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The service desk acts as the go-to place for all IT-related needs and issues, typically managing incidents or service disruptions, requests, and changes. The service desk scope of work can be enormous and wide-ranging, depending on the nature and size of the organisation in question. As a critical function used by employees across a company, it needs to be managed appropriately.

Technology has upended the way business is done across all industries around the world. At the same time, traditional IT service management (ITSM) solutions have become inefficient in maintaining customer satisfaction levels and meeting increasing customer expectations in a fast-paced digital world.

According to the SolarWinds IT Trends Report 2019: Skills for Tech Pros of Tomorrow, 79 per cent of IT managers werent able to spend sufficient time on value-added business activities or initiatives due to interruptions with day-to-day support-related issues. This resulted in misleading or incorrect manual entries into a problem log, which caused misinformed decision-making. With managers inundated with work, its easy for them to accidentally become the victim of manual or human errors.

With IT environments changing at an accelerating rate, its crucial IT service desks adopt emerging technologies. An explosion of data in recent years has intensified the pressure for IT professionals, but automated processes and machine learning (ML) can alleviate this pressure significantly. Artificial Intelligence (AI) and ML arent just buzzwords anymore. Enterprises worldwide are incorporating these technologies to enhance and improve operational efficiencies.

Whether for their use in predictive analytics, providing business intelligence, performance monitoring of networks, applications and systems, or even for its importance in self-driving cars, AI and ML are transforming the IT space. So, what are the applications of ML when it comes to ITSM? As an essential driver of how a business operates, a service desk solution can employ ML to streamline processes, and reduce manual, time-intensive tasks, which will ultimately free up time for additional projects and training to deliver business-wide transformation.

Incident resolution time has the potential to be cut in half. ML will enable self-resolution of incidents without the involvement of technicians and users will be able to search for solutions by themselves. Chatbots (like Google Assistant, for example) will be able to give information to end users without them having to log a ticket by providing easy access to relevant knowledge base articles based on their queries. Through ML, help desks could learn from past incidents and data to route tickets to the appropriate technician or support group. This can considerably increase efficiencies. Even better, automated help desks can run 24/7, making services available to employees at all hours at their own convenience.

Old IT assets can cause performance degradation for employees who rely on technology assets to do their jobs. In turn, this can result in a sizeable number of incidents in an organisation. Businesses spend a lot of money on hardware and software because of asset management solutions with poor transparency. This can be turned around using asset management solutions with ML technology to help track their performance based on insights from performance levels or incidents associated with a given asset. If incidents about a specific technology asset come into the system frequently or en masse, ML can recognise these as being associated and therefore indicative of a broader problem to be addressed.

ML can consume large datasets of past performance data to enable an analysis of incidents to predict future problems. Predictive capabilities can help save time, money, and effort for the entire organisation as steps can be taken before the severity or impact of the incident increases.

When end users submit a ticket, automation rules rely heavily on data like categories and subcategories to ensure accurate routing. ML helps facilitate this process by providing end users with suggestions for the most relevant categories and subcategories for a given ticket.

Service desk reporting can show trends about seasonality. Predictive models, however, take into consideration rate of change, frequency of problems, and other key factors helping predict service degradation and likely resulting in increased incident flows. This can help determine when more coverage is needed to maintain service levels.

ML, while being versatile as-is, demonstrates some critical applications when it comes to ITSM. Increasingly, organisations are taking leaps and bounds in their digital journeys, and it is only right their IT services evolve with them.

Now is a critical time for the Information Technology service management industry. The market is growing at a double-digit figure each year and is forecasted by analyst house IDC to reach over $8.5 billion by 2023.

Today, organisations need to re-examine how they can use new IT management software incorporating machine learning capabilities. Only this can change the course of IT service management which has historically been a cumbersome function of every business IT department.

Just as with huge transformative initiatives, software and machine learning can help streamline processes and increase employee productivity to drive better business outcomes. Service desk software will let IT pros consolidate asset information from multiple sources and provide real-time asset intelligence, thus improving service delivery while enhancing flexibility for collecting and managing data. By removing the manual burden of tasks like ticketing and tracking of assets and their performance, this will enable IT professionals to focus on critical projects and business transformation.

Steve Stover, Vice President of Product and Strategy, SolarWinds

Link:

The role of machine learning in IT service management - ITProPortal