While some may feel Network Observability is still a buzz word; it truly is the only way to build a foundation for AI-ready networks. What does AI-ready networks mean? It means you build so much resiliency into your networks that you go from good-enough network performance to near-perfect network performance. Any Artificial intelligence (AI) initiative you tackle will demand this from your network.

Meta has publicly reported that their AI workloads spend 54% of their time on the network. Any network blips, packet loss or capacity issues will not lead Meta to the outcomes they expect from their AI initiatives.

A successful network observability practice means improving operational efficiency through baby steps. There is no reason you need to boil the ocean here and rip and replace your current toolsets or processes. But the more network complexity you deal with (software-defined tech, work from anywhere, public network usage, cloud), the more you need to continually improve network operations to stay ahead of this complexity.

Step 1 Manual (hyper-reactive)

Only about half the on-prem network is monitored with free/open source tools lacking any enterprise support and advanced features. Additionally, network alert noise is extremely high.

Action: Integrate or consolidate toolsets. 80% of orgs report a high priority to consolidate while 72% seek tight integration in their tools. Network operations with tight integration across tools have more success with NetOps.

Step 2 Traditional (reactive)

With tighter integration of monitoring tools, you now have 100% of your on-prem network monitored, with multi-vendor support. Some of your monitoring tools are still siloed and swivel chair management dominates. Alert noise is still high and triage and troubleshooting is still time-consuming.

Action: Expand on the data collected and analytical features supplied by these solutions. 57% report they want more unified alerting (centralized alerting) while 56% report more event correlation is needed. Also consider supplementing SNMP metrics and monitoring configuration changes with streaming telemetry. 

Step 3 Modern (Proactive)

In stage 3, network alert noise is moderate, virtual and software-defined technologies are monitored now along with your traditional infrastructure but correlating underlay and overlay network data to evaluate the performance of your software-defined deployments is still in its infancy. 

Action: Embrace AI-driven network observability solutions that have domain expertise in public cloud networks, WAN overlays, WAN underlays, Wi-Fi, and data center fabrics. 

Step 4 Next-Gen (predictive)

In this stage, you are collecting data across on-prem and public network infrastructure for end-to-end triage of network experiences, false alerts are rare and advanced analytics (AI/ML) is enabling predictive management with baselining, and anomaly detection.

Action: Synthetics and web testing extends visibility into public networks and an overall broader collection of data to enable proactive monitoring. Adopt telemetry features to stream real-time events into a centralized event mgmt/analytics/reporting and automated workflows for traffic engineering, troubleshooting and network performance optimization.

Step 5 Future (automated)

Now you have full visibility across private and public networks to understand network performance at every hop in the end-to-end network path, advanced analytics for alarm noise reduction, configuration management and synthetic testing to evaluate the resilience of your network and public cloud and ISP networks, you are still manually troubleshooting any network issue that may occur.

Action: Network configuration roll backs to known good state, enriching alarms with powerful data that leads to root cause with minimal bread crumbs or automated escalation of issues to level 2 or level 3 engineers and architects.

Full network automation will become a reality soon and it's definitely moving in the right direction. 

A mature network observability practice will not only reduce outages and improve triage times, but will protect company revenue and their brand. Most importantly, all of this is needed to be successful in any AI initiative you plan to tackle.

Everyone is talking about AI today. They talk about GPUs and power and storage but no one is talking about the network that delivers the data for AI to be successful.

You wouldn’t drive a Ferrari on a dirt road, would you? You would build a super highway to unleash the true performance for why that Ferrari was built. Do the same for your networks and I promise you they will be AI-ready in no time.