As the pace of change in IT continues to race forward, expectations about IT infrastructure performance are escalating from the business side at the same time. To deal with these dueling changes, having effective IT service management practices in place is critical, especially with the IT skills shortage facing so many organizations.
To that point, according to a recent Forbes Insights survey, 36% of 261 executives said IT staff limitations was the top hurdle to successfully linking IT to support corporate services.
IT service management (ITSM) automation can play a crucial role in helping close the gap between IT staff and business expectations. While automation has been part of ITSM since its earliest days as a foundational framework for defining, operating and governing IT services to support business goals, advances in technology are improving its value.
Specifically, developments in AI and machine learning are producing compelling new use cases for ITSM automation -- many of them moving network automation forward. AI systems manage processes that normally require human intervention, including speech recognition, decision-making and language translation. AI technology offers elements like digital assistants and bots to play key roles in ITSM automation.
For its part, machine learning -- a subcategory within AI that applies statistical algorithms to understand aspects of the infrastructure environment and improve processes -- is helping extend network automation and improve tasks in areas such as network troubleshooting and threat identification.
In terms of network infrastructure, network automation helps remove repetitive manual tasks, freeing IT staff to manage more complex processes. It also eliminates human error that often occurs during redundant manual work. Applying ITSM automation in more aspects of IT service management allows businesses to do more with less staff. Through the automation of ITSM, enterprises can cut costs, increase efficiencies and improve overall agility in areas like network provisioning.
ITSM automation offers manual exit strategy
ITSM automation is a key piece of the puzzle on a number of fronts, ranging from asset and configuration management to change management and service catalog management. Automation facilitates the migration away from manual data collection and verification to mechanized discovery and self-registration. IT professionals can manage much larger configuration data sets than in the past to help them support efforts such as continually shifting cloud configurations and densely populated IoT environments.
IT infrastructure and network automation make it easier to cut costs for discovering and managing configuration data. And machine learning supports real-time recognition of changes in the infrastructure, which expedites problem recognition. If organizations deploy cognitive automation with network analytics, up to 70% of incident management tickets and service requests can be handled and resolved automatically, according to India-based IT services provider Wipro.
Through elements like programmable machine learning for networking, APIs deployed in production environments and other tools that enable ITSM automation, IT can troubleshoot network issues more efficiently. Machine learning for networking also can be used to accurately identify potential network threats and reduce the volume of false positives.
The use of virtual agents and bots also supports elements of self-service in customer support. Additionally, it reduces staff requirements and expedites service fulfillment. While these AI tools have been available as part of service desk implementations, vast improvements in their functionality and consistency are helping extend possible use cases to the network. Lines of business can tap these digital assistants for service fulfillment that goes beyond IT requests. Advances in automation also open the door to better coordination across IT financial management systems and the service catalog.
ITSM challenges often nontechnical
As promising as the advances in ITSM automation are, barriers to its success still exist. Some of the most difficult obstacles to overcome are nontechnical in nature. Many organizations are behind in implementing a cohesive approach to IT service management and instead execute practices in isolation from one another. For example, an IT department may provide aspects of IT service management in support of one line of business, but not the entire enterprise.
Steep challenges are also associated with integrating heterogeneous tool sets that can impede the development of a unified IT service management strategy. Organizations have to reconcile whether integrating multiple systems will create a consistent approach to service management, or if it is more pragmatic to scrap some legacy tools to avoid extensive re-engineering work.
What's becoming clear is if IT and lines of business can collaborate to implement a comprehensive strategy, IT service management functionality can extend beyond IT service fulfillment. But this requires groups across various technical and business units to work together to understand how IT and corporate assets are interconnected.
ITSM automation can play a crucial part in helping organizations move toward a consistent IT service management practice, eliminating many manual processes that add cost and increase the risk of error. Ultimately, an effectively implemented IT service management approach that uses automation puts the business in prime position to better adapt to the constant change that is likely to continue into the foreseeable future.