Enamul Haque's Blog - Posts Tagged "itil"
The Use of Artificial Intelligence in ITSM Practice
The stunning promises to improve IT service management through artificial intelligence (AI) have been turning heads for quite some time. For the last couple of months, I have been heavily involved in getting the cloud operations run team mobilised with automation at its core. It's a gigantic project for a life-sciences company towards a colossal move to digitisation. During this troubling time of the pandemic, this project took my heart and soul.
The automation leveraging next-gen, chat as a medium for operations, collaboration and communication. Seamlessly integrating business users, developers, operations teams, cloud providers and BOTs and all in real-time.
But despite the genuine advantages that AI offers in an ITSM environment, the IT teams responsible should carefully check whether the use of the technology is really worthwhile in their specific case. They should also consider whether they can operate and support tools and processes based on AI.
While ITSM, in theory, has long promoted the entire life cycle of IT services and products - from the ideation of service to its decommissioning - many ITSM implementations only focus on ongoing tasks.
Many companies have set up a service desk and incident management as well as processes for answering inquiries. Others have defined a simple change management process that handles requests through a central approval point. Although these approaches have their own advantages, they are far from a comprehensive and holistic ITSM strategy.
With the emergence of new approaches such as DevOps, VeriSM, Lean IT and Shift Left in IT service management, frameworks such as ITIL, ISO / IEC 20000 and COBIT have undergone a significant revision.
Technologies such as Cloud Computing, the Internet of Things (IoT) , Containers, Microservices, Big data, automation and artificial intelligence require a fundamental revision of traditional ITSM processes. Another driving force behind the change in IT service management is purely business considerations, for example, the focus on customer and employee experience. Luckily, ITIL 4, proudly introduced XLA (Experience Level Agreements). XLAs are important in understanding the impact we’re having on the customer experience. XLAs can help us as an organisation develop our capacity for digital empathy.
Anyway, IT teams need to balance high responsiveness, which has become the standard in the digital economy, with organisational stability.
Artificial intelligence can improve or expand existing methods in IT product and service management. Most current AI-enabled ITSM systems focus on the following technical areas, with service desk and IT operations being the most popular use cases:
Service Desk: Relieves employees of tedious and repetitive tasks, such as the routine troubleshooting of recurring problems or answering inquiries.
Ongoing Monitoring: managing events with filters and disclosure of relationships.
Incident Management: Categorises incidents based on defined criteria, forwards tickets automatically or resolves simpler incidents without the need for human intervention.
Answering Inquiries: Answering end-user queries automatically, preferably via a self-service portal, including requests for all sorts.
Problem Management: Analyses large amounts of data in order to identify patterns and relate IT incidents to one another.
AI-enabled ITSM platforms can usually be divided into three categories: Reactive, Proactive or Predictive.
Reactive AI tools support the service desk primarily through self-service functions for the end-user. These tools are reactive because they only become active in response to a stimulus, for example, an alarm or human interaction. Examples of reactive AI in ITSM tools are chatbots and virtual assistants.
Proactive tools independently recognise a critical condition or an upcoming situation. They then initiate effective measures - for example, to resolve a necessary condition before an interruption in operations occurs. Automation, robotic process automation (RPA), and process orchestration tools are typical entry points into this category.
The third category is Predictive, predictive tools. They forecast future performance, demand or necessary work in the IT department. The forecast is based on an analysis of historical and current data. To make such predictions, some tools create new data sets from the collected data sets. Examples are ITSM tools that rely on machine learning and the evaluation of large amounts of data.
The fourth category of ITSM tools with artificial intelligence is currently emerging: Autonomous tools. They can make value judgments and take action that may have nothing to do with their original program. These platforms can recognise complex concepts such as risk-benefit considerations or the ethical effects of actions in ITSM and apply these findings. However, autonomous ITSM tools are not yet ready for the market.
The automation leveraging next-gen, chat as a medium for operations, collaboration and communication. Seamlessly integrating business users, developers, operations teams, cloud providers and BOTs and all in real-time.
But despite the genuine advantages that AI offers in an ITSM environment, the IT teams responsible should carefully check whether the use of the technology is really worthwhile in their specific case. They should also consider whether they can operate and support tools and processes based on AI.
While ITSM, in theory, has long promoted the entire life cycle of IT services and products - from the ideation of service to its decommissioning - many ITSM implementations only focus on ongoing tasks.
Many companies have set up a service desk and incident management as well as processes for answering inquiries. Others have defined a simple change management process that handles requests through a central approval point. Although these approaches have their own advantages, they are far from a comprehensive and holistic ITSM strategy.
With the emergence of new approaches such as DevOps, VeriSM, Lean IT and Shift Left in IT service management, frameworks such as ITIL, ISO / IEC 20000 and COBIT have undergone a significant revision.
Technologies such as Cloud Computing, the Internet of Things (IoT) , Containers, Microservices, Big data, automation and artificial intelligence require a fundamental revision of traditional ITSM processes. Another driving force behind the change in IT service management is purely business considerations, for example, the focus on customer and employee experience. Luckily, ITIL 4, proudly introduced XLA (Experience Level Agreements). XLAs are important in understanding the impact we’re having on the customer experience. XLAs can help us as an organisation develop our capacity for digital empathy.
Anyway, IT teams need to balance high responsiveness, which has become the standard in the digital economy, with organisational stability.
Artificial intelligence can improve or expand existing methods in IT product and service management. Most current AI-enabled ITSM systems focus on the following technical areas, with service desk and IT operations being the most popular use cases:
Service Desk: Relieves employees of tedious and repetitive tasks, such as the routine troubleshooting of recurring problems or answering inquiries.
Ongoing Monitoring: managing events with filters and disclosure of relationships.
Incident Management: Categorises incidents based on defined criteria, forwards tickets automatically or resolves simpler incidents without the need for human intervention.
Answering Inquiries: Answering end-user queries automatically, preferably via a self-service portal, including requests for all sorts.
Problem Management: Analyses large amounts of data in order to identify patterns and relate IT incidents to one another.
AI-enabled ITSM platforms can usually be divided into three categories: Reactive, Proactive or Predictive.
Reactive AI tools support the service desk primarily through self-service functions for the end-user. These tools are reactive because they only become active in response to a stimulus, for example, an alarm or human interaction. Examples of reactive AI in ITSM tools are chatbots and virtual assistants.
Proactive tools independently recognise a critical condition or an upcoming situation. They then initiate effective measures - for example, to resolve a necessary condition before an interruption in operations occurs. Automation, robotic process automation (RPA), and process orchestration tools are typical entry points into this category.
The third category is Predictive, predictive tools. They forecast future performance, demand or necessary work in the IT department. The forecast is based on an analysis of historical and current data. To make such predictions, some tools create new data sets from the collected data sets. Examples are ITSM tools that rely on machine learning and the evaluation of large amounts of data.
The fourth category of ITSM tools with artificial intelligence is currently emerging: Autonomous tools. They can make value judgments and take action that may have nothing to do with their original program. These platforms can recognise complex concepts such as risk-benefit considerations or the ethical effects of actions in ITSM and apply these findings. However, autonomous ITSM tools are not yet ready for the market.
