Research Found Organizations Lacked AI-Ready Networks
Only 15 percent of global companies possess the infrastructure required to scale their artificial intelligence initiatives.
Updated on Oct. 8, 2026 in Artificial Intelligence
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A global research report from Cisco, published in October 2026, reveals that only 15 percent of organizations currently maintain networks flexible enough to support AI workloads. While 93 percent of surveyed decision-makers report that AI has accelerated their modernization efforts, critical infrastructure gaps remain.
Why it matters
The gap between strategic AI ambition and physical infrastructure readiness creates a bottleneck that threatens to stall deployment for most enterprises. As bandwidth and latency requirements intensify, the reliance on outdated systems presents a growing barrier to realizing the efficiency gains of AI agents.
Only 15 percent of organizations possess network infrastructure capable of supporting AI, while 60 percent of Australian decision-makers identify latency as a critical performance bottleneck. Organizations are increasingly turning to platform-led automation to manage these ecosystems.
The players
Cisco
A global provider of networking hardware, software, and cybersecurity solutions that serves as a primary architect for enterprise-scale IT infrastructure.
The details
Organizations are attempting to address these capacity pressures by modernizing physical network infrastructure to better handle real-time data flows. This involves shifting from legacy hardware to integrated environments where platform-led automation — software tools that automatically configure and monitor network traffic — is used to optimize ecosystems. These upgrades are necessary to meet the increasing demand for bandwidth, particularly as 73 percent of organizations identify Generative AI as the primary driver of future resource consumption.
Timeline
October 2026: Publication of the Cisco research report.
Next two years: Period in which 63 percent of Australian organizations expect to face network capacity constraints.
The Tech Race
This data highlights a critical divergence between the rapid deployment of AI models and the slower, capital-intensive pace of hardware upgrades. It underscores a shift where infrastructure readiness has become the primary constraint in the global race to scale enterprise AI.
Organizations should prepare for potential operational slowdowns as 74 percent of Australian respondents expect AI workloads to reach network capacity limits. IT teams will need to prioritize investments in bandwidth and low-latency hardware to maintain service levels over the coming 24 months.
The takeaway
The gap between AI ambition and network capacity remains a structural weakness that requires immediate capital attention. Executives should watch for further industry-wide benchmark reports in 2027 to see if modernization investment rates accelerate to meet projected two-year capacity deadlines.
Further reading
For more on the infrastructure challenges associated with model deployment, visit the Artificial Intelligence section.
Source note: This article includes information reported by ARN.
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