Future of Network Automation with AIOPS 2024

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Future of Network Automation with AIOPS [2025]

Future of Network Automation with AIOPS [2025]

Future of Network Automation with AIOPS – If you work in IT, you’ve probably heard of AIOps, or artificial intelligence for IT operations. Gartner coined the term in 2017 to describe the process of managing data from an application environment using artificial intelligence. While the concept is simple, putting AIOps into practice is quite difficult.

AIOps combines machine learning (ML), behavioural ethics, and predictive analytics with massive amounts of telemetry data generated by network devices. There are numerous automation tools available that perform well in static environments. However, because modern environments are not static and are constantly changing, organisations require tools that can keep up with the changes.

AIOps significantly improves critical application performance. The ML system always monitors the critical apps. The network understands the traffic patterns and dynamically responds to those needs. With that said, let’s dig deeper into network automation with AIOps and how it aids Network Automation.

What Are AIOps?

As we know AIOps, or artificial intelligence for IT operations describes technological platforms and processes that enable IT teams to make faster, more accurate decisions and respond to network and system incidents.

In real-time or near-real-time, AIOps contextualises large volumes of telemetry and log data across an organization’s IT infrastructure. It then combines this information with relevant historical data to produce actionable insights. AIOps is the personification of an assistant with extensive knowledge of the IT and network environments, as well as the ability to use that knowledge to provide real-time analysis and execute or recommend next steps.

Why Is AIOps Important?

Individual applications and services benefit from AIOps’ increased efficiency and performance. AIOps-enabled organisations improve everything from security and outage incident response times to infrastructure purchases. Those who are just getting started with network automation and AIOps see it as an investment in performance analysis, anomaly detection, and event correlation, which allows them to predict future network-impacting events.

Benefits of AIOps

Traditional IT tools lack the intelligence and automation required to handle the dramatic increase in new services, remote users, Internet of Things (IoT) devices, cloud technologies, and data.

AIOps for network automation offers the following advantages:

  • Allows IT departments to respond to and prevent outages before they occur.
  • Improves IT efficiency by reducing Mean Time To Resolution (MTTR).
  • Detects and filters out noise so that IT operations do not waste time on low-priority issues.
  • Tips for improving network, security, and application expectations are provided.

What is the Future of Network Automation with AIOps?

In this digital age, organizations are attempting to make the best decisions for their digital initiatives in order to stay ahead of the competition. As a result, it is not surprising to see all of the remarkable advancements that the year 2020 holds for technologies like 5G, SD-WAN, Edge computing, Wifi-6, and so on. But we must remember that any technology is only as good as how it is managed.

While today’s enterprises priorities service assurance and network uptime, they are open to implementing network transformation to achieve these goals. That is why they are also the ones who can predict the next wave – the wave of Artificial Intelligence.

AIOps has sparked a lot of interest as the network genie for the next few years. NetOps powered by Artificial Intelligence is as self-explanatory as it gets. It is generating a lot of new and renewed interest in reorganizing how we manage our network infrastructure.

Automated service assurance is critical to the success of any organization’s customers. AIOps for network automation will support NetOps by automating manual tasks and speeding up innovation. AIOps, which are powered by workflow automation, service modelling, compliance capabilities, and closed-loop automation, can enable organisations to redefine network automation with a twist thanks to Artificial Intelligence. With one hand on ML and the other on Analytics, feedback measurement at each stage of automation is more accurate and contributes to further learning. AIOps will also pave the way for the much-discussed Intent-based networking.

Final Say

AIOps is much more than just another acronym, as you can see. It provides a new level of actionable intelligence, immunity, and business advantage for enterprises looking to keep their networks ahead of the curve.

It’s new, but it’s gaining traction. Adoption of AIOps for network automation may be slow, but we anticipate significant progress in the coming year. This shift will necessitate a cultural shift, both at the organizational and individual levels. Today’s networks are complex, and humans are struggling to keep up. There is a lot of room and need for technologies like AI to step up, as long as the right use-cases are identified.

At the same time, we must remember that any digital transformation exercise, as well as network and operational transformation, is a journey. AIOps for network automation is about to change the way you build, operate, and manage networks. Need help? We’re always here to assist.

The Future of Network Management with AIOps

The Future of Network Management with AIOps and machine learning for network automation.

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Network management strategy and software have evolved steadily over the course of the 21st century, but a newer development is hinting at a transformative future for network management: artificial intelligence for IT operations, or AIOps.

Some enterprises are still hesitant to adopt new AIOps technologies and initiatives. But as we’ve seen with the growth of AI and machine learning in other areas, AIOps is likely to grow quickly and become the standard consolidation method for the future of network management.

AIOps and the Future of Enterprise Management

  • What is AIOps?
    • AIOps vs. Digital Experience Monitoring

    What is AIOps?

    AIOps is the process of incorporating machine learning and big data analytics into network management in order to automate network monitoring, troubleshooting, and other network management goals. Some experts believe the term is a misnomer, as AIOps relies more heavily on machine learning actions than on artificial intelligence-powered human behaviors. The idea is not to replace manpower on the network, but rather to automate network processes so that network administrators can focus on more strategic network tasks.

    AIOps vs. Digital Experience Monitoring

    AIOps is growing at a rapid pace, and so is a similar network technology: digital experience monitoring (DEM). Although the two network management approaches are very similar, AIOps goes a step further than DEM. Digital experience monitoring focuses solely on combining application performance monitoring (APM) and end user experience monitoring (EUEM) to avoid data and performance silos. However, AIOps’s use of machine learning and data analytics not only assesses performance, but also automates fixes that would normally have to be handled by a network admin.

    Both tools are increasingly replacing other network technology. In a recent report, Gartner predicted that AIOps and digital experience monitoring tools were used as the exclusive network infrastructure tool for around 5% of enterprises, but they expect that number to grow to 30% of enterprises by 2023.

    Benefits of AIOps in the Enterprise Network

    AIOps offers several key benefits to the enterprise networks that willingly embrace the software and network management practice:

    Smart Data for Troubleshooting

    It automates network management and monitoring with more intelligent data via big data analytics. This in-depth data analysis and application is particularly helpful for optimized troubleshooting and network security needs.

    Improved User Experience

    Machine learning and automation helps AIOps software to detect network problems sooner, or perhaps before they become a problem. The speed and thoroughness of this technology improve the overall user experience with limited effort on the part of network admins.

    Strategic Growth for Network Administrators

    ML-powered automation in AIOps primarily eliminates the need to perform repetitive, time-consuming tasks. This automation frees up time for network administrators to focus on higher-value strategy needs.

    Avoiding Communication and Technology Silos

    AIOps allows enterprise networks to stream relevant analytics into one space for network monitoring and strategy. This consolidation approach eliminates some of the networking tool sprawl, where so many other network management tools are siloed and focused on one network task. Not only is it quicker, more efficient, and less expensive to consolidate, it also ensures that all teams and network tools have the same information when making changes to the network.

    Current AIOps Solutions

    Although AIOps is a fairly new field, several software vendors have jumped into the space and are offering comprehensive solutions to enterprises. Most solutions currently handle root cause analysis and problem prediction/solving on the network. Some of the top AIOps solutions currently on the market are:

    • Dynatrace
    • LogicMonitor
    • Splunk
    • ZIF
    • Moogsoft

    More AIOps Solutions to Look Out For: Top AIOps Tools & Platforms of 2021

    The Future of AIOps

    AIOps is quickly becoming a reality across global industries, but in many ways, AIOps has not truly arrived at the enterprise level. As AIOps becomes a more typical way for enterprises to manage their networks, organizations will need to consider these best practices and changes looming on the horizon for AIOps and the digital transformation that it ushers in.

    The Importance of Improving Data Quality

    AIOps tools are only as successful as the data quality that powers them to work. That’s why networks that are looking to move toward AIOps must also consider what tools and data analysis best practices they’ll need to make their data actionable.

    • Database management systems help organizations to manage huge quantities and types of big data, whether they’re working with unstructured, structured, quantitative, or qualitative data.
    • AIOps isn’t possible if ML-powered technology cannot read an enterprise’s data. Data annotation is the most important data step that enterprises must become familiar with and implement in order to make machine learning possible across their network.

    Improved data quality will be important to AIOps tools across the board, because the automation of so many network skills will use a larger amount of data that other tools may have previously overlooked or considered “useless” to operations. With stronger data practices, enterprise AIOps will be able to improve security practices like anomaly detection in the future.

    Integrated Security and Preparation for New Attacks

    As AIOps matures and provides stronger data insights over time, experts predict that enterprises will marry AIOps with other existing security software. Because AIOps provides both data visibility and real-time monitoring of how the network is being used, the integration of AIOps with other security tools can create more informed data segmentation and improve alerts when an inappropriate action is taken by a user on the network.

    But with machine learning-powered technology like AIOps also comes the possibility for new kinds of malicious network attacks. Adversarial machine learning attacks are not quite commonplace attacks as of yet, but laboratory tests of ML hacks show that these attacks will likely succeed more often in the future. With this knowledge, enterprises must optimize their overall security best practices (both through technology and stringent policies), adjust their ML logarithms over time, and back up their most sensitive data in other locations in case an attack on their AIOps succeeds.

    https://www.computertechreviews.com/network-automation-with-aiops/

    The Future of Network Management with AIOps