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 aren’t 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.
How are AI solutions being integrated into IT service management?
1. Efficient handling of level 1 incidents
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.
2. Asset management
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.
3. Problem prediction and prevention
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.
4. Automated ticket routing supported by ML
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.
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5. Predictive ticket flows for service desk staffing
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.