Use cases for the application of AI in eu-LISA

• Author: Cepilovs, Aleksandrs
• Pages: 23-32

AI IN THE OPERATIONAL MANAGEMENT OF LARGE-SCALE IT SYSTEMS  23

4. Use cases for the application of AI in eu-

LISA

4.1. AI-enabled IT service management and IT operations

Although improving the efficiency of IT service manag ement operations using automation of machine

intelligence seems like a low hanging fruit, this area has been somewhat lagging in terms of deployment of AI

solutions64. However, IT opera tions in both private and public sectors fac e a number of challenges, which

already in the near to mid-term is likely to stimulate more interest in AI-based so lutions.

 First, user expectations towards service providers continue to grow, resulting in increasingly tight SLA

requirements. Users today expect both very high levels of system availability and an increasingly short

time for incident resolution. At the same time, public sector organisations are under constant pressure

to improve effectiveness and efficiency, which often results in severe budget constraints.

 Second, overall complexity and interdependencies between different integrated components and

services which make incident resolution increasingly time-consuming.

 Third, the ma nual systems for IT service management (in particular for IT service desk s) currently in

place are often insufficient for todays fast-paced, dynamic and complex IT service operations.

Responding to thousands of incident al erts  a significant number of which are inevitably false alarms

 places IT service desks under unnecessary pressure and may result in alert fatigue. Freeing up part

of the service desk capacity by partially automating incident management will allow these resources

to be dedicated to other tasks that require increased human judgment, such as problem management,

for example.65

The challenges outlined above, coupled with budgetary constraints, put organisations under increasing

pressure to improve cost-effectiveness. Some of these challenges can be addressed by introducing automation

into IT operations.

When it comes to AI in support of IT infrastructure and service management, its application can be roughly

divided into three categories.

 Anomaly detection systems using ML algorithms can be applied to application performance

monitoring as well as IT infrastructure and network monitoring. Tools using ML algorithms for pattern

64 https://www.bcg.com/publications/2019/artificial-intelligence-coming-inform ation-technology-operations.aspx

65 (ibid.)

Application

performance

monitoring

IT

infrastructure

monitoring

Network

monitoring

and

diagnostics

IT event

analytics

IT service

desk

Figure 4: Opportunities for AI in IT service management