Despite more than three years of investment since the generative AI wave began, most enterprises have yet to realise meaningful value from artificial intelligence, according to a GlobalData report that identifies four interdependent areas where organisations are falling short.
The report, titled Overcoming Barriers to Enterprise AI Adoption, maps failure modes across strategy, data and technology, talent and governance. Each pillar carries its own blockers; the analysis suggests that weakness in any one undermines the others.
On the talent side, GlobalData draws a distinction between two kinds of skills gap. Technical shortages — AI engineers, architects, research scientists, governance leads — are widely recognised and remain acute. Less discussed is the foundational layer: the working knowledge that non-specialist employees need to use AI tools effectively and without creating risk. GlobalData argues this gap is, in aggregate, the more widespread problem.
The governance picture is one where competitive pressure has outrun controls. Businesses are deploying AI faster than they are building the oversight structures to manage it, partly because in-house expertise is scarce and partly because regulatory guidance remains unsettled in many jurisdictions.
GlobalData frames the transition from generative to agentic AI as raising the stakes across all four pillars. The shift toward agents seeking real-world returns, combined with growing interest in physical AI in sectors including defence, manufacturing and logistics, makes the infrastructure and governance gaps more consequential.
The report was produced using GlobalData's proprietary intelligence platform. GlobalData, listed on the London Stock Exchange as DATA, serves more than 5,000 organisations.
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