Automating service management by using AI Service Management capabilities (NLP and clustering)

AI Service Management or AISM is the application of artificial intelligence (AI) to service management by leveraging technologies such as Natural language processing (NLP) and clustering techniques. AISM is an emerging approach that aims to solve the increasing challenges of traditional IT service management, and is focused on proactive prevention, faster restoration of services, and rapid innovation. Service desk agents perform a variety of time-consuming, repetitive tasks that can be easily automated. Automation can thus help service desk agents by resolving tickets faster and providing a higher quality of service. 

Automatically responding to inbound emails by providing self-help options and resolving tickets

The goal of this use case is to help business users resolve issues on their own by providing them with self-help options.

Scenario

Susan is a service desk manager at Invention Inc. She notices that roughly 10% of the tickets logged in every month can be easily resolved by the users themselves by referring to knowledge articles. She requests Jonnie, the administrator, to enable the Apply-Cognitive-For-Recommending-KB centralized configuration parameter for recommending knowledge articles. Jonnie has already enabled integration with BMC Helix Digital Workplace and BMC Helix Knowledge Management by ComAround.  When a user sends an email to the service desk, they receive an auto-reply that contains links to one to three  relevant knowledge articles. If one of the articles solves the issue, users click the Resolve link in the email to update the incident. For more information, see Automatically responding to inbound emails with knowledge articles.

Workflow

Identifying problems proactively

The goal of this use case is to help problem coordinators identify frequently recurring incidents and initiate a root cause analysis.

Scenario

Colin is the Problem Coordinator at Invention Inc., where one of his core tasks is to review closed historical trends to proactively identify underlying problems, which have not yet been logged and brought under management. Colin usually obtains detailed incident reports in Excel spreadsheets and manually categorizes incidents that are similar to analyze the trends. Colin uses the Proactive problem Management workspace in ITSM Insights to analyze closed and resolved incidents. He creates recurrent and one-time jobs to generate incident clusters. From the dashboard, he is then able to analyze the clusters that generate a significant amount of work for the Service Desk. For a service for which there is a significant number of incidents, he creates a problem investigation to perform the root cause analysis. After creating a problem, Colin assigns the problem to a specialist to identify the root cause and propose a solution. For more information, see Identifying problems proactively.

Workflow

Identifying and tracking multiple incidents for the same issue

The goal of this use case is to help Service Desk managers correlate incoming incident tickets that refer to the same issue. They can then relate these incidents to streamline incident management and reduce duplicate work.

Scenario

After an outage of Skype across various divisions of Invention Inc., the service desk has been receiving multiple incidents where users reported that they are unable to connect to Skype. Susan, the service desk manager, is worried that multiple agents will work on the same issue. Also, Susan realizes that after an incident flood, agents need to spend a lot of time and effort to manually relate incidents as duplicates. Susan uses the Real-time incident correlation workspace in ITSM Insights to analyze new, incoming incidents for similarity in real-time and take necessary actions. With ITSM Insights, Susan can mark incidents as Original and indicate when she encounters duplicate incidents. In case of multiple duplicate incidents, Susan can easily tag these incidents as candidates for relating to a cluster and thereby, reduce duplicate work. For more information, see Identifying and tracking multiple incidents for the same issue.

Workflow

Detecting major incidents

The goal of this use case is to help Service Desk Managers to identify issues that are likely to turn into major incidents.

Scenario

Susan, the Service Desk Manager at Invention Inc. monitors the Real-time Incident Correlation dashboard in ITSM Insights to view the trend of incoming incidents. She notices many incoming tickets about the CRM application in the dashboard. Many users have reported that the CRM application is taking a long time to load and perform routine actions. Susan finds that 54 new tickets were logged in the last one hour. She analyzes the cluster, flags an incident in the cluster as a candidate for a major incident, and assigns it to the Major Incident Manager. The Major Incident Manager in Invention Inc. uses BMC Helix ITSM Major Incident management to track and manage these major incidents. For more information, see Detecting major incidents.

Workflow

Benefits

By using artificial intelligence and machine learning capabilities to automate service management, organizations can reap the following benefits:

• Self-service for users
• Proactive prevention of issues
• Reduced service management costs
• Enhanced service levels
• Scalable service
• Improved service predictability