Unlocking Business Success with Intelligent Network Insights

Key points

47% of enterprises believe their network observability tools are fully prepared to monitor and manage AI traffic, posing a significant challenge for AI project leaders.
Companies with fully prepared observability tools are five times more likely to expect success with their AI networking strategies, highlighting the importance of network observability in AI readiness.
Real-time network monitoring and intelligent analysis are crucial for managing AI networks, with 69% of survey participants requiring real-time infrastructure monitoring and 51% needing more real-time network flow monitoring.

As the adoption of AI applications continues to grow, network infrastructure teams are struggling to optimize their networks for AI traffic. This is a critical issue, as AI workloads are highly sensitive to latency, packet loss, and congestion, requiring seamless connectivity across data centers, clouds, and edge environments. According to a research report by Enterprise Management Associates (EMA), titled Readying Enterprise Networks for Artificial Intelligence, only 47% of enterprises believe their network observability tools are fully prepared to monitor and manage AI traffic. This finding should serve as a warning for any AI project leader, as AI training and inference jobs will fail without deep, real-time visibility into network performance.

The report highlights the importance of network observability in AI readiness, with companies that have fully prepared observability tools being five times more likely to expect success with their AI networking strategies. These organizations tend to have an AI center of excellence guiding strategy, significant IT budget allocations for AI, and fewer concerns about compliance and privacy risks. The research also found that AI workloads are distributed across hybrid architectures, residing in private data centers, public clouds, and edge computing environments, emphasizing the need for end-to-end network observability.

The biggest priorities for improving network observability are public cloud networks and cloud interconnects, with enterprises also looking beyond the big three hyperscalers – AWS, Azure, and Google – to emerging GPU-as-a-service providers. However, these providers may have less mature mechanisms in place for supporting network observability, posing a challenge to visibility. Additionally, enterprises need to improve visibility into their data center network fabrics and WAN edge connectivity services.

The report also emphasizes the need for real-time data, with 69% of survey participants requiring real-time infrastructure monitoring that SNMP simply cannot support. Real-time telemetry is necessary to close visibility gaps, and network teams will have to adopt streaming network telemetry to achieve this level of metric granularity. Furthermore, 51% of respondents need more real-time network flow monitoring, with network flow technologies such as NetFlow and IPFIX delivering data nearly in real-time.

Network teams also need their network observability tools to be smarter about AI networks, with 59% wanting their tools to identify AI applications in network traffic. This will allow them to monitor AI application performance, optimize networks for AI traffic, and detect rogue AI adoption. Many are also looking for advanced analytical capabilities tuned to AI traffic, including predictive analysis and anomaly detection.

In summary, network observability is key to AI readiness, and investments in real-time, intelligent, and comprehensive network observability will determine a network team’s success at supporting AI adoption. As AI workloads grow in complexity and scale, effective observability will be the difference between innovation and failure, making it essential for enterprises to prioritize network observability in their AI strategies. With Microsoft Azure and other cloud providers playing a critical role in AI adoption, the need for network observability will only continue to grow, emphasizing the importance of real-time monitoring and intelligent analysis in managing AI networks.