Most enterprises still unprepared for AI deployment, researcher warns

Infrastructure bottlenecks slow enterprise AI adoption

Most enterprises remain unprepared to deploy AI systems at scale despite widespread industry enthusiasm, with infrastructure modernization and cost management emerging as primary obstacles to moving beyond limited experimentation.

Current adoption concentrates heavily on large language models, edge computing, and narrow agent applications for calendaring, software development, and process automation. Companies have yet to extend AI meaningfully into supply chains, inventory management, and other core business functions that demand more rigorous data governance, security controls, and operational integration, according to David Linthicum, founder and lead researcher at Linthicum Research.

"Right now we're just getting started with AI. I know everybody thinks there's a huge AI party out there and they haven't been invited, but the reality is businesses and enterprises are just getting going," Linthicum said during VMware Explore. "They're figuring out their infrastructure. They're figuring out how much it's going to cost."

Why it matters

The gap between AI rhetoric and enterprise readiness carries strategic implications for technology vendors and corporate buyers alike. Companies rushing to deploy autonomous agents without adequate business justification risk introducing operational complexity and security vulnerabilities that outweigh potential benefits. Meanwhile, infrastructure providers face demand for simplified deployment paths that can bridge the gap between bare-metal systems and production AI workloads.

Broadcom targets private AI infrastructure

Broadcom's VMware AI Factory, built on VMware Cloud Foundation, represents one vendor response to infrastructure challenges. The platform aims to automate the deployment path from physical infrastructure to model deployment for organizations pursuing private AI implementations.

Linthicum noted that many attendees at the VMware event were actively seeking partners to modernize infrastructure specifically to support on-premises AI systems, indicating sustained demand for private deployment options alongside public cloud alternatives.

Agent complexity exceeds business requirements

The current wave of agentic AI applications frequently introduces unnecessary architectural complexity, Linthicum warned. His assessment suggests that approximately 95 percent of applications marketed as agentic AI do not require agent-based architectures and would function more efficiently with simpler designs.

"The reality is people just need to calm down with the agent stuff," he said. "Probably 95% of the applications that I see that are agentic AI applications don't need to be, and they're hitting a thumbtack with a sledgehammer."

The observation highlights a pattern where technology selection precedes business justification, with companies adopting agent frameworks because of market momentum rather than operational necessity. This approach adds security concerns and operational overhead without delivering commensurate value.

These details were first reported by SiliconANGLE during coverage of VMware Explore.