A huge portion of the typical labor force today will be replaced by artificial intelligence. It is not a question of if, but of when, by that time people needs to see new possibilities to focus on such as creative work, the arts and other aspect of human interest that AI will never able to do with current technology available. All of us need to correctly understand that it is going on, and what is our future labor situation once the world reaches the age of singularity.

Manual labor and repetitive jobs are doomed to be discontinued by AI for IT Operations, for example, clerical jobs that have routine tasks. A specially-created script that sorts and processes documents can replace clerks. Since the image recognition accuracy by artificial intelligence is improving year by year, the practical application that the document is simply transferred to the system by scanning the form is not a distant science fiction, but just an engineering problem. Therefore, if you think in terms of “If we cannot develop creative skills? We may lose our jobs”.

Recently, there are more cases where such artificial intelligence is used for the “operation” of the information system department. System operation is often considered “defensive IT investment”, and many companies operate as cost centers. Certainly, it has been difficult to create a business from system operation until now, but in the modern world where artificial intelligence is steadily implemented, system operation is no longer a cost center but has been transformed into a profit center.

If you say “artificial intelligence”, there may be many people recalling computer personalities such as IBM Watson or ASIMO of Honda. However, such artificial intelligence is rather far few in between, and most artificial intelligence for IT Operations is treated as a tool for solving business problems by performing data processing specialized in a specific field. What is important in developing artificial intelligence is what learning method and what data to read. There are two learning methods: “supervised learning” and “unsupervised learning”, as well as the topic “deep learning” since last year.

Supervised learning is to tag the data to be brought into artificial intelligence (answers) and learn its characteristics by reading a large amount of data. Unsupervised learning, on the other hand, does not tag data but is used to discover the relationship between data, etc. As for deep learning, it is a little complicated, and it is a learning method for a computer to learn something autonomously, inspired by a neural network called “Neutral Network” in the human brain. For example, an artificial intelligence developed by Google in 2012 can be recognized as a “cat” by reading numerous “cat” images, even though it is not tagged. It is still being enhanced as we speak, as the search giant changed its CAPTCHA system recently, accommodating pattern matching challenges. Yes, all of us are training Google’s AI even without informing us through the CAPTCHA system.

The most familiar artificial intelligence for Internet users is the search engine. Have you ever wondered if search engines like Google, Duckduckgo and Bing will hit the right web page for the keywords you searched? There is a major connection to artificial intelligence.

Among search engines that store a large amount of data, there is a robot called “crawler”, and is actively collecting information on the Web site 24/7. By evaluating the value of Web sites and their pages from the collected information, we can display Web pages that are valuable for Internet users on search results. By the way, since the algorithm is private, no one other than Google knows what kind of standard is being evaluated.

Did you know that such artificial intelligence has already been put to practical use in the world of system operation? For example, in the field of “abnormality detection”, which is a very important role for information system department operation personnel, we have already achieved remarkable results. chatbots that use the knowledge base of system operation are gradually spreading. Regarding frequently asked questions about the manual among the inquiries from the user department, by installing a bot in the business chat space using artificial intelligence, it is possible to solve the problem immediately without directly contacting the system operation. The environment variables can be set depending on the requirements.

The biggest benefit of using artificial intelligence in system operation will be “significant improvement in work efficiency”. In conventional system operation, it is difficult to predict abnormalities, and there will be many cases in which responses are taken after the fact. As a result, not only did the efficiency of system operation not improve, but it also reduced the productivity of department users.In other words, by realizing “predictive maintenance” rather than “post-maintenance” or “periodic maintenance”, it is possible to eliminate the occurrence of an abnormality itself.

This significantly improves the efficiency of system operation and allows people to focus more on measures to improve customer satisfaction. In some cases, it is possible to add artificial intelligence to maintenance operations at the customer site and monetize from there. In other words, system operation has been transformed as a profit center, not a cost center, and is important for a cheaper, yet more productive IT management strategies. The use of artificial intelligence for IT operation is steadily expanding.

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