What does Artificial Intelligence in Operations mean and how you can utilize it?
Operational efficiency has always been on the agenda for organizations looking to succeed in their field, but only within the last couple of years have IT operations really embraced AI as a means of improving their services. The use of Artificial Intelligence (AI) in IT Operations (Ops) has led to AIOps, an umbrella term that refers to the use of AI across all primary functions within IT operations.
The complexity of the tools needed today and the separation of functions in operations have created the siloes of data and business processes that AIOps is poised to solve. A key enabler is therefore data coming from all levels of the IT stack, from infrastructure to applications. Incidents, events, network performance and changes are the data you want to aggregate before utilizing machine learning to enable event correlation and root cause analysis. The right use of this output can provide actionable insights and automation that will improve both operator and end-user experiences.
While AI capabilities have been increasingly deployed in recent years, there’s always a hesitance to interfere with the most trusted and most critical systems. That game is changing. All sectors have been both implementing RPA processes to automate manual tasks and playing around with proofs-of-concept within emerging technologies. However, the increasing complexity of the underlying services creates the need for an intelligent and proactive IT infrastructure.
The most important impact of AIOps in any organization is not really the tools and architecture. Your developer has probably played around with some of these tools already, and your engineers and architects will have no trouble finding the proper landing zone for you.
The impact you should care about is the cultural shift necessary to enable the right cross-team collaboration. This requires a new mindset – some of us must admit that success in operations is about more than just being stable, secure and reliable. Now is the time to empower the professionals closest to the problem. They are the ones who can create tomorrow’s solution through CI/CD and DevOps toolkits. But DevOps has been around for years, so why now?
The latest tools and platforms enable many more people to take part in the AI journey. “Everyone” can create an AI proof-of-concept or even push one into production, but many struggle with scalability and sustainability, and haven’t yet considered the implications of continuously managing the machine learning lifecycle in production, often referred to as MLOps.
How to manage your new AIOps capabilities and the required collaboration isn’t always clear, so you should consider the options carefully before deciding how to proceed in this important area.
There are generally two options. You can buy a platform with embedded AI functionality and a training studio, or you can build the AI solution yourself, often from public cloud APIs and capabilities. The popularity of the first option has grown rapidly over the last few years within areas like IT monitoring and chatbots, but the latter option remains an approach trusted by data scientists working directly with the data. Both models can work, but it’s important to consider the effect the decision has on the organization – and the management your choice requires.
It’s difficult to build and maintain everything yourself, and even if you could, there might not be a sustainable business case for total in-house control of all capabilities. This is the time for business owners to step up their game and join the AI journey. It’s more important than ever to identify how AIOps can add value to the team and overall business, and if you want to improve SLAs, reduce mean time to resolution (MTTR) and increase the quality of your daily operations, you already have a starting point.
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You should also check out the recent ISG Private/Hybrid Cloud study.
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