Despite substantial investment and strategic focus on AI over nearly four years, most enterprises are still in the early phases of AI adoption and have yet to realise meaningful value from the technology, according to GlobalData.
AI has been the hottest enterprise technology since OpenAI released ChatGPT in November 2022, kick-starting the generative AI boom. Enterprises have since shifted their focus from generative to agentic AI as they seek meaningful returns on AI investments, while physical AI is also gaining traction in sectors like defence, manufacturing, and logistics.
GlobalData’s latest report – Overcoming Barriers to Enterprise AI Adoption – reveals there are four core pillars for successful enterprise AI adoption: strategy; data and technology; talent; and governance. The report also identifies the key barriers to enterprise AI adoption for each pillar, along with strategies to overcome them.
Jordan Strzelecki, senior analyst, Strategic Intelligence team at GlobalData, comments: “Scaling AI requires significant resources and presents major challenges, but it is crucial if enterprises are to derive meaningful value from their AI investments. Many enterprises have been unwilling or unable to commit to such a complex, expensive, and high-stakes transition. However, those with the resources must commit and do the hard work to scale AI. Not doing so would be a strategic failure.”
Demand for AI talent continues to outpace supply, with enterprises facing two main types of AI skills shortages: technical and foundational. Technical AI specialists, including engineers, architects, research scientists, and governance leads, remain scarce and highly sought after. Foundational AI skills shortages are arguably more widespread, encompassing the core and role-specific AI knowledge that employees require to use AI effectively and safely.
“Enterprises require an AI talent strategy to ensure they have the right skills to deploy AI competitively and responsibly,” says Strzelecki. “A good starting point is developing an AI skills taxonomy that maps the technical and foundational AI skills needed across the organisation to deliver on the company’s AI strategy. Enterprises can then use this taxonomy as a benchmark to assess current AI skills, pinpoint gaps, and implement internal and external measures to fill them.”
Enterprises also face well-documented risks from AI. However, considerable AI governance gaps exist as intense competitive pressure to deploy AI, limited in-house AI governance expertise, and regulatory ambiguity push enterprise AI adoption ahead of controls.
“As organisations scale AI, risks multiply, and the need for robust oversight grows,” Strzelecki says. “Corporate executives must be mindful that if something goes wrong with AI, their company could face substantial reputational and financial risks.”