If you’re finding it a little difficult to sift through all the varying, often conflicting information surrounding the topic of network observability, you’re certainly not alone.

As a seasoned IT professional and marketer, I can admit that network observability is definitely a buzzword being thrown around by many IT vendors these days. But with the evolving nature of networking and network operations, we definitely need more than just traditional network monitoring. These legacy approaches are simply not equipped to address the gaps in operational visibility that are introduced by work-from-anywhere models, cloud migrations, and SaaS adoption.

Further, just when you thought your day-to-day network operations were challenging enough, AI-ready networks are becoming a cornerstone for innovation. Now, executive teams are tasking IT with getting their infrastructure ready for yet another disruptive technology.

To do effective network operations triage, teams must harness intelligence from alarms, faults, network flows, performance data, network configurations, DNS, and logs. Further, they need to be able to leverage this data from networks they manage and those of ISPs, cloud providers, and more. It is only with this complete operational visibility that teams can make quick, intelligent triage decisions and reconcile root causes faster.

Imagine a world where networks are not just reactive but proactive, adapting to the needs of businesses in real time. This is where a Network Observability Maturity Model is useful, guiding organizations on their journey to harness the full potential of AI.

A maturity model framework provides a structured approach to evaluate and enhance the capabilities of networks to support AI applications. It outlines stages of development, from basic connectivity to advanced, self-optimizing networks.

Here are the key components of AI-ready networks:

  • Scalability: Networks must be able to grow and adapt without compromising performance.
  • Security: Protecting data integrity and privacy is paramount.
  • Automation: Leveraging AI to automate routine tasks and optimize network performance.
  • Analytics: Utilizing data insights to guide decision-making and innovation.

Organizations must assess their current network capabilities and identify gaps. "It's about understanding where you are and where you need to be," said one leading network strategist. The framework provides a clear path forward, with actionable steps to achieve AI-readiness.

In conclusion, AI will demand near-perfect performance from network infrastructures. Consequently, building AI-ready networks means advancing your network observability practice, so you can move from good-enough network performance to resilient network experiences.