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.
