A study released by Lucidworks on Wednesday (10) indicates that the implementation of Generative artificial intelligence projects (Generative AI) in the industry is occurring at a slower pace than expected. Concerns about accuracy are driving industry caution.
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The survey interviewed more than 2.500 global leaders involved in IT technology decisions. artificial intelligence. Among them, 58% of industrial leaders plan to increase investment in AI in 2024. This number is below the global (63%) and US (69%) average.
It is worth noting that, in 2023, optimism was much greater: 93% of leaders in general and 93% of industry leaders planned to increase spending on AI.
Why does it matter
Data from PitchBook last week showed a quarterly jump in venture capital funding in the US, driven mainly by significant investments in AI companies. Investors bet on startups hoping that revenue from AI adoption will bring significant returns. The Lucidworks study supports this bet, as nearly 50% of global manufacturers reported greater cost savings this year after implementing AI initiatives.
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“While many manufacturers see the potential benefits of Generative AI, challenges such as response accuracy and cost are leading them to take a more cautious approach,” said Mike Sinoway, CEO of Lucidworks.
Understanding Generative AI
Generative AI processes information or instructions to produce new content based on past data used in its training. However, it sometimes generates inaccurate or meaningless results, known as “hallucinations".
Fear about accuracy is shared by 36% of respondents overall. However, this number rises to 44% among industry leaders, demonstrating greater concern in the sector.
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Although only 20% of planned AI projects were implemented by manufacturers last year, 55% of manufacturers feel they are on par with their competitors when it comes to AI adoption.
Cost factor
In the last twelve months, 70% of companies have opted for AI business models, which are generally more expensive. Lucidworks suggests that, if efficiency and lower cost are proven, there may be a shift to more accessible open-source models.
Even with optimism regarding cost reduction with AI, manufacturers aim to maximize the value of the technology in a scenario of lower investment planned for this year.
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