Part 4 – The future of Automation



In the final post of our four-part series on the future of automation, we are going to have another look at automated log analysis but coupled with artificial intelligence to provide additional insights into the most challenging question that IT professionals face – Is anything going to fail?

In part three, we listed some powerful new tools available that can help the application and system administrators analyze their data better. However, the drawback to this approach is that these administrators must take the time to analyze what is presented to them to perform their work. While these tools make the job easier, it still is a time-consuming task.

To further extend the productive time of administrators, IT leaders are now turning to Artificial Intelligence and Machine Learning to turbocharge activities related to failure prevention. Tools such as Splunk are now offering AI in their platforms, enterprising developers are also applying Tensor Flow or SciKit Learn analysis engines to the data gathered by automated log analysis to essentially “learn” what normal looks like in the organization and then proactively alert administrators when the network is drifting away from normal.

Additionally, these tools are most effectively being used with business cycle analysis to predict future demands-resources. For example, consider a case where an online retailer might have an annual sale, the data gathered from these tools can look back at past sales and the demand they created and then exponentially predict what future needs will be.

As technology increasingly complements the human workforce, businesses need to take a holistic approach to enterprise automation to reap significant benefits. The widespread and accelerated adoption of automation and AI has given organizations a powerful tool to aid them in their journey towards digital transformation.

The integration of automation technologies such as Robotic Process Automation (RPA), Hyper Automation, Test Automation, DevOps Automation, AIOPs, and Machine Learning (ML), not only overcomes the limitations of each technology on its own but also open new horizons to solve complex business problems. The next wave of revolution is all about automation at scale leveraging all these technologies.

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