Today, smart automation tools, such as Machine Learning is transforming the world of work, not only in such areas as manufacturing and logistics but also in the office. It has been possible for some time to automate various routine and repetitive tasks, but the future lies in the application of AI technologies that learn and iterate from past data and experience, and incessantly maximize accuracy in response to a wide range of issues.
Many reports claim that AI will replace legacy automation systems. Today, it is IT administrators who build and maintain the automated processes in IT service desks. For instance, categorization of incoming requests is currently automated through rules that carry out this task, based on set parameters. In very dynamic IT environments these automation rules might not hold well all of the time since they lack the intelligence to adapt and advance. But with the application of Machine Learning, a categorization algorithm can be trained based on requests from a particular period or ‘n’ number of traditional requests. This trained algorithm will be able to perform categorization more efficiently than human-defined rules, and it will continuously learn over time. This could save hundreds of workforce hours that are either spent on the manual categorization of requests, or in creating, maintaining and updating the automation rules.
The AI revolution must take many small steps before its promise is fully recognized. One ubiquitous, labor-intensive office task that is ripe for transformation through Machine Learning is the IT service desk. Currently, service desk tools’ automation is static, has very little built-in intelligence, and requires periodic human interference to recalibrate over time. However, this will change as Machine Learning applications allow smart automation. Empowered with changeable smart automation, IT service desk tools will be able to classify incoming requests, assign them to the appropriate technicians, and suggest solutions without the need for any human support. They will achieve this by learning from the traditional IT service desk data.