AI Project Bottlenecks
IDC Survey Finds Networking Infrastructure Limits Stymie Agentic AI Projects' Production
Anonymous
Networking infrastructure — not the AI models themselves — is the biggest reason companies are failing to move AI projects from pilot to production, according to a new IDC report. SecurityBrief Asia reports that IDC's 2026 AI in Networking Special Report Survey, sponsored by Google Cloud, found that security gaps, automation shortfalls, and staffing limits are the top obstacles holding organizations back.
The findings arrive as businesses race to deploy agentic AI — systems that can act and make decisions on their own. But the pipes carrying all that data are getting in the way, IT Brief Asia reports, with networking problems directly causing project delays and, in some cases, full abandonment.
Networking Is the Weak Link Slowing AI Deployments
Most companies assume that picking the right AI model is the hard part. The IDC survey says otherwise. According to SecurityBrief News, the real bottleneck sits in the network layer — the infrastructure that connects data, compute, and AI systems. When that layer is slow, insecure, or poorly managed, AI projects stall before they ever reach real users.
The problem is not small. IT Brief News reports that networking issues are directly linked to project abandonment — not just delays. That means companies are spending time and money on AI pilots that never graduate because the underlying infrastructure cannot keep up with the workload.
Security, Staffing, and Automation Gaps Compound the Problem
IDC identified three core obstacles beyond raw networking performance. Security is the biggest concern — agentic AI systems can access data and take actions automatically, which creates new risks if the network is not properly locked down. Automation shortfalls mean teams still rely on manual processes that cannot scale. And staffing limits mean many organizations simply do not have enough skilled people to manage complex AI infrastructure, according to SecurityBrief Asia.
Together, these three gaps form a compounding problem. A team short on staff cannot automate fast enough. Without automation, security gaps multiply. And without security, agentic AI — which acts on its own — becomes a liability rather than an asset.
Platform vs. Best-of-Breed: Companies Are Split on Strategy
The survey also exposed a strategic divide among organizations. Some want a single platform that handles everything. Others prefer to mix and match the best individual tools — an approach called best-of-breed. IT Brief Asia reports that IDC found no clear winner, with companies split between the two approaches as they try to build AI workloads.
The split matters because it shapes how companies build their networks going forward. A single platform is easier to manage but may lock organizations in. Best-of-breed offers more flexibility but adds complexity — which circles back to the same staffing and automation problems the survey already flagged.
What a Future-Ready Agentic AI Network Needs to Look Like
IDC did not just flag problems — it also outlined what a good solution looks like. According to SecurityBrief News, the ideal platform for agentic AI must support integration with third-party and open-source tools. That means no walled gardens. Companies need the freedom to plug in the tools they already use.
Crucially, the report says security and observability — the ability to monitor what the AI is doing in real time — must be addable without tearing down the whole architecture. IT Brief News notes this is a key requirement: organizations need to bolt on these functions as needs grow, not rebuild from scratch every time a new risk emerges.
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