At every tech conference I attended last year, AI was on everyone’s lips. From synthetic data to chatbots, intelligent automation to anomaly detection, AI was a magnetic force pulling the attention of executives, engineers, and marketers alike. Yet for all the chatter, few had a tangible plan – it didn’t feel like anything more than a “buzzword” topic at the time.
This year, however, the mood has shifted seismically. This buzzword has transformed into boardroom mandates. CIOs are now forced to ask hard questions – not what AI can do, but rather “how can we use it, and what can we do to support it?” The answer to that question increasingly points back to one critical issue: the network.
Gartner recently reported that nearly 40% of agentic AI projects will fail by 2027. If AI is to become the brain of the modern enterprise, the organization’s network is the nervous system – and it is very underprepared.
The Illusion of Readiness
Everyone likes to think that they’re ready for what’s next. However, recent research tells us that many organizations are not as “future-proof” as they’d like to believe. The truth is that it is now more complex than ever to manage organizational networks and the network delivery experience, as noted by 77% of respondents to a recent survey by EMA.
This is due to a litany of factors, the first of which is a shift away from centralized data centers to distributed cloud and hybrid work environments. These environments have a significant impact on the demands that are placed on modern network operations, and when they are implemented, the network perimeter effectively dissolves, ultimately encompassing vast amounts of employee devices and home networks.
Adopting new technologies, such as software-defined wide area networks (SD-WAN) and network function virtualization (NFV), provides many advantages to the businesses that invest in them, but they also add another layer of complexity and more strain on the network.
Additionally, popular toolsets are leaving teams ill-equipped to remediate these issues. Traditional monitoring tools often lack the ability to effectively monitor cloud and internet environments, which leaves teams with limited visibility and control. These hurdles can lead to long triage times and slower issue resolution.
Given that many modern enterprise networks are mired in thousands of complex layers, many different kinds of tools, and distributed networks all demanding vast amounts of bandwidth, implementing AI before the organization’s network is ready will only exacerbate these issues. Creating an AI-ready network requires mature network observability practices. Here are some steps for creating this kind of network:
The Components of a Mature Network
- Gaining Visibility into Third-Party Environments: Teams need to be able to address every component of their network to quickly and easily isolate issues, no matter if they’re occurring on the organization’s networks or ones run by third parties. A mature network observability solution should provide the organization with extended, inside-out visibility into third-party cloud and ISP networks. Modern data collection methods enable organizations to gather real-time insights into end-to-end network performance – an absolute imperative when adopting AI tools.
- Correlated and Scalable SD-WAN Visibility: Mature network observability practices should be able to easily support hundreds of thousands of encrypted tunnels that SD-WAN can produce, while correlating multi-vendor underlay metrics, while providing visibility beyond the internal network and into third-party networks.
- Advanced Intelligence and Analytics:
A network observability practice that can be considered mature must employ algorithmic capabilities such as event correlation, root cause analysis, alarm suppression, volatility analytics, and predictive capacity planning. Instead of causing large amounts of event and alarm noise, they can quickly reveal which process, device, or user is responsible for an issue, allowing the situation to be remediated immediately. These can also help network operations teams adapt to future requirements.
- Active Synthetic Monitoring:
Given the nature of hybrid work and its increasing prevalence, real-time data and proactive monitoring are necessities, as employees work from various locations and networks, ultimately making troubleshooting far more complex. Passive monitoring in itself lacks the capabilities to address the complexities of the modern network.
- Streamlined Operations:
Removing the complexity of managing network operations is another key component of a mature network observability practice. This offers teams focused and easy-to-understand remediation recommendations or automated resolutions without straining the organization’s budget.
The Maturity Model & AI Readiness:
Modern networks pose significant challenges that require new observability techniques. The three primary goals are to optimize network operations, create visibility across the organization, and build capacity for active monitoring. Achieving these goals is based on your level of network observability maturity, which is also crucial for evaluating your readiness for AI. This section will now cover each stage of the maturity model and the corresponding level of AI readiness.
1. Manual:
This stage is characterized by the involvement of solutions from many different vendors that typically only offer coverage of a specific system or technology domain. By nature, these solutions are siloed and lack comprehensive metric collection and don’t integrate with each other, preventing an overall view of the health of the infrastructure.
This mixed bag of tools provides some insights, but also introduces data conflicts, false alarms, and slows triage and root cause analysis of issues.
If an organization’s network falls into this first stage, the AI initiative is more likely to fail. Since AI places a large burden on network infrastructure, the organization must have the necessary tools in place to monitor network health, detect and remediate issues quickly, and collect relevant data. Without these capabilities, AI initiatives can overwork the network, leading to outages, poor user experience, and a lack of ROI.
2. Traditional:
At this point, most teams have achieved full network visibility across alarms, events, inventory, fault, performance, flows, logs, and configuration management for their internally managed networks. Yet they still rely on some siloed monitoring technologies, like vendor-specific tools. These siloed tools collect some important metrics that can be correlated, but other tool sets still collect important network data that can only be accessed via separate controls and teams.
Although this is a step in the right direction, teams still have to contend with long triage times and “blame games” that can result in poor customer experience.
Like the Manual stage, organizations in the Traditional phase are not ready for AI. The continued presence of siloed tools and fragmented visibility means AI would not have the comprehensive, real-time data it needs to perform effectively. Implementing AI at this stage could result in poor outcomes, straining the network and diminishing the potential return on investment.
3. Modern:
In this phase, teams have consolidated a few primary tools with some level of integration. This allows some correlation of data, helping teams isolate the root cause of the issue. Some also add web testing and synthetic network monitoring to their traditional passive methods to extend their visibility into business-critical applications and third-party networks. When complete visibility into internal and external networks is achieved, teams can gain end-to-end coverage of network and application delivery paths.
This level of maturity is denoted by evolving from a reactionary to a proactive state, wherein instead of reacting to metrics, they proactively analyze issues. This is possible in the lower tiers, but less mature network operations teams lack application context and visibility into external networks. The addition of more modern data collection methods that correspond with this stage also paves the way for AI-driven analytics.
The third stage of maturity is where organizations can begin to implement AI tools. The increased capacity for data correlation and the additions of web testing and synthetic monitoring help extend visibility into the third-party networks and business-critical applications that AI tools will interact with. Although AI tools can be introduced here, organizations are still not mature enough for widespread or transformative AI adoption — but the foundation is being laid.
4. Next-Gen:
This next-generation stage begins with the ability to automatically stream comprehensive, end-to-end network metrics into a centralized analytics platform. This system utilizes a central data lake with AI capabilities to correlate and analyze data from the entire network, including office, home, data center, and SaaS environments. Thanks to these advanced analytics, most alerts become actionable and provide clear direction. In addition, features like anomaly detection, root cause analysis, and performance baselining all lead to faster insights and quicker issue resolution.
This level is the first step into achieving fully predictive network observability and management. In this, metrics are layered into advanced traffic baselining, engineering, anomaly detection, and software-defined technologies.
In this stage, metrics are autonomously streamed into a unified analytics and reporting system, encompassing all areas of the network. The presence of a central data lake and correlation capabilities are critical for mass AI adoption. Organizations at this maturity level can begin to see meaningful ROI from broader AI initiatives, as AI tools now have the consistent, high-quality data streams they require to function effectively and proactively.
5. Future:
This stage represents the future of automated networks by building on AI-driven insights to add intelligent alerting, external event correlation, and automated remediation. However, due to a residual distrust of full automation, this phase still relies on closed-loop, human-driven intervention. The implementation of these technologies fundamentally changes how network teams operate, shifting their role from musicians to conductors of an orchestra. This new capability allows teams to work together to ensure optimal user experience and overall network performance.
The hallmark of this level is characterized by automating network functions as much as possible, with human supervision or intervention only when necessary. Through using predictive insights, baselining data, and other inputs, networks can change dynamically.
When the network fully enters the Future state, network functions are as automated as possible, building on earlier AI integrations. Intelligent automation, predictive insights, and dynamic self-correction characterize this level. With full visibility and control, organizations can realize the full ROI from AI across their network operations. Human oversight remains, but AI becomes the engine behind resilient, responsive, and scalable network performance.
Getting Your House in Order:
Understanding the different stages of maturity is crucial when deciding if your network is ready to adopt AI. If adopted too early, AI initiatives can fail, overworking the network, causing issues ranging from network outages to poor user experience and performance, ultimately tanking the ROI of an initiative. However, if adopted at the right time, AI can provide a host of benefits that will position the organization to be more competitive and efficient than ever before.
Every organization may find itself within a different stage of the maturity model, or in between multiple. No matter what stage your organization may be in, it is crucial to have your house in order before undertaking one of these AI initiatives. If it’s rushed, it can be very detrimental to your organization’s network, finances, and potentially even your customer base. However, when done correctly, wherein all of the necessary supporting infrastructure is in place, it can unlock a whole host of benefits and opportunities.
