7 Key ITSM Use Cases for Machine Learning

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By Stephen Mann, guest blogger from ITSM.tools

Principal and Content Director at the ITSM-focused industry analyst firm ITSM.tools. Also an independent IT and IT service management marketing content creator, and a frequent blogger, writer, and presenter on the challenges and opportunities for IT service management professionals.


The world of IT service management (ITSM) is changing fast and to deliver the service or customer experience employees now expect (and, in many ways, to remain relevant to them and the business as a whole), corporate IT organizations need to look to consumer-world customer service and support advances.

Why? Because employees are also consumers, and they are bringing their consumer-world experiences and higher expectations of service and support into the workplace. Particularly where new technologies are already creating better customer experiences in business-to-consumer (B2C) scenarios.

But corporate IT organizations can’t just look for, and walk along, the more obvious, well-worn paths. Instead they also need to keep a watching brief on, and potentially plan for, other consumer technologies that are expected to gain traction in the short to medium term.

Three consumer technologies that will improve IT service delivery and support

There are of course others, but it’s worth looking at the three below to start thinking about “the art of the possible” when using such technologies for IT service management (ITSM):

  1. Augmented reality (AR) – “The real-time use of information in the form of text, graphics, audio and other virtual enhancements integrated with real-world objects” (Gartner). From something as simple as an end user being able to see what part of the printer to open to remove a paper jam, to an end user mirroring a remote engineer’s hands (and actions) to replace a broken screen on a laptop.
  2. Bots (or software agents) – “A computer program that acts for a user or other program in a relationship of agency” (Wikipedia) – and virtual personal assistants (VPAs) – “A conversational, computer-generated character that simulates a conversation to deliver voice- or text-based information to a user via a Web, kiosk or mobile interface” (Gartner). With VPAs most likely becoming the employee access point for corporate services, data, apps, and other content. For example, an end user could request the solution to an IT issue, or be sent content relevant to the workplace IT ecosystem, in the same way they would use Siri or Cortana, or allow Google Now to prompt them with useful information.
  3. Machine learning – “The study and construction of algorithms that can learn from and make predictions on data” (Wikipedia). With so much data available in ITSM tools, there’s a wealth of opportunity to exploit this technology to improve both operations and the customer experience. Machine learning also ties in nicely with the aforementioned bots and VPAs.

A deeper dive into ITSM and service desk machine learning opportunities

There’s so much information held within ITSM tools – related to entities such as end users, assets, services, tickets, knowledge, and operational/usage patterns. Some might even say that there’s too much information trapped within ITSM tools, as industry surveys such as the Service Desk Institute’s “Life on the Service Desk in 2016” report often cite reporting capabilities as the biggest issue ITSM and service desk professionals have with their ITSM tools.

Machine learning can definitely help ITSM professionals to make more of their data, and data patterns, whether in the form of:

  1. Predictive analytics – this could be predicting issues and problems, the risks associated with proposed changes, or even to understand the future levels of customer satisfaction across different types of service desk activities. With two other wider opportunities for predictive analytics being in demand planning and predictive maintenance.
  2. Demand planning – machine learning can be used to predict the future demand for both IT services and IT support capabilities, helping to gauge the required levels of variables such as capacity, stock, pricing, and people levels.
  3. Predictive maintenance – while normally a term associated with engineers rather than IT operations, machine learning offers up the ability to selectively apply maintenance to the IT infrastructure and critical business services to prevent failures. For instance, with the growing adoption of the internet of things (IoT), and the criticality of these network-connected devices to business processes, real-time data and machine learning can be used to predict when devices, and thus processes, will fail and allow preventative action to be taken.
  4. Improved search capabilities – such as intelligent search that knows what was right, answer-wise, for the majority of people previously using similar search terms. It definitely isn’t just the standard search capabilities already used by IT and end users (within their ITSM and self-service tools respectively) – it’s the ability to provide a number of relevant options with a high degree of accuracy. It’s very similar to the next bullet…
  5. Providing recommendations – such as end users already get with Amazon and Netflix in their personal lives. This could be recommended knowledge or solutions for service desk agents, or for end users using self-help, speeding up processes to deliver resolutions or services more quickly.
  6. Identifying and filling knowledge gaps – machine learning not only supports the identification and distribution of knowledge it can also help to create it. Whether it be the identification of knowledge-article gaps based on the analysis of aggregated incident ticket data. Or the conversion of documented ticket resolutions into knowledge, using algorithms to identify the most pertinent and valuable information from which to create a new knowledge article.
  7. Intelligent autoresponders – dependent on the issue type, some tickets could potentially be completed and closed by the technology without human involvement with a high degree of accuracy. It’s a high-value use case scenario of the search/recommendation capabilities above. For instance, when an end user emails their issue in and immediately receives an automatic reply with the most likely solutions for their issue. The solution works, the ticket is closed without the need for manual intervention, and time, money, and inconvenience are saved.

As to when these technologies will hit corporate IT service delivery and support organizations is the wrong question, as these technologies are already here and available to IT departments to help improve service quality, operational efficiency, and the customer experience for end users. Instead the real question is “When will these technologies be the norm for corporate IT service delivery and support organizations?”

Please look out for the next blog in this series, which digs deeper into how having a successful self-service portal can benefit the service desk.

We held a live webinar on January 25th to discuss the impact of consumerization on corporate IT service desks. If you missed it, you’re in luck! You can view the webinar by clicking here.

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Karen Chisholm

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