Published on February 18, 2024, 6:43 am

A recent study conducted by Intel-owned cnvrg.io has shed light on the current state of generative AI based on large language models. Despite the widespread excitement surrounding this technology, the adoption of generative AI is still in its early stages.

The study surveyed 434 data scientists, developers, and IT managers and revealed that only 10% of companies have successfully integrated generative AI solutions into their business operations. The remaining 90% are still in the experimental phase, indicating that there is significant room for growth and development in this field.

Interestingly, 56% of respondents view generative AI as moderately important (32%), somewhat important (16%), or not important (8%). This perspective contrasts with the hype surrounding generative AI, emphasizing the practical challenges faced when implementing these solutions in real-world scenarios.

Key sectors such as financial services, banking, insurance, and defense have emerged as early adopters of AI technologies, particularly leveraging chatbots and translation solutions. In contrast, industries like education, automotive, and telecommunications are lagging behind in integrating AI initiatives.

The study also highlights that while there has been a notable increase in the use of chatbots (26%) and translation/text generation tools (12%) compared to 2022, many organizations are still grappling with challenges related to IT infrastructure. Respondents identified computational intensity and a lack of technical expertise as major obstacles hindering the deployment of large language models.

Moreover, 84% of participants expressed a need for enhanced technical skills to effectively manage complex language models. A significant portion (58%) reported low levels of AI integration, often running five or fewer models concurrently. This indicates a persistent struggle with adopting AI technologies within organizations.

Looking ahead to 2024, top priorities include implementing new AI technologies, scaling their usage within enterprises, and enhancing existing offerings. Despite advancements such as ChatGPT entering the market, enterprise adoption of AI remains relatively low due to various factors like skill shortages and regulatory constraints.

Intel’s Tony Mongkolsmai emphasized the importance of simplifying tasks to facilitate smoother collaboration between developers and AI technologies. The study underscores the industry’s need to address barriers like skills gaps and inadequate infrastructure to fully harness the potential of large language models in the near future.

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