My last article was the beginning of a series on eliminating noise in existing operations. In that article, one simple rule of thumb that was suggested is to identify noise in operations that does not require human intervention. In this series of articles, I will focus on what contributes to this noise in the form of the machine (environment), method (workflow and processes) and model (people and skills) and how to deploy artificial intelligence (AI) and machine learning (ML) techniques to identify and remove the noise. This article covers machine-induced noise.
Before we get into that, I would like to draw your attention to the core functions of AI for IT services (or, for that matter, any kind of service), which is to learn how problems are currently solved, identify the flaws in the current method of problem solving and build intelligence on how to solve problems differently to achieve peak business performance. Here, looking at the way problems are solved is of top importance, and in the upcoming articles, I will examine how the environment, process and people together are producing the noise we aim to eliminate.
Let us focus on machine-induced noise now.
One of the most low-hanging opportunities for a technology-driven transformation has been end-of-life products. Although upgrades and replacements are common sense, it is of paramount importance to first establish the connection between the end-of-life machine to the noise in the AI-driven IT services framework. We also need to quantify how much of the end-of-life machine noise is causing noise in the method and model. CXOs should be able to determine how the end-of-life machine noise, which reflects on the method and model, impacts business performance. This relationship in the form of behaviors and patterns is available in the historical data, as mentioned in my previous article.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/05/preparing-for-artificial-intelligence-enabled-it-services-machine-induced-noise/?sh=5e66ca40454d
Before we get into that, I would like to draw your attention to the core functions of AI for IT services (or, for that matter, any kind of service), which is to learn how problems are currently solved, identify the flaws in the current method of problem solving and build intelligence on how to solve problems differently to achieve peak business performance. Here, looking at the way problems are solved is of top importance, and in the upcoming articles, I will examine how the environment, process and people together are producing the noise we aim to eliminate.
Let us focus on machine-induced noise now.
One of the most low-hanging opportunities for a technology-driven transformation has been end-of-life products. Although upgrades and replacements are common sense, it is of paramount importance to first establish the connection between the end-of-life machine to the noise in the AI-driven IT services framework. We also need to quantify how much of the end-of-life machine noise is causing noise in the method and model. CXOs should be able to determine how the end-of-life machine noise, which reflects on the method and model, impacts business performance. This relationship in the form of behaviors and patterns is available in the historical data, as mentioned in my previous article.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/05/preparing-for-artificial-intelligence-enabled-it-services-machine-induced-noise/?sh=5e66ca40454d
