AI is an exciting topic within IT Service Management (ITSM). It has the potential to unlock new efficiencies, services, and capabilities in any organization. But it is not without its own risk, misinformation, and problem. We have come up with 5 steps to get your ITSM ready for AI and make sure the process will be as smooth as possible, and you will see the benefits in no time.
AI or artificial intelligence is a complicated topic that will often require some form of outside help. But that doesn't mean that you can always buy the help you need. It is a relatively common misconception that data and statistics are the ultimate truth. When considering AI, you will need a data scientist, period. You might even need help with your process, security, technologies, and other areas. The best advice is to expand your AI-related knowledge because even having a basic understanding will help you ask the right questions surrounding development, security, and more.
Everyone knows that data is king when it comes to AI. You need a substantial amount of data for any AI feature to work correctly. But one topic that often gets pushed to the side is data quality. It is great to have one million incidents (or any other data sources) to train your AI. But if every resolution text is "solved," how beneficial will that be to agents or users? When it comes to AI and data quality, the adage "crap in, crap out" rings true.
Even with a perfect and complete data set, you will need someone who understands what they are seeing. If you are not one of the fortunate souls with an excellent data set, a data scientist can help identify the underlying problems. By combining this knowledge with process consultation, you will have clear guidance on the changes needed to remedy your core problems.
Typically, when organizations work on a new and exciting endeavor, everyone is looking for benefits. Still, they often forget to evaluate a project's value. Benefits and values often get used interchangeably, but they have very different meanings. You should introduce additional measurement points as early as possible to measure the impact of your AI implementation on other areas as well.
AI can often have underly effects that have on a particular process or area. In the case of incidents, you might be looking to reduce resolution times. After adding AI, you don't see a statically significant drop, and you start wondering what the issue was. But if you had been tracking the number of status changes, category changes, comments, or another area that affects the total work relating to an incident, you might find something different. Perhaps your AI has been helping to reduce these changes or work, which should ultimately reduce processing time or, at a minimum, decrease the "work time" of an incident.
AI has the potential to unlock a wide range of services and efficiencies within any organization. These could include reducing workloads, improving first pass resolution, or reducing the number of category changes. When starting such a large project, it is crucial to have laser focus to ensure that the project won't suffer from scope creep or a never-ending expansion until the project is finally completed way over budget and addressing everything except for your original goals.
Remember that your first few AI endeavors will be more of a pilot project scoping out technologies, capabilities, and areas improvement areas. When you think of it in these terms, it makes sense that you will start small to allow proper testing, adjusting, reporting, and reviewing before expanding.
AI is inherently risky, just like any IT or ITSM project, technology, or system. But that doesn't mean that you need to be afraid of AI. With proper knowledge and a basic understanding of how your AI system will be used, what data it needs, and how it will store and analyze it, you'll be on the right track. With proper planning, testing, and documentation adding AI to your ITSM isn't any riskier than adding any other system to your IT arsenal.
Another risk to consider, not surrounding data security, is training-related. When will the AI be trained and retrained? How long does it take to retrain the system? Who decides to retrain the algorithm? AI is only as smart as the data it uses and the people who help to train it. You will need to ensure that you are feeding the algorithm good data and coaching before expecting it to do the same.
AI and ITSM don't need to be a daunting or scary topic. It is already here and only will grow in the future. With proper planning, goal setting, and an honest evaluation of your capabilities and needs, any organization can add AI to its ITSM system. If you would like to find out more tips for excelling with AI in ITSM, be sure to check out our guide where we go into the areas of data and organizational maturity, value, direction, and risk in more detail.
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