Cisco Research Has Found Global AI Infrastructure Gaps
Most organizations currently lack the network flexibility required to fully support the scaling of AI workloads.
Updated on Oct. 8, 2026 in Artificial Intelligence
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A global study by Cisco revealed that only 15 per cent of organizations possess network infrastructure currently capable of supporting AI scaling. While 93 per cent of surveyed leaders claim AI has accelerated their modernization efforts, many struggle with significant technical and budget limitations.
Why it matters
As organizations rush to integrate generative AI, the resulting demand for real-time applications and massive data processing is pushing existing network capacities to their limits. Without necessary upgrades, firms face performance bottlenecks that threaten to stall their strategic AI objectives.
Of the 3,472 surveyed decision-makers, 93 per cent reported that AI has accelerated network modernization plans. However, only 15 per cent of organizations currently maintain networks with the flexibility required to effectively support AI scaling.
The players
Cisco
Cisco is a global technology corporation that designs, manufactures, and sells networking hardware and software solutions.
The details
Organizations are increasingly shifting toward platform-led automation to optimize ecosystems and prepare physical infrastructure for capacity pressures. In Australia, 90 per cent of organizations cite budget constraints as the primary factor hindering their modernization efforts, while 64 per cent note that performance remains strictly dependent on available bandwidth.
Timeline
The Cisco research report was published in October 2026.
Sixty-three per cent of Australian organizations expect network capacity constraints within the next two years.
The Tech Race
This study underscores a critical turning point where enterprise ambitions for generative AI deployment have outpaced the evolution of legacy network architectures. It highlights a shift where the speed of software innovation now mandates a total overhaul of the underlying hardware ecosystems.
For users and developers, these infrastructure limitations often manifest as increased latency and service outages during peak data usage. Companies may experience slower AI tool performance until the necessary network upgrades are successfully implemented.
The takeaway
Organizations should prioritize infrastructure flexibility to ensure their long-term AI strategies remain viable in a bandwidth-constrained environment. Leaders should evaluate whether their current network architectures can withstand the projected capacity demands of generative AI models.
Further reading
For broader trends regarding the intersection of enterprise software and hardware, visit the Artificial Intelligence section.
Source note: This article includes information reported by ARN.
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