Manufacturers slow Gen AI rollout on rising accuracy concerns
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Technology
Manufacturers slow Gen AI rollout on rising accuracy concerns
(Reuters) - Manufacturers are deploying planned Generative AI initiatives at a slower-than-anticipated pace on concerns around accuracy, Lucidworks said in a study on Wednesday.
The study surveyed over 2,500 leaders worldwide involved in AI technology decision-making and found that 58% of manufacturing leaders plan to increase spending on AI in 2024. This is lower than the global consensus of 63% and U.S. consensus of 69%.
In 2023, 93% of all leaders and 93% of manufacturing leaders planned to increase spending on AI.
WHY IT’S IMPORTANT
PitchBook data from last week showed a quarterly jump in U.S. venture capital funding, largely driven by significant investments in AI companies.
Investors are betting on startups with the hope that revenue from AI adoption will yield significant returns. Nearly 50% of manufacturers worldwide reported increased cost savings this year after adopting AI initiatives, according to Lucidworks' study.
"While many manufacturers see the potential benefits of generative AI, challenges such as response accuracy and cost are causing them to take a more cautious approach," said Mike Sinoway, CEO of Lucidworks.
CONTEXT
Generative AI processes inputs or prompts to produce new content based on past data used to train it. However, it sometimes generates inaccurate or nonsensical outputs known as hallucinations.
While 36% of all respondents expressed concerns about response accuracy due to hallucinations, a higher number of manufacturing respondents, 44%, share this concern.
Investment in AI startups more than doubled in the second quarter.
Despite only 20% of planned AI projects being implemented by manufacturers in the past year, 55% of them feel that they are on par with their peers in AI adoption.
COST FACTOR
Over the past year, 70% of manufacturing companies opted for more expensive commercial AI models. Lucidworks suggests that a shift to more accessible open-source models could occur if they prove to be as efficient and more resourceful at a lower cost.