For all the technology they directly handle and help fix, the IT service desk is often shorthanded when it comes to cutting edge solutions of their own. Many IT service management (ITSM) processes are based upon a legacy of predictable service delivery, which can lead to long delays in resolutions to unforeseen problems. Worse, without a way to completely understand problems and proactively address them, ITSM goals can only get so far.
What IT service teams need is an approach that addresses unpredictability by leveraging their existing systems and talent. Implementing artificial intelligence (AI) technologies can augment the capabilities of your current IT team, enabling them to accomplish more with a similar resource pool to what they already have.
“When AI is a part of enterprise ITSM offerings, it can enhance the quality of every employee’s experience — from technician to end user,” writes CMSWire.
But what does it mean for AI to become a part of ITSM, and how does AI in service management further the goals of enterprise-wide digital transformation? Below are some examples that illustrate the potential AI offers to IT service employees and how these enhancements benefit everyone in the organization — from top to bottom.
Trends for AI in Service Management
There are a number of use cases for AI in ITSM that are currently being implemented in enterprises.
The use of chatbots and “virtual” IT staff is one of the most widespread — and rapidly increasing — implementations of AI in ITSM. According to one recent survey, 53% of organizations that use chatbots internally use them within their IT department.
Both chatbots and virtual service workers offer IT customers expedited access to self-service capabilities or the right IT assignment group they need to resolve their issues most efficiently. In some cases, that might mean being directed to a knowledge base article for a quick self-fix, but in others, it can mean automatic assignment to a high-level assignment group. This bypasses the human-based system that often acted as a gatekeeper for escalating IT tickets past the lowest level assignment groups.
Natural Language Processing (NLP)
AI can help both users and IT service staff get a more accurate understanding of requests, incidents, and problems in order to more directly address trends and recurring issues. In the past, categorization relied on drop-down menus with confusing options like “software” vs. “application”, or they may have relied upon open-field text entries that could be difficult to parse.
The use of natural language processing (NLP) can give structure to human-generated data, while topic clustering can automate the assignment of ticket metadata to more intelligently manage problems on a group basis. This technology can also allow IT to rapidly view patterns, such as a trending problem type with a specific application that could lead to an incident.
Robotic Process Automation (RPA)
Robotic process automation (RPA) can automate resolution of a ticket once it has reached the correct assignment group. RPA can reduce the effort and resources needed to resolve a ticket while allowing groups of similar incidents to be ameliorated in a more efficient fashion. RPA capabilities typically include simple, but repetitive and time consuming tasks that require form entry.
“In current service management organizations, 70% to 80% of resources are spent on operational activities: executing service requests, closing incident tickets, and delivering changes,” Pink Elephant’s Jan-Willem Middelburg explained to TechBeacon. “All of these activities can be automated, making service delivery faster, cheaper, and more efficient.”
Putting Enterprise Data to Work Using AI
One of the most important functions of AI is to give human ITSM workers access to insights they need to make key business decisions. Many IT systems and especially the applications they address are held tightly within their respective siloes. Generating any sort of holistic knowledge describing an organization is impossible as long as these siloes persist.
The use of AI and data analytics can make insights far more accessible by aggregating enterprise data into one place. By making all data equitable and then performing analysis upon it using AI and machine learning (ML), IT service leaders can determine opportunities to not only address ongoing incidents but also proactively prevent them.
Root Cause Analysis (RCA)
A root cause analysis (RCA) engine can identify the actual cause of problems, allowing IT service teams to address their provenance rather than just the resulting symptoms. Combining RCA with topic clustering can also allow IT to identify issues grouped by similar causes — similarities that might not be picked up on without the use of AI. This allows for resolution of batches of issues through an efficient problem fix rollout, such as an operations change or the release of a new knowledge base article.
Visualizing IT Service Data
Many times, visualizing data related to IT’s service domain can allow for better service to be offered to individual users and specific user groups. For instance, analyzing unstructured social chatter data from corporate productivity and messaging tools can allow IT teams to quickly understand what applications or problem types are giving users the most trouble. They can also reveal the types of solutions that have already been tried by the users in question, preventing the back-and-forth “did you try this or that?” that some knowledgeable users find tiresome.
Best Practices Include: Use an Ecosystem Focus, and Find Quick Wins
Having a system to house and analyze data and then display it on informative dashboards should, in fact, be priority one for organizations interested in seeing their AI investments return the most gains. The groundwork laid by this data infrastructure can facilitate current projects while opening the door to more ambitious ones down the line.
“Organizations will increasingly need to adopt an open innovation and ecosystem approach to acquire the needed data, technologies, and services,” advises a Boston Consulting Group report on AI’s role in digital transformation. “This approach can often lead to more and better data, which can translate to better results.”
Allowing IT service leaders to visualize the sentiments of their user groups can prompt proactive solutions to improve service delivery, especially for user groups currently experiencing the most pain.
“Quick wins are smaller projects that involve optimizing internal employee touch points,” offers Kartik Hosanagar, a professor of operations, information and decisions at the Wharton School of Business at the University of Pennsylvania. “For example, companies might think about specific pain points that employees experience in their day-to-day work, and then brainstorm ways AI technologies could make some of these tasks faster or easier.”
AI Offers Tangible Benefits to IT and the Customers They Serve
Adding AI to the IT service toolkit can reduce the burden of low-level administrative tasks that lead to delays in problem resolution. RPA can further optimize the workflows ITSM recommends by removing the need for in-between tasks that are necessary but add little incremental value.
Predictive analytics and trend analyses can allow IT service teams to proactively identify both problems that stand to become full-blown incidents and nagging IT issues that tend to drive down user satisfaction.
The use of IT business analytics facilitates these activities while providing IT service leaders with a firm infrastructure to derive further insights. KPIs that directly measure IT service satisfaction can be monitored, allowing IT to care for sensitive user groups while bolstering priority elements of the value production chain from service disruptions. Visualizing IT service data can also rapidly illustrate enterprise-wide problems and their potential solutions, giving IT leaders a powerful tool to earn collective buy-in from key organizational stakeholders.
Overall, AI can act as the catalyst to bring digital transformation fully to an organization by empowering the teams that keep its technology running smoothly. In this way, AI and ITSM make a powerful combo that has a way of producing exponential value for an organization through improved efficiency and the proactive addressing of persistent technology-related problems.