To run at AI speed, networks need to adapt

March 30, 2018 Mike Frane

Welcome to the age of AI. It’s the dawn of an era that will change everything, enabling amazing advances in science, medicine, business, and life itself.

Yes, you’ve likely read this same sentence, in one form or another, for the last 20 years. For nearly as long as we’ve had computing, there have been periods of AI hype mixed with progress, followed by … What happened? But this time, consider that in the past few years we’ve experienced:

  • Explosive Internet growth
  • Quantum leaps in computing power
  • Corresponding advances in big data
  • The emergence of technology powerhouses like Google, Facebook, and Amazon
  • Mainstream recognition that data matters

In addition, the computing industry is developing a roadmap to address AI challenges relating to education and talent, ethical concerns, overall digital momentum, and the drive to apply AI and its sibling, machine learning, towards innovation in the customer experience.

Enterprises are aligned with AI

Optimism among business and IT leaders regarding AI and machine learning and their impact on digital transformation is stronger than ever. The Accenture Technology Vision 2016 survey of 3,100 business/IT execs in 11 countries found that 70% of organizations are investing significantly more in AI compared to three years earlier. In a recent Infosys poll of 1,600 senior business decision-makers, 76% said that AI is fundamental to the success of their organization’s strategy.

What’s driving these trends is that to compete in the cloud economy (and with the likes of the tech powers mentioned above), companies must deliver a customer experience (CX) that transcends channels and is fast, reliable, personalized, mobile, seamless, and secure. This demand reaches into virtually every industry with research by a myriad of analysts reporting a vast majority of organizations believe that CX will be their primary basis for competition in the next few years.

A looming bottleneck

Improving the customer experience for competitive advantage requires learning from oceans of data on the back end, while providing a seamless customer experience up front (something we’re doing ourselves to drive our own CX). All of this adds tremendous stress to the network, with specific implications regarding performance, reliability,  bandwidth, security, resiliency, visibility, and control.

And it’s only going to get worse, with a new generation of bandwidth-hungry customer/user experience-enhancing technologies and apps (AR, VR, etc.) about to crash the network party. When it comes to supporting enterprise AI with network infrastructure, it’s like when Chief Brody said to Captain Quint after his first up-close look at the Shark in Jaws: “You’re gonna need a bigger boat.”

When it comes to AI and enterprise networks, “you’re gonna need a bigger boat.”
JawsTM image ©Universal Studios

The essential problem is that traditional networks were developed for a vanishing enterprise technology landscape. Left unaddressed, this will at best lead to annoying bottlenecks. At worst, it could bring a swift end to AI and IT digital transformation initiatives that overpromised and under-delivered.

To run at AI speed, networks need to adapt

To deliver the promise of Machine Learning AI, networks must enable vast amounts of data to be instantaneously gathered, transferred to the cloud, analyzed, retrieved, and then applied wherever work is to be accomplished. All in a blink of the eye. This presents substantial challenges, as the solution may fail if the data is inaccurate or incomplete, or delayed.

This will require a new type of network infrastructure that provides:

  • Operational efficiency, with high value at lower total cost of ownership (TCO) than traditional networking
  • Exceptional performance, with high availability, redundancy and flexible bandwidth
  • New levels of visibility and control, including granular application QoS
  • Unprecedented agility and including analytics

In other words, it sounds like a job for SD-WAN.

This is why the growth profile and maturity/adoption curve for SD-WAN – which IDC estimates will see a compound annual growth rate of 69.6% and become an $8.05 billion market 2021.

WE’s SD-WAN architecture is designed to deliver the cloud performance and reliability that applying AI to CX in real time demands

Is your network AI ready?

If you have not already done so, it’s time to begin preparing your enterprise network for AI. The starting point is to answer four key questions:

  1. Is your network prepared to meet projected bandwidth needs in the next year/five years?
  2. Will it meet them across all locations?
  3. Do you have the access diversity to guarantee uptime needed for AI applications?
  4. Is your network optimized to prioritize these apps for the cloud?

These are tough ones to answer for a lot of organizations. To make sure you address them properly, and to be sure your network is ready for the data tsunami that will accompany the artificial intelligence era, it is essential that you step up your investigation soon. SD-WAN is a great place to start. A conversation with a cloud/AI ready network provider might be even better.

The post AI Spotlight: Artificial Intelligence Will Transform Everything, Including Your Network appeared first on Windstream Enterprise.


About the Author

Mike Frane

Mike Frane is the Vice President of Product Management for SD-WAN at Windstream Enterprise, where he is responsible for the company’s overall SD-WAN strategy, as well as the network and security service portfolios. Since joining the organization in 2008, he’s overseen the launch and lifecycle of services including LTE wireless, Ethernet and MPLS IPsec access elements, Secure WiFi & Analytics, Application Performance Optimization, IPsec VPN and Unified Communications. Prior to Windstream’s acquisition of EarthLink, Mike led the launch of EarthLink’s SD-WAN service; their most successful product introduction in over a decade. Mike has a BS in Genetics and Cellular Biology from the University of Minnesota and was involved in gene therapy research at the Institute of Human Genetics before entering the telecommunications industry.

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