Using AI and ML to Help Keep the Hyper-distributed Workforce Productive

In the last few months, workers across the globe have moved from a traditional office setting into the home. This has created new challenges for businesses and placed new strains on the networks that support them. IT organizations are now working to adapt to the new reality of the hyper-distributed workforce and they’re seeking solutions that can improve how they manage connectivity in this new environment.

Artificial intelligence (AI) and machine learning (ML) technologies have brought several benefits to the traditional office, ranging from reduced help desk tickets to more rapid troubleshooting to successful support of service level agreements (SLAs). But how can we leverage these technologies to improve network manageability and enhance the end-user experience when data is more fragmented and employees are more spread out than ever before?

In an upcoming panel, our experts will explore the impact of this new era on IT, businesses and employees. Phal Nanda, RUCKUS VP, Cloud Services, Rajesh Pazhyannur, RUCKUS CTO and Alec Pinkham, Director of Product Marketing at AppNeta will lead a discussion on this timely topic at the Fall 2020 ONUG Virtual Conference.

The Work-from-Home Challenge

Not only has the enterprise shifted from a distributed to a hyper-distributed environment, but the traffic on the network has undergone a significant change, too. Employees are working from home for extended periods of time – not just at night and on weekends – and using more bandwidth for Zoom meetings and Teams calls than ever before. They’re also competing with the bandwidth requirements of virtual learning and other online activities. While the demands on the home network may have changed, the role of IT has not. IT admins are still on the hook to ensure employees can be productive – no matter where they’re located.

Aligning Technologies with the New Workplace

IT teams have historically had less control over the home network than the office network, for obvious reasons. But that’s changing. New approaches are extending feature-rich enterprise networking to the home and improving productivity for remote workers. Fundamental to these approaches is the use of AI and ML to give IT the insight it needs to effectively aid the remote employee. Capabilities like incident analytics, automated health monitoring and advanced troubleshooting give IT more visibility and, therefore, more control. Let’s look at some ways this type of intelligence helps IT continue to keep employees well connected and productive at home, as well as when they sporadically visit the office.

Proactive Troubleshooting and Remediation:

AI- and ML-enabled tools provide IT with an early warning system, identifying network performance issues and prioritizing them, establishing root causes and recommending the best path to remediation. Because these systems learn continuously, early warnings come earlier and root cause analysis becomes more accurate with time, enabling IT to become effortlessly more efficient.

Enhanced Quality of Experience:

Comprehensive network intelligence helps IT deliver on network SLAs, ensuring mission-critical applications and customer-facing services get the required network support to run at peak performance. Employees can do their jobs with no delays or disruptions for optimal quality of experience (QoE).

Optimized Connectivity:

In the new hyper-distributed model, there may be five people in the office one day and 50 the next. IT needs the flexibility to increase and decrease bandwidth and resource allocation at will. With the intelligence modern analytics can provide, IT staff can predict when and where bandwidth is needed, making it easier to prioritize based on actual capacity needs.

Get More Insight

Join us on September 9, 2020 for our ONUG panel on “Using AI and ML to Improve Networking in the New Work Reality,” moderated by Zeus Kerravala. We’ll discuss the technologies that can best support IT, employees and businesses in the new work environment and we’ll address further topics:

  • With more personal devices being used to conduct business, do businesses need to revisit the security mechanisms that are in place on endpoints and at the edge? Will it be more difficult for IT to adhere to security regulations? For example, how can remote workers be securely connected to corporate data centers without losing control over proprietary information?
  • When IT manages network equipment in the home, they end up having access to traffic that’s not work-related. What does this mean for employees from a privacy standpoint?
  • Advanced Network Capabilities. Can analytics and device location history be used to establish certain geographical boundaries and help with contact tracing? If we give our ecosystem of partners the tools to derive location intelligence, can they integrate vertical market logic and devise new IoT solutions?

 

 

Author's Bio

Phal  Nanda

Phal Nanda

RUCKUS VP of Cloud Services, CommScope