Key findings
The research demonstrates that enterprise AI adoption is no longer an experimental initiative… it is rapidly becoming a production-scale operational requirement. Organizations are evolving their networking strategies to support distributed AI workloads across cloud, colocation, and on-premises environments while balancing security, performance, visibility, and operational efficiency. One of the strongest findings is the growing importance of intelligent network operations. Enterprises are prioritizing network monitoring, observability, AI-powered analytics, and automation to manage increasingly dynamic environments. Traditional approaches to networking operations are proving insufficient for AI-driven infrastructure demands, especially as bandwidth requirements rise into the 400G, 800G, and even 1.6 Tbps range. The findings also reinforce the strategic role of integrated security and multicloud visibility. Organizations recognize that AI success depends on secure, resilient, and observable infrastructure that can adapt in real time to changing workloads and application demands.
What the data shows
AI readiness across enterprise networks
The report reveals that many organizations remain underprepared for AI-scale networking demands, with significant infrastructure modernization still required across cloud and data center environments. Security, reliability, and performance emerged as the top indicators of AI readiness.
AI is driving massive bandwidth growth
AI workloads are rapidly pushing organizations toward 400G and 800G networking requirements for AI training clusters, cloud connectivity, and inter-data-center networking. These requirements are reshaping enterprise networking architectures and operational priorities.
Monitoring and security lead AI networking investments
Network monitoring, observability, and security technologies are the top upgrade priorities for enterprises preparing for AI. The findings highlight a growing demand for intelligent automation, threat detection, and real-time operational visibility across hybrid environments.
What this mans for networks
What this means for network and infrastructure leaders The transition to AI-ready infrastructure is forcing organizations to rethink how networks are designed, secured, monitored, and automated. Organizations that rely on fragmented tools, limited visibility, or manual operations risk infrastructure bottlenecks that slow AI initiatives and increase operational complexity.
- Improving observability across hybrid and multicloud environments
- Scaling network performance and bandwidth for AI workloads
- Automating network operations and configuration management
- Strengthening integrated security and threat detection capabilities
- Reducing operational complexity through intelligent NetOps platforms
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