What’s keeping so many businesses from adopting generative AI?

Despite growing interest, the majority of organizations face adoption hurdles such as infrastructure challenges and knowledge gaps.

 An illustrative image of an artificial intelligence (AI) bot. (photo credit: INGIMAGE)
An illustrative image of an artificial intelligence (AI) bot.
(photo credit: INGIMAGE)

As organizations grapple with the integration of large language models (LLMs) — the technology driving generative AI applications such as the monolithic ChatGPT — the use of chatbots and virtual agents has surged by 26%, and translation/text generation has witnessed a 12% increase in 2023.

In its 2023 ML Insider survey, Intel company cnvrg.io sheds light on the state of AI adoption, highlighting that despite the heightened interest in AI across industries, a majority of organizations are yet to fully leverage generative AI (GenAI) technology.

Global adoption trends

With insights gathered from a global panel of 430 technology professionals, the report reveals that organizations that have embraced GenAI have experienced significant benefits.

The survey revealed that 58% of respondents who have utilized generative AI reported improved customer experiences, with the technology serving to enhance satisfaction through personalized interactions and advanced conversational capabilities. Additionally, 53% noted increased operational efficiency, attributing it to GenAI’s ability to automate tasks and optimize workflows. In terms of product capabilities, 52% of organizations reported enhancements, spanning from more intelligent software solutions to innovative products infused with advanced AI functionalities.

 Artificial Intelligence words are seen in this illustration taken March 31, 2023 (credit: REUTERS/DADO RUVIC/ILLUSTRATION/FILE PHOTO)
Artificial Intelligence words are seen in this illustration taken March 31, 2023 (credit: REUTERS/DADO RUVIC/ILLUSTRATION/FILE PHOTO)

Furthermore, 47% of GenAI-using respondents highlighted tangible cost savings resulting from the technology’s adoption, achieved through streamlined processes, resource optimization, and the development of cost-effective AI solutions. These benefits collectively showcase GenAI as a catalyst for holistic business transformation, driving positive change in customer interactions, internal operations, and fostering innovation—a strategic shift towards a more agile, responsive, and competitive business landscape.

Challenges Hindering Adoption

Despite the promises of GenAI, the survey uncovers several challenges hindering widespread adoption. Infrastructure emerged as a predominant obstacle, with 46% of respondents identifying it as the primary challenge in developing large language models (LLMs) into practical, production-ready products. Compliance issues regarding evolving regulations and adherence to privacy standards, cited by 28%, complicate the landscape.

The study also brings attention to a significant knowledge gap within organizations, as 84% of respondents admit that their skills need improvement due to the increasing interest in LLM adoption. Only 19% claim to possess a strong understanding of the mechanisms behind LLMs’ response generation, highlighting the urgent need for upskilling to overcome barriers to GenAI adoption.

The Future of GenAI

Looking ahead, the survey anticipates increased adoption in the coming year as organizations gain greater access to cost-effective infrastructure and services. The industry is poised for growth as fine-tuning, customization, and deployment of existing LLMs become more streamlined, reducing the reliance on specialized AI expertise.

Markus Flierl, vice president and general manager of Intel Cloud Services, provided insight into the industry landscape.“Already in its early stages of development, Generative AI became the most talked about and revolutionary technology in 2023, but the survey shows us that most organizations in the world have not yet adopted generative artificial intelligence solutions, this is due to the challenges in implementing large language models,” said Flierl.


“We believe that by expanding access to cost-effective infrastructure and services, such as those provided by cnvrg.io and Intel Developer Cloud, a more significant adoption of Generative AI applications will be possible in 2024,” he added. “These tools will facilitate the tuning and deployment of LLM models without the need for special specialization in the field.”