IT network administrators beware: AI is not a silver bullet

If there’s one constant in the tech world, it’s the constant tug-of-war between hype and reality. I’ve seen this unfold with every new “transformative” technology. With the advent of artificial intelligence (AI), the era has returned to the future, asking how this promising advancement will change network management.

In theory, AI should be a game changer. Network teams will be able to identify problems in real time and preempt potential problem spots before they become critical. The same is true for tracking traffic patterns and managing network performance. The result is better utilization of network capacity, fewer support calls, and higher user satisfaction.

But before jumping in, network managers should take a closer look at what the move to AI really means and try to separate the hype from reality.

Assess your infrastructure

Increased complexity and device proliferation at record rates make the network administrator’s job very difficult. As IT budgets continue to shrink and organizations seek to reduce network support spending, IT departments are stretched to their limits and operating at dangerously thin levels.

Here, the network team can use AI to dig holes.

Problems can be repaired and resolved more quickly, reducing network downtime and improving network performance while reducing overall IT costs. It also helps you deliver a better experience to your customers with fewer support calls and complaints.

Here’s a real-world example of how the industry can support it. By incorporating AI into their networking solutions, technology providers can take snapshots of their entire network when a customer reports a problem and run that data through a learning engine to figure out what happened. can be produced.

By using an AI/ML engine that learns from problems that occur in other customer networks, problems that occur once are not repeated elsewhere. This saves a lot of time as problems can occur anywhere. The glitch may be related to the software load of the access point. Or maybe it’s in your support network. But with the help of AI, organizations are now able to see in detail what’s going on in a fraction of the time it used to take to troubleshoot issues.

Unlock big data

AI is especially useful in parsing the vast amount of client telemetry generated by network infrastructure. In the past, the only way to extract information from all this data was with the help of (highly paid) professionals familiar with various network technologies. But if companies don’t have the right talent, this treasure trove of valuable data will go largely untapped. This is especially amplified when customers deploy network solutions from different vendors, making it impossible to see a single view of the network.

With the help of AI tools, organizations can now solve this big data problem and get the insights they need to address issues facing IT departments, such as:

• Which sites and clients are experiencing poor network experiences?
• What are the root causes of poor performance?
• Which sites are running at full capacity and what network changes are needed to improve the situation?
• Can you continuously and automatically scan your network to maintain a good network security posture?
• Do IoT devices introduce security vulnerabilities?
• Will your network services function well during peak network times?

Don’t get caught up in the hype

There is no doubt that AI is ever increasing in relevance to network management. And as processing power increases, the technology will continue to improve. But be careful how you use it. Do not ignore the fact that AI should not be applied indiscriminately.

Some routine manual tasks are better automated. For example, you don’t need AI to publish network patches. That’s why I think we can’t and shouldn’t leave everything to AI. Deploying this kind of solution can be expensive.

Focus on your use case. What business problem are you trying to solve? This may seem elementary, but this basic question is often ignored.

Second, does it fit your economics? Every business must adhere to a budget. Be careful that AI deployment doesn’t make a lot of money.

Third, test to see if the deployed network really provides the desired results. Does it help solve business problems? How do you do it? And are you sure it’s working?

Choose practically, not based on hype

There are many tools in the world. Some are AI-powered, some are not. Don’t get caught up in the hype.Instead, be sure to select the one that solves your problem. Otherwise, your expenses will just skyrocket.

Above all, remember that this AI transformation won’t happen overnight. Throughout my career, I have watched this situation play out every few years as the market strikes the right balance between enthusiasm and over-confidence in new technology. All this is exciting, but plan your trip little by little.

As AI begins to gain more trust and more automation within the network, capabilities can be built accordingly. It’s a long journey, but perseverance is sure to pay off. Therefore, proceed step by step.

Rad Sethuraman is Vice President of Product Management at Cambium Networks..