The AI Readiness Gap: Why Network Infrastructure May Be Holding Back Innovation

AI workloads place greater demands on bandwidth, latency, network visibility and traffic management.

Among the many conversations about midmarket organizations' struggles with scaling AI projects beyond the pilot stage, some industry experts are advising organizations to take a closer look at their network infrastructure.

The concern comes at a time when AI ambitions continue to outpace enterprise adoption. Despite two-thirds of global CEOs acknowledging that scaling AI is among their top three priorities, a recent SAS and IDC survey found that more than a third of businesses are still exploring AI in isolated, unconnected ways, with only 9 percent having fully embedded it into their strategy, operations and decision-making.

That raises an important question for IT leaders: are the networks they rely on today capable of supporting the next phase of AI adoption?

Rethinking Networks In The AI Era

Traditional midmarket networks were built for predictable business traffic: SaaS applications, email, video conferencing and web browsing. But AI is changing those traffic patterns. Instead of moving data primarily between users and cloud applications, AI workloads generate continuous flows between applications, storage systems, inference engines, cloud services and edge locations. These place greater demands on bandwidth, latency, network visibility and traffic management.

Those pressures were a focus in Broadcom's 2026 State of Network Operations report, which found that network congestion, limited visibility and latency are some of the biggest obstacles to scaling AI.

It's a trend that Sandip Patel, senior cloud solution architect at Microsoft, has observed with customers adopting AI.

"Many midmarket organizations assume AI readiness is primarily a compute problem, but in practice, the biggest bottlenecks often originate in the network. AI workloads move significantly more data across users, applications, edge locations, and cloud services than traditional business applications,” Patel told MES Computing.

Enterprise AI architect at UPS, Kaan Esendemir, has been grappling with many of the same issues from an enterprise deployment perspective. He said he sees more bandwidth and latency issues due to AI workload demands.

"If we assume that we can run certain GPT models without issue to handle emails and IMs, that should be covered under normal use workloads. But once you're thinking about cloud agent infrastructure running 24/7, organizations need to understand what can quickly become the bottleneck," Esendemir said.

For organizations to get the most value from AI, Patel said they must be ready to treat network modernization as part of their AI strategy from the moment they conceive the idea of AI adoption.

Don't Trust 'AI-Ready' Labels

As vendors race to position products as "AI-ready," IT leaders face a more practical question: ready for which workload?

Jose Lejin P J, principal member of technical staff at Salesforce, advised organizations to treat vendor claims with caution by not relying on product specifications alone.

"Do not believe the spec sheets without running your internal pilot, because 'AI-ready' depends on the workload,” he said.

Lejin P J added that pilot assessment should extend beyond internet connectivity. Organizations also need to understand bandwidth headroom inside their networks, storage performance close to compute resources and how much latency individual AI use cases can tolerate.

Power and cooling requirements also shouldn't be overlooked.

"GPUs consume much more electricity than the equipment initially planned for many edge locations."

That advice was also echoed by Patel, who maintains that before expanding AI across branch offices, warehouses or retail locations, organizations should evaluate network latency between users, edge locations and AI services, alongside available bandwidth, data accessibility and observability across operational systems and cloud platforms.

Esendemir believes operational readiness deserves just as much attention. Organizations, he cautions, must prepare for increased logging, ensure they can patch distributed sites without disrupting operations, and continuously monitor latency as AI workloads expand.

Addressing these constraints early can help organizations avoid expensive redesigns once AI initiatives move beyond isolated pilots into production.

Four Priorities for AI-Ready Networks

Once organizations understand where the bottlenecks lie, the next challenge is ensuring their networks can support AI securely and consistently as deployments expand across distributed environments.

Drawing on insights from these experts, several priorities stand out for midmarket IT leaders:

Adopt an identity-first security model: Lejin recommends treating every workload, endpoint and AI agent as an identity governed by least-privilege access rather than relying on traditional network trust. "Trust the identity and not the network," he says.

Secure AI traffic and automate policy enforcement: Route AI traffic through secure gateways with data loss prevention (DLP) inspection and use SASE or SSE to apply consistent security policies across every edge location.

Apply the same security fundamentals everywhere: Organizations don't need a separate security model for AI. Instead, they should keep AI data encrypted in transit, maintain comprehensive logging and preserve visibility across every deployment.

Make network modernization part of your AI strategy: According to Patel, organizations seeing the greatest return from AI are investing in connectivity, visibility, security and data infrastructure before those shortcomings become obstacles.

The winners won't necessarily be the companies with the largest AI budgets. They'll be the ones that built networks capable of securely moving data to the right place, at the right time, with the right governance.

For many midmarket organizations, building AI-ready networks may prove just as important as adopting AI itself.