How to Enhance Productivity Through AI-Driven Network Automation

Today’s WiFi and wired networks are complex. They consist of hundreds or thousands of devices and applications, many of which are data intensive, and must support a mobile workforce. Enterprises who want to support business continuity have to be able to troubleshoot and optimize networks in real time, scale effectively, and adapt cost-efficiently.

AI-driven network automation offers global leaders a powerful solution to meet all these needs. Let’s explore how these solutions streamline operations, reduce costs, and drive greater, reliable productivity.

Understanding AI-Driven Network Automation

AI-powered network automation solutions automatically and proactively detect, identify, and mitigate wired and WiFi network issues.

These vendor-agnostic solutions complement vendor management software suites because they provide the final pieces needed for true reliability and optimization: end user digital experience metrics, and real-time and historical analytics for future-proofed network assurance.

Key Benefits of AI-Driven Network Automation

1. Improved Efficiency and Reduced Downtime

The AI-powered engines of these solutions learn to recognize the unique behavior of every network they analyze. Thanks to this understanding, solutions can then alert IT to both existing and potential issues as soon as they occur. 

Depending on the solution, alerts can include root cause identification and actionable resolutions. These details empower IT to resolve problems faster than ever, significantly reducing network downtime and creating more resilient networks overall.

2. Enhanced Scalability

Solutions analyze data packets from every connected device in real time. They will continue to do so, no matter how many devices connect to the network. With this support, enterprises can adopt the technologies they need to remain competitive, without worrying about overloading IT professionals.

3. Better Decision Making

AI-driven network automation solutions deliver actionable analytical insights. As long as they provide both immediate and historical analytics, they will identify both real time and long-term patterns and trends in network behavior and performance. With this knowledge, decision makers can better understand which network elements need upgrading, which do not, and what specific new technologies should be adopted to meet an enterprise’s goals.

This better decision making also contributes to greater cost-efficiency, the next benefit on our list.

4. Cost Savings

Along with the identification of unique current and future network needs, these solutions deliver additional cost savings thanks to the AI-powered insights driving predictive maintenance. 

With these insights, enterprises experience lower operational costs and a reduced need for IT intervention because networks are more resilient and future-proofed.

Use Cases for AI-Driven Network Automation

Here are two ways that global enterprises are using AI-driven network automation today.

1. Automated Network Monitoring

Network ecosystems are dynamic. There is no such thing as optimizing a network, walking away from it, and expecting it to remain optimized. Instead, these ecosystems require constant, 24/7, oversight. At least, they require that for enterprises that want to identify issues in real time and eliminate problems while they are small in order to preserve the digital experience and business continuity. If that isn’t of importance, then 24/7 oversight isn’t needed.

For enterprises that do prioritize the digital experience, IT professionals on their own cannot provide the network with this constant attention. Not only can they not work, uninterrupted, 24 hours a day, but they also cannot analyze tens of thousands of data packets a second.

No, for this intensive network analysis, enterprises need AI-driven network automation.

Once connected to the network, these solutions immediately begin analyzing all connected devices, backend and frontend infrastructure, and applications. They work nonstop, even while IT professionals must be offsite. They automatically save records of all issues, and alert IT to the particulars in real time, successfully eliminating network mysteries.

2. Predictive Maintenance and Issue Resolution

An AI-driven solution that analyzes network behavior is important. However, an AI-driven solution that goes further, that promotes predictive maintenance, and proactive troubleshooting and issue resolution, is critical to long-term success and maintaining a competitive edge.

Without this support, an enterprise’s network is beholden to employees to notice something is wrong. While IT professionals will of course analyze network performance and run network tests, these tests do not cover every minutiae of network performance as experienced by employees performing their daily tasks. For that, automated end user experience metrics are needed. When it is employees and not AI-solutions and network experts experiencing issues, the resolution process looks like this:

1. Employees alert IT – sometimes right away, often not – that there is a problem. 
2. IT teams must then review analytics in order to determine the root cause of the issue. 
3. If the problem is no longer occurring, they must wait until it recurs. 
4. Only after the root cause is identified can a resolution be implemented.

This is time consuming and costly. If the issue goes unnoticed for a while, what was a small problem can become an expensive headache.

However, with AI-driven network automation, issues are noticed in real time, and can sometimes be identified while they are still only potential problems. Within the alerts, IT teams are given everything they need to implement resolutions immediately. This results in streamlined operations, and a protected digital experience for employees who often don’t even know there was an issue.

Considerations Before Implementing AI-Driven Network Automation

1. Assessing Infrastructure Needs

Every enterprise that depends on a wired and WiFi network needs AI-driven network automation. Assess your infrastructure to determine how many solutions you need. For example, one Wyebot sensor can analyze ~10,000 sq ft. With this knowledge, how many sensors would your infrastructure require?

2. Integration with Existing Systems

Before adopting a solution, determine how easily it will integrate with your existing systems. Wyebot’s Wireless Intelligence Platform (WIP) is vendor agnostic in order to simplify the integration process and provide continuous support no matter how other systems may change over time.

3. Training

How extensive of a training program is required before IT professionals can use the AI-driven network automation solution? Is the training expensive? This will not only determine how long it takes to begin using the solution, but also how any of your IT professionals can be trained to support it.

WIP is a plug-and-play solution that begins returning insights minutes after it’s connected to the network. It’s described as a network-assistant-in-a-box, and supports professionals even if they are not WiFi experts.

4. Vendor Support

Determine how readily available the vendor of the solution is in the case of any questions. Is support ongoing or does it end at any point? How are support teams reached?

Many of Wyebot’s customers highlight how knowledgeable our support team is and how easy they are to work with if customers have any questions.

Your Network is the Foundation of Your Work – Support It

WiFi and wired networks are the bedrock of today’s global enterprises. If they aren’t working optimally, reliably, and securely, every department feels the resulting negative impact. Adopt an AI-driven network automation solution today and be sure your network is always a strategic asset and not an expensive problem.

Wyebot is the leader in AI-driven WiFi Automation. Our sensor, cloud, and client device technologies eliminate wired and WiFi network issues and improve the digital experience for every user, independent of location. Our award-winning, first-to-market, innovative solutions are changing people’s lives in the enterprise, healthcare, manufacturing + warehousing, MSP, retail, and education industries.