AI-Driven ITSM is Transforming IT Management | ENP – EnterpriseNetworkingPlanet

Artificial intelligence has been making headlines for driving autonomous vehicles or writing startlingly clever poetry, but it is also quietly finding new applications that can reduce the workloads—and the headaches—at the IT service desk. AI’s natural strengths at processing huge volumes of data and divining the hidden patterns from within are now being brought to bear in the IT realm, where they can resolve problems quickly and proactively, reduce server downtimes, and balance work more efficiently. 
Companies that have adopted the IT Service Management (ITSM) model—an organizational philosophy that seeks to standardize IT best practices to improve customer experience and service quality—are finding that not only can AI assist to close tickets faster, but it also empowers the customer with rapid self-service.
Also read: Enterprise Service Management and ITSM: What is the Difference?
A company employee is struggling to print a document from the new office printer. He calls IT, but there was a massive operating system update rollout this week, and they are backed up with a three-hour queue of troubleshooting calls. Help is not coming quickly. Fortunately for this employee, he is experiencing a common problem with common causes that the employee can resolve on his own with the assistance of AI.
Chatbots have come a long way since the 1960s, and their roles have broadened as their ability to process natural language and respond to simple queries has improved. By applying machine-learning techniques, a chatbot can search through troves of data pertaining to printer troubleshooting, find the commonalities, and walk our hypothetical employee through the routine steps to identify the cause of the problem and resolve it—all without the necessity for IT to author a knowledge base article on the subject. 
As printer problems accumulate, the bot has a greater well of data from which to collate and prioritize recommendations, making future problem-solving steps quicker and more accurate. Bots are not a panacea solution to every imaginable problem, but they can cut down call volume for small, frequent issues. This leaves IT with greater bandwidth for resolving more complex issues.
The AI can also be employed to conduct sentiment analysis, in an effort to determine user satisfaction. This analysis can be derived from natural language processing of survey results, and also from user word choices when interacting with chatbots.
Read more about Conversational AI: Leveraging Conversational AI to Improve ITOps
For problems requiring human intervention, AI still has roles to play. For example, through the use of predictive analytics, AI can help IT with ticket creation and assignment using recommendations based on degrees of confidence. This works much the same way Google serves up ads: AI examines past behavior to inform future action, just as Google monitors browsing habits to serve personalized ads. Through analyzing historical tickets, the AI can generate predictions about future ones, such as determining the urgency and classification of a ticket, or even to whom the ticket should be assigned based on their skills, experience, and past assignment of certain types of tickets. Ticket assignment can also be made based on the prioritization of work distribution, to avoid bottlenecking. All these recommendations are generated on the fly and plugged into a ticket as a human is authoring it, saving considerable time in filling out each field individually.
One of the best practices of ITSM is to identify the underlying cause of a group of similar problems—treat the disease rather than the symptoms. By employing an AI-driven Root Cause Analysis engine, a computer can once again engage with large quantities of historical data and derive solutions that may have eluded an all-human team.
Menial but time-consuming tasks can be significantly reduced through the use of Robotic Process Automation (RPA). Tasks that are repetitive in nature can easily be handled by an AI, thus resolving tickets more quickly and allowing humans to focus on more complex issues.
By leveraging an enterprise’s existing network, systems, and application performance monitoring tools, an AI can detect anomalies within a system that might be precursors to a server outage, and quickly generate incidents to the attention of the service desk, potentially reducing or preventing downtime. Some industries are already adopting this same practice across entire facilities, using monitoring tools and an AI that has been trained to detect failures in equipment, or even observe the warning signs of an impending outage. In response, the AI can alert maintenance and asset management staff to a potential problem. Insights from the historical lifecycles of facility assets can further inform an AI of future asset performance deterioration, and lead to the generation of timely service, maintenance, or inspection requests.
Lastly, AI has a role to play in Knowledge Management. If an incident requires a solution not found in the existing knowledge database, then an AI would be empowered to search for an answer from a broader network of trusted knowledge sites external to the enterprise. Once the AI identifies the proper solution, it can summarize and document the knowledge locally for future use.
AI is not a magic solution to every problem; it is a suite of technologies that deliver incremental efficiencies to an organization’s workload. By unburdening IT from time-consuming, repetitious labor, AI allows its human counterparts to focus on bigger objectives that serve an enterprise’s strategic business goals. 
Read next: Top AIOps Tools & Platforms of 2021
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