Is Your ITSM Team Ready for its First AI Use Cases?
The IT service management (ITSM) industry is awash with articles about artificial intelligence (AI) right now. From the AI apocalypse, the impact on IT jobs, to its magical powers. In the same way that the stock answer to nearly every ITSM-related issue five years ago was “You need a service catalogue”, the 2018 version seems to be: “Oh, AI will be able to solve that”.
There’s no doubt about the potential of AI for ITSM, but organisations need to be careful in how they approach it. Importantly, separating the here-and-now from the near-term future and even longer-term possibilities. Plus, of course, not burying their heads in the sand hoping that it will go away. AI is already here and those IT organisations that fail to understand how it can be exploited will be putting their parent companies at a competitive disadvantage.
So, what do you need to know about the AI here-and-now?
4 Examples of Here-and-Now AI Use Cases
According to “The Global CIO Point of View” report, 8 out of 10 CIOs already use AI in the form of machine-learning technology or have plans to adopt it. With machine learning offering the opportunity for organisations to “drive faster, more-accurate decisions that fuel digital transformation”.
This might sound grand but there are AI use cases that are far more grounded. For instance, the automation of the categorisation, prioritisation, and assignment of IT service desk tickets (or for any other service desk, for example external customer service).
The examples given below use specific vendor solutions, but they can also be achieved using different products and services.
Here-and-Now AI Use Case #1 – Initial Ticket Handling
In applying AI-enabled automation to the categorisation, prioritisation, and assignment of tickets, it:
- Speeds up resolutions/provisioning
- Reduces handling costs
- Reduces human errors, and
- Improves the customer experience.
And it’s definitely a here-and-now use case, with the ServiceNow customers that piloted its “Agent Intelligence” capability reporting an 8% saving of their service desk’s time through the automated and accurate categorisation, prioritisation, and assignment of tickets.
You can read more on this AI use case, and a lot more about AI, in this blog by Simon Morris: “Machine Learning and The Third Way of Work”.
Here-and-Now AI Use Case #2 – Automated Level 0 Support
The use of AI for Level 0 support, via chatbots (or virtual agents), is already here in consumer-world support-channel scenarios – with companies such as Amazon to British Gas. The technology works (if implemented correctly). So, there’s no reason why it shouldn’t be put to work in internal IT support.
But, as with IT self-service, as well as creating a capability that’s suited to user needs, there’s also the need to apply organisational change management (OCM) tools and techniques in order to address potential issues with what’s ultimately a change in the way of working (and thus a people-related change).
If you’re a ServiceNow customer, then an ITSM chatbot solution is already available via the IBM Watson Conversation service. This integration provides a ServiceNow chatbot capability today, with a new native capability incoming thanks to the acquisition of Qlue – a provider of an AI-enabled conversation management capability.
Either way, your IT service desk could be using virtual agents right now to:
- Offer 24×7 (and automated) support
- Respond more quickly to issues and requests
- Deliver a better customer experience
- Deflect tickets from the service desk, freeing up support staff for more intelligent work
- Reduce costs.
Here-and-Now AI Use Case #3 – Knowledge Management
Knowledge management has been around for a good two decades but it’s something that organisations, not just IT service desks, continue to struggle with – from the capture of knowledge through to its effective use and reuse. AI can definitely help here.
It might seem an odd thing to state, with knowledge perhaps viewed as a very human thing – people sharing what they know to help other people (who in turn might help other people). But there are already AI-enabled knowledge management solutions on the market (and in use) that finally allow organisations to reap the benefits that have been long promised through the power of knowledge sharing.
Examples of this AI use case are already provided by companies such as Kaleo Software, and include:
- Intelligent search capabilities – that not only understand the context and meaning of the search terms used, but also what was right, answer-wise, for similar previous searches.
- Intelligent autoresponders – where inbound emails are logged, actioned (by the automated provision of the most-probable solutions) and closed by the technology without the need for IT staff involvement. Think of this as a virtual agent that comes to your customer’s inbox rather than requiring them to search out the available help access points.
- Automated knowledge-gap identification – based on the analysis of aggregated incident ticket data. This capability highlights both missing knowledge articles and existing articles that aren’t working as well as they should, i.e. common issues that are still hitting the service desk.
- Automated knowledge-gap filling – machine learning not only supports the identification and distribution of knowledge it can also create it. Converting documented ticket resolutions into knowledge articles, identifying the most valuable knowledge “nuggets” for the prospective knowledge user.
Here-and-Now AI Use Case #4 – AIOps
It’s a somewhat fancy name for something that Gartner describes as the combination of Big Data and machine learning functionality to support IT operations. Making it easier for IT operations to handle the increasing number of technology-related alerts.
As with the above use cases, AIOps uses AI to remove manual activities, delays, and costs. An example AIOps solution is Moogsoft AIOps, which uses algorithms to automatically cluster and reduce event management alerts (and the associated operational “noise”). It also offers predictive capabilities, proactively detecting problems and diagnosing root causes.
What These Here-and-Now AI Uses Cases All Have in Common
An important thing to note, re where AI for ITSM is now, is that each of these use cases is narrow in nature – in that the technology solves a specific ITSM issue or opportunity.
So, the technology readily available today, while offering significant opportunities to ITSM teams, does not necessarily replace whole roles (or people). It replaces specific tasks.
As the technology and its productization advance, this will of course happen. But don’t sit around waiting for this to happen, if you do you’ll probably be costing your business more than the patience/delay saves. Instead take advantage of what’s already available today, of course where it’s beneficial for your organisation to do so.
As to where your organisation is best to start, evaluate the routine tasks your teams perform, where needed create key performance indicators (KPIs) and baselines to measure their current efficiency, and then look for some quick wins that will help to build the business case for introducing AI. Once the AI is in place, and making a difference, you can then demonstrate how effective it is and strengthen the argument for automating even more tasks.
I’ll be on stand 460 at SITS if you would like to discuss AI or any other ITSM topic.