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IT leaders see the many headlines promising the delivery of AIOps, autonomous IT operations or self-healing capabilities. However, there are few automation technologies that can be deployed successfully while delivering even partially on their promise. There is a risk that while IT organizations are planning their near-term automation and AI strategies, going in those directions might not deliver the expected outcomes. This makes it important to review your enterprise IT automation strategy — starting with discovery.

Process Discovery For IT Automation Candidates

Anyone who has already started their automation journey recognizes early in the process that to scale beyond opportunity — the first low-hanging fruit — can be difficult. It often requires investing a lot of manual work, as well as extensive time and resources, and full collaboration from many stakeholders in the organization. 

There are a few ways you could make this investment, including automated discovery, dependency and mapping, as well as purpose-built machine learning (ML) and Natural language processing (NLP). NLP specifically can help extract automation candidates from your IT service management/network management systems.

These automated capabilities can be especially helpful as many work remotely, which can make it hard to review manual tasks and impact the ability to execute, collect and assess end-to-end processes without the appropriate teams physically with you.

Another challenge you may face is the rapid pace of your network, infrastructure and application changes, which are either in transition or already moved to the cloud. These changes may impact your processes and transactions, which may no longer be properly documented. It’s important to ensure those changes and applications are being monitored automatically.

And finally, your systems, applications and users are creating tons of data daily. Millions of records are being stored in your database (nowadays data lakes) and configuration management database, where you can find a great source of intel for what your employees, your clients and even your system are looking for in terms of solutions. There you also may find a great number of sources for your process automation candidate.

Intelligent IT Automation

How many times have you heard that? What does it mean anyway? To start, it doesn’t have to be AI or ML to make it intelligent, but AI/ML is required to have the ability to provide you with information that you didn’t know before! These include things that you didn’t know were missing and the ability to make decisions based on insights that were hidden in your data.

Most people who have built their automation are now searching to scale, learn, improve and repeat to keep some decisions and actions from needing further human intervention. Once it becomes intelligent, your level of maintenance of that task is either reduced or completely eliminated. Which is good by the way, you no longer need to do that task again, right?

You can achieve intelligent automation in many ways, starting with creating well-documented, well-defined processes. These can easily transform into automated processes, which then can be used by ML to continue the improvement of those actions or even use more sophisticated algorithms. These algorithms could help make the decision of when and how to execute these tasks.

Use ML/AI To Monitor And Improve IT Processes

The most effective and probably more practical approach to ML/AI is to use them to monitor, collect, learn and improve your enterprise’s IT processes. They can be especially helpful in calculating the ROI by using automation. The long-term ML could help move processes to self-maintained IT processes where you no don’t need to spend additional time on changes.

As many workforces are remote, ML/AI becomes your “brain” for the enterprise, which can make decisions when no one is available to make those decisions. Using intelligent IT automation processes, your team can become more agile and effective, moving from guessing to making smart decisions based on data and analytics, measuring service-level agreements (SLAs), projecting possible service interruptions and more.

The Road To Autonomous IT Operation

This is the holy grail for many AI companies and the dream of many chief experience officers. Technology is currently moving at a rapid pace toward “AI-driven anything,” and it’s hard to tell who is really making progress toward that goal. Yet the suggested approach above of selecting these three automation areas to focus on can help you get closer to reaching automation milestones. They can also help build the foundation for AI/ML to drive decisions based on rich and accurate data that is already at your fingertips.

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