AI Adoption in Enterprises – Progress Hindered by Challenges

A recent study conducted by, a subsidiary of Intel, has cast light on the slow pace of AI Adoption within enterprises. The 2023 ML Insider survey, which engaged a global cadre of data scientists and AI experts, reveals a significant disconnect between the optimistic discourse surrounding generative AI (GenAI) and its actual operational deployment within organizations.

The State of AI Adoption in Enterprises

The survey’s findings reveal that a mere 10% of organizations have effectively transitioned GenAI solutions into their production processes. This figure starkly contrasts with the heightened expectations and public enthusiasm surrounding GenAI technologies. The sectors leading in AI adoption include Financial Services, Banking, Defense, and Insurance, all recognizing the potential of AI to enhance efficiency and enrich customer experiences. Conversely, industries such as Education, Automotive, and Telecommunications exhibit reticence, with AI initiatives still at a nascent stage.

Markus Flierl, Intel’s corporate VP for the developer cloud, suggests that the tepid adoption rate might be attributed to the challenges organizations encounter when implementing large language models (LLMs) that are the bedrock of GenAI. Flierl advocates for increased accessibility to cost-efficient infrastructure and services, such as those offered by and the Intel Developer Cloud. He posits that such advancements could facilitate the customization, fine-tuning, and deployment of existing LLMs, thereby reducing the need for specialized AI talent to navigate the inherent complexities.

Survey Highlights and Key Challenges

The survey delineates several critical barriers impeding the broader adoption of AI:

  1. Infrastructure Constraints: About 46% of respondents identified inadequate infrastructure as the primary obstacle, noting that the compute-intensive nature of LLMs places a significant strain on IT resources.
  2. Skills Gap: A staggering 84% acknowledged the need for improved skills to effectively manage and leverage language models, with only 19% expressing confidence in their proficiency.
  3. Limited Deployment: Despite advancements in GenAI, only 25% of organizations have integrated generative models into their production environments.
  4. Low AI Integration and Execution Difficulties: A majority of organizations, 58%, are running five or fewer AI models, with no substantial increase since the previous year. Furthermore, 62% rate the execution of successful AI projects as challenging, particularly for larger companies.

The Path Forward

The survey underscores a critical narrative: despite the burgeoning interest and potential of AI, as exemplified by tools like ChatGPT, the journey from experimentation to full-fledged integration is fraught with challenges. Factors such as the skills gap, regulatory concerns, reliability issues, and infrastructural limitations serve as formidable barriers to the rapid scaling of AI within enterprises.

Tony Mongkolsmai, Software Architect and Technical Evangelist at Intel stresses the urgency of addressing the technical skills gap, which significantly impedes the adoption of ML and LLMs. He calls for industry-wide efforts to demystify complexities and streamline tasks, thereby empowering developers and accelerating the organizational adoption of GenAI capabilities.


The 2023 ML Insider Survey by, reveals a cautious and tentative approach to GenAI adoption across various industries. Despite the potential and hype surrounding AI, enterprises are navigating a landscape riddled with challenges, including infrastructural inadequacies, skill deficiencies, and implementation hurdles. For AI to transition from a promising technology to an integral component of enterprise operations, concerted efforts are required to bridge the gaps in infrastructure, expertise, and implementation strategies. As the industry rallies to address these challenges, the coming years will be pivotal in determining the trajectory and the transformative potential of AI within the enterprise sector.

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Derick Payne
My name is Derick Payne. With a deep-seated passion for programming and an unwavering commitment to innovation, I've spent the past 23 years pushing the envelope of what's possible. As the founder of Rizonetech and Rizonesoft, I've had the unique opportunity to channel my love for technology into creating solutions that make a difference.

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