Artificial Intelligence has been a popular topic among service desk managers for a couple of years now, but companies are trying to understand where and how to invest. This is largely because, depending on their specific service desk strategy, they have a plethora of different paths they could take.
One of the biggest challenges for companies has been managing their service desk tickets. In 2017, the HDI Technical and Salary Report highlighted that 55% of support organizations saw an increase in ticket volume, this number increasing to 61% in 2018. Gartner also stated that the problem for most organizations has been scaling support without increasing manpower in order to manage end-user demand for more support channels. So how can AI support these challenges?
If you aren’t certain how AI can benefit your service desk, the first step you can take is to identify where you can get a quick win with measurable ROI. Easier said than done—but there is hope! Self-service engagement tends to be an area where you will experience high returns on investment quickly by implementing simple solutions.
The list below outlines artificial intelligence technologies that will transform the way your service desk operates.
It shouldn’t be a surprise that chatbots and virtual agents are the first item on our list. For years now, organizations have used live chat as part of their self-service strategy to increase engagement with the service desk. But traditional chat requires a human to interact with a human.
Chatbots and virtual agents can act as a gatekeeper and help “auto-triage” simple requests, allowing your service desk staff to focus on escalated requests that truly require human support. Automating access to FAQs, knowledge, instructional guides and service requests through chatbots and virtual agents is where you want to focus in order to provide employees and customers with non-human interactions that feel like human interactions.
The return on investment here can be calculated via deflected calls to the service desk through:
The rest of our list covers key features needed to make chatbots and virtual assistants work more effectively, increasing your chances of a successful AI project.
Natural Language Processing, or NLP, is a foundational tool for your upcoming AI initiative. For context, NLP is a subfield of computer science and artificial intelligence focusing on language interactions between humans and computers.
The most important aspect of NLP is that it helps AI interfaces, such as chatbots and virtual assistants, better understand human language so they can converse with humans more efficiently and more accurately. For example, let’s say that you decide to order an “8-inch Amazon Fire” tablet using your voice assistant. Without NLP, your assistant might not recognize your request and could even result something completely out of context, such as “setting an 8-in surface of the Amazon forest on fire!”
When it comes to increasing user engagement with self-service or self-help platforms, including with the addition of a chatbot or virtual assistant, you need to ensure that customers will feel comfortable engaging and conversing with these technologies. That starts with these technologies actually understanding what the end user is asking for, in the user’s native language, and providing accurate responses.
To provide the most effective and accurate service via automation, the automated answers and solutions delivered by a chatbot should be targeted to the end user and their environment. Specifically, the bot must understand user context such as their role in the company, direct manager, access to systems, devices that they own or manage and their level of approval, to name a few. This is key to providing a useful and efficient experience.
Selecting a device from a list of all of the devices that are available to employees in the company or selecting scenarios from a knowledge article that have different journeys depending on what building you work in are examples of frustrating experiences for end users that may result in a call to the service desk. Think of it this way—you always expect a human customer service representative to look up who you are, including your account information, when you call for customer assistance. You will not have different expectations with a bot. In fact, you could argue that it should be easier for a bot to query for this information than it is for a human to search your CRM.
Speaking of querying CRM data—giving your end users the ability to “take action” across enterprise platforms directly from a chatbot or virtual assistant will close the loop on value-add artificial intelligence.
For end users, getting answers to questions is their first and most important expectation when interacting with a bot. But they do not want to navigate to a portal or 3rd party application to then use the answer to their question, or to get service. Let’s go back to the example of ordering an 8-inch Amazon Fire. Assuming the bot knows who you are—and helps you to troubleshoot any problems that you may be having with your existing tablet—it should help you order one if you aren’t able to fix the existing tablet.
This is where an integration into your IT Service Management platform would be a significant value-add for self-service engagement. You will be making this interaction incredibly easy for your end user by having the chatbot submit a service request for a new 8-inch Amazon Fire, auto-route the ticket to the correct technician or team, check against any existing stock of the item and provision or procure the item without toggling between applications or making a call to the service desk.
As outlined above, increasing engagement with employees and customers through artificial intelligence does not begin and end with a chatbot. There are many considerations when embarking on this journey. The main areas to focus on are quick wins with measurable ROI, comfortable and contextualized conversation, access to valuable FAQs or articles and allowing for the end user to take action on the answer or solution that they receive, all in a single location.
One more point that we didn’t mention in our list above—make sure that you can report on end user interactions with this technology to ensure you have data for continual improvement. Consider all of these factors and you will see more end users open up to automated support channels, freeing your team up to add more value to the business.
Justin has over 10 years of experience in the IT industry including roles in IT support, IT operations and technology sales and marketing. He has spent the last several years in the ITSM field developing expertise in Service and Asset Management, among other passion subjects such as Agile Methodology and Software Integrations. Outside of work, Justin spends all of his time with his family, improving his golf game and rooting for his favorite college and professional sports teams.