recovery followed an initial period of adjustment during which companies fine-tuned processes, scaled digital tools, and capitalized on the data generated by AI systems. 

That upswing wasn’t distributed evenly, though. Firms seeing the strongest gains tended to be those that were already digitally mature before adopting AI. 

“Firms that have already done the digital transformation or were digital from the get-go have a much easier ride because past data can be a good predictor of future outcomes,” says McElheran. Size helps, too. “Once you solve those adjustment costs, if you can scale the benefits across more output, more markets, and more customers, you’re going to get on the upswing of the J-curve a lot faster,” she says.

Better integration of the technology and strategic reallocation of resources is important to this recovery as firms gradually shift toward more AI-compatible operations, often investing in automation technologies like industrial robots, the researchers found.

Older firms see greater short-term losses

Short-term losses aren’t felt equally across all firms, the study found. The negative impact of AI adoption was most pronounced among established firms. Such organizations typically have long-standing routines, layered hierarchies, and legacy systems that can be difficult to unwind. 

These firms often have trouble adapting, partly due to institutional inertia and the complexity of their operations. “We find that older firms, in particular, struggle to maintain vital production management practices such as monitoring key performance indicators and production targets,” the researchers write.

“Old firms actually saw declines in the use of structured management practices after adopting AI,” says McElheran. “And that alone accounted for nearly one-third of their productivity losses.” 

In contrast, younger, more flexible companies appear better equipped to integrate AI technologies quickly and with less disruption. They may also have less to unlearn, making the transition to AI-enabled workflows more seamless.

“Taken together, our findings highlight AI’s dual role as a transformative technology and catalyst for short-run organizational disruption, echoing patterns familiar to scholars of technological change,” the researchers write. They note that the results also show the importance of complementary practices and strategies that mitigate adjustment causes and boost long-term returns to “flatten the J-curve dip and realize AI’s longer-term productivity at scale.” 

Published July 9, 2025, by MIT.