Blog AI & ML

How Network Automation Can Move AI from Science Fiction to Reality

Greg Castellucci Senior Manager, Systems Engineering Published 30 Oct 2017

Artificial intelligence (AI) has become a buzzword in the federal space, and what once was realized only in sci-fi movies, is now a burgeoning reality in IT processes. In fact, just last year, the White House encouraged federal agencies to explore all of the possibilities AI could offer, and the General Services Administration (GSA) launched programs to enable federal adoption of AI.

Furthermore, a recent Deloitte study found that AI could potentially save government 1.2 billion hours and $41.1 billion annually and increase mission-delivery speed by automating processes. However, before government can take advantage of advancements like AI today, agencies must take a few key steps. One area where Extreme Networks has implemented fundamental changes to make way for AI technology is in the network. Below I will explore how agencies can begin to evolve their network technology in order to leverage AI capabilities in the near future:


Network automation is a meaningful step towards AI that can provide enhanced mission delivery today. By leveraging automation capabilities within the network, immediate efficiencies can be realized. Automated processes give IT professionals back the time needed to proactively focus on efforts like improving cybersecurity and mission deliverables, rather than on day-to-day ”break-fix” events. Network automation improves operational efficiency across the entire enterprise and can address current IT maintenance spending concerns that constrain most budgets.

Internet of Things (IoT) orchestration solutions like Extreme Networks Workflow Composer can help facilitate these automation efforts. With EWC, agencies can automate the entire network lifecycle by integrating workflows across multiple IT domains for end-to-end automation. This solution allows agencies to improve their IT operations and drive greater business agility. What used to require any number of server, storage and network administrators to provision and troubleshoot services can now be orchestrated with tools, like EWC, using programmatic languages that can utilize Application Programmatic Interfaces (API) to effect changes based on pre-built workflows that trigger on specific events without human intervention. These automation capabilities are the precursor to artificial intelligence across the enterprise. 

Today’s networks are required to support a much higher volume of data than ever before. IoT and cloud-focused digital transformation are pushing the limits of today’s networks. With so many unique data sets, automation could be the difference between network outages and network connectivity.

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As volume, velocity, and variety of data in the network expands, comprehensive visibility into the operational state and the type of traffic within the network becomes critical. Pervasive network visibility allows agencies to quickly identify problems, accelerate mean-time-to-remediation and improve overall service levels.

Visibility into the network is also necessary to enable more intelligent automation. For automation to be “intelligent,” workflows must be strategically generated based on an agency’s unique set of needs. Automation shouldn’t be approached from a one-size-fits-all perspective. Rather, visibility into common issues and processes will ensure that automation is tailored to common agency events and is therefore efficient in nature and applied to functions that are the most cost-effective. 

Machine Learning

With exponential data growth expected to increase year over year, it is essential that federal agencies use what they have to their advantage. This is where not just automation and visibility become necessary, but also where machine learning (ML) comes into play. Leveraging ML is a step that agencies can take so that their IT can learn and adapt accordingly. Through ML, agency IT has the ability to keep a record of and recognize different types of network events, such as failure, congestion, various security anomalies, and other network problems, and then create models to forecast where to apply resources or other actions. By taking advantage of advances in ML, agencies can automatically plot and define these recurring events in real time, better understand connections between them, and to some extent, predict what event will happen next. This form of knowledge allows agencies to build upon the previous steps for an even deeper level of “intelligent” IT automation— the cornerstone of getting to AI today and deploying it across the entire enterprise tomorrow.

While enhanced network automation, visibility and machine learning may not have the same reputation as buzzwords like artificial intelligence, it is something that agencies can take advantage of today, without hesitation. Through identifying strategic areas in the network enterprise where automation and visibility can be injected, agencies can begin cutting sustainment costs and create opportunities to implement administrative efficiencies as they work to meet their mission. It is critical that government invests in network enhancing solutions today, so that in the future of AI, it can actually realize the benefits predicted.

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