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AI Skills Shortage Deepens as Enterprise Demand Grows Faster Than Talent Supply

 

The shortage of skilled professionals in artificial intelligence is becoming a major concern for enterprises, as organizations race to adopt the technology without a matching increase in qualified talent. The latest Harvey Nash Digital Leadership report, released by Nash Squared in May, highlights a sharp rise in demand for AI skills across industries—faster than any previous tech trend tracked in the last 16 years. 

Based on responses from over 2,000 tech executives, the report found that more than half of IT leaders now cite a lack of AI expertise as a key barrier to progress. This marks a steep climb from just 28% a year ago. In fact, AI has jumped from the sixth most difficult skill to hire for to the number one spot in just over a year. Interest in AI adoption continues to soar, with 90% of surveyed organizations either investing in or piloting AI solutions—up significantly from 59% in 2023. Despite this enthusiasm, a majority of companies have not yet seen measurable returns from their AI projects. Many remain stuck in early testing phases, unable to deploy solutions at scale. 

Numerous challenges continue to slow enterprise AI deployment. Besides the scarcity of skilled professionals, companies face obstacles such as inadequate data infrastructure and tight budgets. Without the necessary expertise, organizations struggle to transition from proof-of-concept to full integration. Bev White, CEO of Nash Squared, emphasized that enterprises are navigating uncharted territory. “There’s no manual for scaling AI,” she explained. “Organizations must combine various strategies—formal education, upskilling of tech and non-tech teams, and hands-on experimentation—to build their AI capabilities.” She also stressed the need for operational models that naturally embed AI into daily workflows. 

The report’s findings show that the surge in AI skill demand has outpaced any other technology shift in recent memory. Sectors like manufacturing, education, pharmaceuticals, logistics, and professional services are all feeling the pressure to hire faster than the talent pool allows. Supporting this trend, job market data shows explosive growth in demand for AI roles. 

According to Indeed, postings for generative AI positions nearly tripled year-over-year as of January 2025. Unless companies prioritize upskilling and talent development, the widening AI skills gap could undermine the long-term success of enterprise AI strategies. For now, the challenge of turning AI interest into practical results remains a steep climb.
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Deciding Between Public and Private Large Language Models (LLMs)

 

The spotlight on large language models (LLMs) remains intense, with the debut of ChatGPT capturing global attention and sparking discussions about generative AI's potential. ChatGPT, a public LLM, has stirred excitement and concern regarding its ability to generate content or code with minimal prompts, prompting individuals and smaller businesses to contemplate its impact on their operations.

Enterprises now face a pivotal decision: whether to utilize public LLMs like ChatGPT or develop their own private models. Public LLMs, such as ChatGPT, are trained on vast amounts of publicly available data, offering impressive results across various tasks. However, reliance on internet-derived data poses risks, including inaccurate outputs or potential dissemination of sensitive information.

In contrast, private LLMs, trained on proprietary data, offer deeper insights tailored to specific enterprise needs, albeit with less breadth compared to public models. Concerns about data security loom large for enterprises, especially considering the risk of exposing sensitive information to hackers targeting LLM login credentials.

To mitigate these risks, companies like Google, Amazon, and Apple are implementing strict access controls and governance measures for public LLM usage. Moreover, the challenge of building unique intellectual property (IP) atop widely accessible public models drives many enterprises towards private LLM development.

Enterprises are increasingly exploring private LLM solutions tailored to their unique data and operational requirements. Platforms like IBM's WatsonX offer enterprise-grade tools for LLM development, empowering organizations to leverage AI engines aligned with their core data and business objectives.

As the debate between public and private LLMs continues, enterprises must weigh the benefits of leveraging existing models against the advantages of developing proprietary solutions. Those embracing private LLM development are positioning themselves to harness AI capabilities aligned with their long-term strategic goals.
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