Manufacturers’ AI spending aspirations decline sharply from 2023
4 mins read

Manufacturers’ AI spending aspirations decline sharply from 2023

Last year, manufacturers were moving full steam ahead toward generative AI. This year, many are less enthusiastic.

Only one in five generative AI initiatives have been successfully implemented by manufacturers, according to new data from Lucidworks. That’s lower than in other industries, where one in four generative AI initiatives have been implemented with some success.

About half — 48 percent — of manufacturers using generative AI reported higher cost savings from these technology-enabled initiatives, the study found. That’s simply higher than the industry-agnostic average, which found that 42 percent of organizations reported significant benefits from their work with generative AI.

According to Mike Sinoway, CEO of Lucidworks, manufacturers’ most successful generative AI initiatives are those they have implemented to cover operational costs.

“One of the most common uses of AI gen seems to be monitoring invoice elements and purchase price variances. (Manufacturers) seem to be planning to use AI tools to automatically respond to suppliers for discounts or credits,” he said.

But despite these early results, which were slow but promising, manufacturers’ interest in increasing spending on generative AI declined between 2023 and 2024. In 2023, 93 percent of manufacturing leaders reported that they planned to increase investment in generative AI. In 2024, that number fell to 58 percent.

The data shows that manufacturers’ investment may be lagging behind other industries, with 63 percent of global leaders across all industries reporting plans to further increase investment in 2024. In the U.S., nearly seven in 10 organizations said the same.

Sinoway said one reason for the failure may be a better understanding of what is really needed to implement and run generative AI systems.

“I think spending has also come down because people are seeing that this isn’t a quick fix—there has to be some really thoughtful planning to make sure that the benefits, which may not be immediate, are worth the potential risk of accuracy and the high cost. We’re out of the honeymoon phase where ‘anything is possible.’ Companies are slowing down their spending to do it right, not just do it fast,” he told Sourcing Journal.

Part of the reason for the decline in spending is manufacturing industry concerns about accuracy and reliability.

Lucidworks data shows that manufacturers have significant concerns about the accuracy of generative AI tool responses, with 44 percent of industry-specific respondents reporting these concerns, compared to 36 percent of non-industry respondents.

Sinoway said this may be because manufacturing requires a greater degree of precision than many other industries.

“B2B buyers are looking for very specific parts and materials; something ‘close’ is not enough, so the answers need to be extremely accurate. Then there’s the complication of dynamic pricing. Different contractors, for example, may have different material costs based on their unique contract with the manufacturer. Getting these numbers right is critical to maintaining trust and retaining business,” Sinoway said.

While manufacturers are primarily concerned about accuracy, there are other major concerns as well. 37% of manufacturers are concerned about cost, and 32% have concerns about safety.

In this sense, manufacturing companies seem to be an exception. Across all industries, data security and cost accuracy were the biggest concerns, with 46 percent and 43 percent of leaders, respectively. When it comes to costs, that number increased 14 times compared to 2023, which Sinoway said came as a surprise to Lucidworks researchers.

Sinoway said manufacturers’ lower concern about cost may be due to significant differences in consumer applications across industries.

For example, he said, 63 percent of retailers cited costs as a concern, which he said could be due to “the overwhelming volume of customer inquiries, (which) exponentially increases costs” compared with other industries such as manufacturing.

Regardless of the industry, Sinoway said, cost concerns may soon be quelled. He noted that costs associated with large language models (LLMs), a technology often used to power generative AI systems, “will continue to decline rapidly,” which could cause cost concerns to “level out” in the future.

“In just 12 months, we’ve developed a completely different understanding of what it takes to scale generative AI across hundreds of thousands of customers. It’ll be interesting to see how that changes in 2025, assuming that many commercial models become cheaper as they become more efficient,” he said.