3 Key Considerations to Make When Evaluating AI for ITSM

12/21/2017 by: Jaime Spector

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Since even before the internet revolutionized computing and communication, the prospect of Artificial Intelligence (AI) captured the imaginations of those who looked toward the future. While AI may still in many ways seem relegated to far-flung sci-fi fantasy, these days you can’t start up an app or internet-enabled device without interacting with some form of computer intelligence.

Last year, Google even laid out their vision to move toward an “AI-first” world, where computing is less tied to physical devices, and shifts towards something more omnipresent. “AI” as a proven method for powering automation, innovation and business intelligence has arrived, and software vendors everywhere are touting their platform’s AI capabilities.

However, despite lofty claims from CEOs and a few “Now with AI!” checkboxes checked, are we really there with AI? Will AI help your IT organization meet critical business goals today? We believe the answer is a resounding yes, however, only if careful considerations are taken when evaluating AI for ITSM. Here are three things to keep in mind:

1. Learn the Lingo

AI is a deep, complex topic in both the computer and cognitive sciences, and to the layman, it can be quite difficult to navigate. Analysts, product engineers, vendors, marketing, etc., love to use the technical terminology associated with AI in order to better describe the impacts it will make to the organization. It’s very important that you have at least a cursory understanding of some the subject’s most used terms (at least applied to your industry). Here are some key terms to know (as defined by Gartner):

  • Artificial Intelligence (AI)Artificial intelligence is technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people, enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of nonroutine tasks. Applications include autonomous vehicles, automatic speech recognition and generation and detecting novel concepts and abstractions (useful for detecting potential new risks and aiding humans quickly understand very large bodies of ever changing information).
  • Machine Learning (ML) - Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks and natural-language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.
  • Deep LearningA modern variation on Machine Learning that can accomplish routine, “humanlike” tasks such as converting text to speech, recognizing and classifying images, prioritize and order data, and even drive cars.
  • Neural Network - A neural net or neural network is an artificial-intelligence processing method within a computer that allows self-learning from experience. Neural nets can develop conclusions from a complex and seemingly unrelated set of information.
  • Natural Language Processing (NLP) - Natural-language processing (NLP) technology involves the ability to turn text or audio speech into encoded, structured information, based on an appropriate ontology. The structured data may be used simply to classify a document, as in “this report describes a laparoscopic cholecystectomy,” or it may be used to identify findings, procedures, medications, allergies and participants.
  • ChatbotsApplications that act as “virtual chat agents” that directly interface with users in a humanlike fashion. They often use ML and NLP to intelligently understand user queries and can accomplish tasks or return answers based on connected data sources. Chatbots are often used to automate support or act as assistants for automating routine tasks.

These are just a handful of terms, but it’s important that understand their meanings in order to gauge whether an ITSM vendor’s AI capabilities make sense for your organization. This brings us to the next point.

2. Decide If You Can Take Advantage

The potential of AI and its power to disrupt enterprise computing is undisputed. However, AI solutions come in various forms, all with different associated layers of complexity. A vendor hyping their latest and greatest innovations in deep learning algorithms may not function properly for an organization that lacks the maturity and datasets to effectively support the system. Training is also another critical component as well. SMBs may find implementation of AI systems that demand time and resources a cost that is too much for them to manage.

Just like other requirements, factor in AI as a part of your ITSM tool replacement RFP. Conduct a MoSCoW method analysis (Must have, Should have, Could have, Won’t have yet) to flesh out your needed requirements when evaluating AI for ITSM, and decide if your organization is able to obtain a high enough ROI for the business based on your decision. This is a step that should not be overlooked.

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3. Consider the Use Cases

This is a big one. Sure, it’s fun to talk ad nauseum about the potential and technical underpinnings behind AI, but it’s all for naught if it can’t produce actionable results for your IT organization. That’s why it’s important to consider the real-world use cases for how the intelligence functions within the ITSM tool or application, and what business value it brings to the table. This can be the true difference-maker when gauging the effectiveness of a vendor’s product offerings; helping you to determine what’s real and what’s all hype.

For example, if you’re evaluating an ITSM vendor solution that includes AI, you should be able to walk away from that demonstration with a clear idea of exactly which features use the AI, how it works, and how those features will deliver tangible business value. Be wary of vendors that claim to be “AI enhanced” but don’t offer much clarity in terms of what features can currently take advantage of it or how they plan on refining it.

Like many other technologies, AI requires long-term investment and commitment from the developers in order improve its functionality over time. Look for the real deal and push back against those simply jumping on the bandwagon.   

These are just a few things to keep in mind when considering an AI-powered ITSM solution. Have anything to add? Leave us a comment below!

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