The main uses of AIOPS in companies

AIOps adds value to organizations. In the future, it will play a very important role in increasing the efficiency of IT teams and will facilitate the adoption of next-generation complex technologies that traditional solutions are unable to cope with. Digital transformation requires AIOps because with this new concept, IT teams can automatically analyze large volumes of digital data and solve difficult problems faster. But what are the main areas in which AIOPS will have an impact?

Analysis of the cause of the problems

AIOs solutions help IT teams quickly understand the cause of a problem that affects a particular service or set of services and contextualize the relevant information so that the most appropriate correction can be performed.

Reduction of algorithms and correlation

AIOPS eliminates redundant alerts and automatically correlates related alerts to improve critical problem detection and expedite resolution.

Preventing problems through smart alerts

AIOps solutions can generate alerts based on abnormal scenario values ​​(anomaly detection). Algorithms learn from tool data and identify events that do not conform to a previously established pattern.

Intelligent automation

Through the intelligent insights generated, AIOps solutions drive processes in the automation and collaboration tools to provide faster problem correction.

Predictive Capacity Identification

AIOps solutions prevent (or at least greatly reduce) service interruptions and reduce waste by discovering underutilized capacity in hybrid infrastructures.

Agility between teams and datacenter groups

AIOps provides each functional IT group with relevant data and perspectives, as it has the ability to learn which analysis data to display for each group.

 

This new way of managing information technologies presents numerous challenges. The first of all is the resistance to change. There is still some mistrust regarding artificial intelligence and there is a fear that automating tasks will put people’s jobs at risk. Another challenge is data disorganization. Most companies don’t have the data in an organized way and Artificial Intelligence works entirely based on reading information to perform its function. When you read wrong information, it creates wrong patterns. Finally, another challenge in this sector is lack of planning. Some companies implement IA not to be out of the market or because the competitor deployed and had good results. However, each case is a case and if there is no planning on what the company expects of the machine and if there is insufficient and well structured data, the whole investment falls to the ground. It is necessary to do market research and evaluate the pros and cons of technology and its applicability to the business.

A few years ago it was practically impossible to think that there would be machines capable of speaking and solving problems autonomously. Whenever we approached questions of artificial intelligence and robots, we thought of a distant future and believed that this future would never come. Well, the future in which machines are able to help the human being autonomously is the present that we are living! Artificial intelligence is increasingly used in day-to-day business. However, it is not yet being fully leveraged by organizations and the IT PEERS Summit aims to demystify preconceived ideas and discuss how far Artificial Intelligence’s contribution to IT management can go.

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