Enterprise data center and cloud networking for AI success
New Omdia research reveals how enterprises are modernizing hybrid cloud and data center networks to support AI workloads, scale AI infrastructure, and improve operational resilience.
Key takeawaysThis key takeaway was generated through LLMs crawling the page and coming up with an overview of the content.Omdia research of 400 North American IT professionals shows enterprises are redesigning hybrid cloud and data center networks to support AI workloads that demand much higher bandwidth, lower latency, and greater resiliency, which traditional networks often cannot meet. The study highlights that network monitoring, observability, security, automation, and high-bandwidth connectivity are top priorities to scale AI infrastructure and maintain performance across distributed multicloud environments. BlueCat positions its Intelligent NetOps portfolio — combining DDI, multicloud management, observability, and AI-enabled analytics — as a way to improve visibility, simplify operations, and reduce infrastructure complexity for AI-ready networks.
AI is fundamentally reshaping enterprise networking requirements. As organizations move AI initiatives from pilot projects into production environments, traditional cloud and data center networks are struggling to keep pace with increasing bandwidth demands, operational complexity, security requirements, and visibility gaps. This new Omdia research, co-sponsored by BlueCat, surveyed 400 IT professionals responsible for data center and cloud networking infrastructure to understand how enterprises are preparing their networks for AI success. The findings reveal that hybrid environments remain the dominant operating model, but many organizations lack the automation, observability, and AI- ready infrastructure needed to scale effectively. The research also highlights why network monitoring, security, multicloud management, and AI-driven operations are becoming strategic priorities, particularly as AI workloads demand ultra-low latency, higher throughput, dynamic traffic management, and intelligent automation across distributed environments.
What the research reveals
AI is accelerating one of the most significant infrastructure transformations enterprises have faced in decades. The research found that hybrid networking environments are now the norm, with workloads distributed across public cloud, SaaS, colocation, and traditional data centers. Organizations identified inconsistent network performance, incomplete visibility, inconsistent security controls, and traffic management complexity as top challenges in hybrid environments.
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
Download the omdia research report
See how enterprises are transforming networking for AI success
What you’ll learn:
- The networking investments organizations are prioritizing to nsupport AI workloads
- Why observability, automation, and security are central to AI-nready infrastructure
- How enterprises are preparing hybrid cloud and data center nnetworks for AI scale