The processes involving IT service management (ITSM) manage to be effective with the most predictable requests of users, specially when there are unexpected situations or problems that are difficult to diagnose.
IT teams today , to deal with the most complex situations, need the synergy of ITSM with the most modern analytical machine learning and artificial intelligence (ML / AI) technologies. Technologies that can improve work efficiency and the quality of the experience, both for technicians and for end users. What does AI add to ITSM capabilities?
First of all, the possibility of taking advantage of chats and voice recognition to create virtual assistants capable of giving quick answers to user requests. A means that allows users to self-service common problems and filter requests in order to direct them to the most suitable technical staff to provide effective answers.
This way you avoid engaging qualified technicians in first level requests. Another theme concerns robotic process automation (RPA) with which it is possible to solve the most repetitive ITSM tasks with automation to solve common or previously seen needs. A contribution that should not be underestimated since normally 70-80% of ITSM people are engaged in tasks that could be automated.
Another area where AI makes the difference is problem diagnostics and support for team intervention choices. The processing of large amounts of operational data through AI engines for root cause analysis allows you to group different symptoms and find the causes of problems more quickly for faster resolution. ML / AI tools can help keep an eye on the data relating to the quality of services provided to departments or end users, possibly correlating them with analyzes relating to the content of e-mail messages or chats relating to IT problems.
The ability to understand user sentiment allows us to intervene more proactively in the resolution of slowdowns or other minor problems before they become reports of failures and ticket openings. To be successful, the implementation of AI in ITSM must follow a pragmatic approach, focusing on what gives the greatest economic returns. It will be necessary to begin with the integration and creation of a data platform for service management with which to power both traditional analysis tools and the more sophisticated ML / AI applications.
BinHexS has been operating for years in the fields of service management and infrastructure management provided both locally and remotely, integrating the customer’s resources. BinHexS is able to operate both on state-of-the-art technologies and on the customer’s human resources to improve the quality and continuity of services up to 24×7 coverage for 365 days of the year.