In the last 10 years, we've seen some major breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be an award-winning reality show. In 2014, Google acquired an AI for IT Support company called DeepMind, and one of their projects, AlphaGo, beat the European Go champion in 2015. Google released its TensorFlow library in 2016, making machine learning more accessible to everyone. The world was stunned by the news that Google Duplex scheduled haircuts over the phone last year. What implications do they have of ITSM?If there's a new technology, businesses are often the ones most affected (at most at the beginning). There are many technological changes that impact companies. A recent study by Gartner declared that "AI will be among the top five investment priorities for more than 30% of CIOs" and "AI technologies will be integrated into nearly every new software application in 2020." AI will change the ITSM practices and procedures within IT service management (ITSM). Let's explore how AI can impact the future of ITSM solutions. Voice assistance for technicians A lot of us think of AI with voice-based assistance. This is probably due to our smart phones and intelligent speakers which are equipped with voice-based AI capabilities. When I first began making use of Google help, I remember wondering what the weather would be like every day. After a while, the novelty faded and I realized I could have an application to display on my home screen, with weather information. In the same way, a lot of simple questions we ask are useless. Therefore, the issue to be addressed is, how can we make voice assistant effective for an ITSM product. The easiest way to do this is to go beyond tricks. For me, basic questions like "How many tickets do we have?" are useless, considering that nearly all top ITSM tools have powerful dashboards. A more comprehensive set of use cases is required. An example is a search tool that lets technicians make complex search queries by combining keywords and search options. The technician can look up tickets, issues or changes, knowledge articles, assets such as. Imagine they have an AI-driven assistant built into their search bar, like Google. An AI that can recognize the context and creates a profile for each technician. The advantages of such a ability include: More efficient ticket resolution that translates to happier customers The technicians don't have to create reports. They can make reports simply by searching for queries Technicians can conduct a deeper search on the issue to gain get the relevant data. AI-driven incident categorization Without the categorization of a ticketing system, there will be an endless chaos. This can result in both the support and end-users picking the wrong category for making tickets. This could lead to wasted time and possibly a business-level impact. Natural language processing (NLP) can allow an intelligent system to understand the context of every ticket. In time it will be able to gather enough information so that it is able to automatically suggest a specific category when making an entry. It could even be more accurate than its human counterparts. Articles that suggest knowledge base articles The best method to speed up the resolution time of tickets is to resolve frequent issues prior to submitting them as tickets. This is an area where artificial intelligence can play a key function. When a user, via the portal to service, attempts to complete an incident form, the system will suggest relevant knowledge base articles (after analysing the topic and the description). This will drastically reduce the amount of requests that are related to issues of common. When you identify the typical processes, you can recommend the automation of workflows Machine learning-based ITSM tools can learn the ways technicians resolve issues. It is able to recognize patterns that often repeat and, based on that it could suggest workflows considering the possibilities. This will allow the IT admin to progressively streamline IT services. Actively identifying problems Every major ITSM software has a problem management database called a known error. A database of known errors is a collection of problems that have happened multiple times and is an excellent resource to use a machine-learning algorithm. ITSM tools are able to recognize known errors and provide an answer without the need for a technician. Analysis of sentiment We have already spoken of NLP. Sentiment analysis is the part of NLP that analyzes the context of text generated by humans. One example is to determine the mood of the requester simply by looking at the description and subject while making a request ticket or conversing in a chat. The main advantage of this capability is that a technician would gain insight into the situation prior to deciding on the best priority. Perhaps it will help you to resolve an issue sooner than expected.
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