Latin America. Rubén Sánchez, LATAM Regional Director of Extreme Networks, highlights that the consultancy IDC estimates that by 2020, the Artificial Intelligence market will obtain a value of $ 47,000 million dollars globally. AI has become a buzzword, and what was once done only in sci-fi movies, is now a flourishing reality in IT processes. In fact, last year, the White House encouraged federal agencies to explore all the possibilities AI could offer, and the General Services Administration (GSA) launched programs to enable federal adoption of AI.
In addition, a recent Deloitte study found that AI could save the U.S. government 1.2 billion hours, $41.1 billion annually and increase mission delivery speed by automating processes. However, before the government can take advantage of advances like AI today, agencies need to take some key steps. One area where Extreme Networks has implemented fundamental changes to make way for AI technology is the network. Below, I'll explore how organizations can begin to develop their network technology to leverage AI capabilities in the near future:
Automation
Network automation is a significant step towards Artificial Intelligence that can provide better mission delivery today. By leveraging automation capabilities within the network, immediate efficiencies can be achieved. Automated processes give IT professionals the time needed to proactively focus on other key efforts such as improving cybersecurity and mission deliverables, rather than focusing on everyday "fault fix" events. Network automation improves operational efficiency across the enterprise and can address current concerns about IT maintenance expense that constrain most budgets.
Internet of Things (IoT) orchestration solutions like Extreme Networks Workflow Composer can help facilitate these automation efforts. With EWC, organizations can automate the entire network lifecycle by integrating workflows across multiple IT domains for end-to-end automation. This solution allows you to improve your IT operations and drive greater business agility. What used to require any number of servers, storage, and network administrators to provision and troubleshoot services can now be orchestrated with tools, such as EWC, using programmatic languages that can use programmatic application interfaces (APIs) to effect changes based on pre-built workflows that trigger specific events without human intervention. These automation capabilities are the forerunner of Ai across the enterprise.
Today's networks require a much larger volume of data than ever before. IoT and cloud-centric digital transformation are pushing the boundaries of today's networks. With so many unique data sets, automation could be the difference between network outages and network connectivity.
Visibility
As the volume, speed, and variety of data on the network expands, comprehensive visibility into the operational status and type of traffic within the network becomes critical. Widespread network visibility enables organizations to quickly identify issues, accelerate mean time to remediation, and improve overall service levels.
Visibility into the network is also needed to enable smarter automation. For automation to be "intelligent," workflows must be strategically generated based on an organization's unique set of needs. Automation should not be approached from a one-size-fits-all perspective. Conversely, visibility into common issues and processes will ensure that automation adapts to common organizational events and is therefore efficient in nature and applies to functions that are most cost-effective.
Machine learning
With exponential data growth expected to increase year after year, it is essential that organizations use what they have to their advantage. This is where not only automation and visibility are needed, but also where machine learning (ML) comes into play. Leveraging ML is a step organizations can take so that their IT can learn and adapt accordingly. Through ML, IT has the ability to keep track of and recognize different types of network events, such as failures, congestions, various security anomalies, and other issues, and then create models to forecast where to apply resources or other actions. By leveraging advances in ML, you can automatically plot and define these recurring events in real time, better understand the connections between them, and to some extent predict which event will occur next. This form of knowledge allows us to build on the previous steps for an even deeper level of "intelligent" IT automation: the cornerstone of reaching AI today and deploying it across the enterprise tomorrow.
While enhanced network automation, visibility, and machine learning may not have the same reputation as buzzwords like AI, it's something organizations can take advantage of today, without hesitation. By identifying strategic areas in the enterprise where automation and visibility can be implemented, organizations can begin to reduce maintenance costs and create opportunities to install administrative efficiencies as they work to fulfill their mission. It is essential that all types of companies invest today in solutions to improve the network, so that in the future, they can actually reap the intended benefits of Artificial Intelligence.
