Artificial intelligence (AI) has become a matured concept, the excitement of which is bearing results for a number of sectors and it holds a good deal of promise for a bright future. AI is the act of making possible for machines to learn from “experience” while adjusting to new input and stimuli so they can perform human-like tasks, ultimately leading for more complete automation in many traditionally man-powered processes. Computers can be “trained” to accomplish specific tasks, process data and recognize patterns in the data.
While the use of service management solutions escalates, so do the potential uses for AI approaches in the sector. Because of this, there are advantages to discuss as well as examples of how AI can help to improve the service management operations of your organizations. But AI is a broad and far-reaching concept that’s often misunderstood and difficult for those that don’t work with the technology to understand its full capabilities.
Is AI All Hype or Is It Actually Helpful?
Based on the 2018 Interop ITX AI: Hype or Substance report, in a survey of 182 technology professionals, almost two-thirds of these respondents’ organizations are currently working on AI products or evaluating the technology. However, only 12 percent are using AI in production today. For those who responded during 2018, 6 percent of those using the technology said their budgets for it were set to decrease. The outcome, per the report, is that AI interest is high, but the ability to execute is low.
Are AI Expectations Too High?
The return on investment of AI is not clear and nearly one-third of organizations say they lack the budget they need to move forward, Information Week reports. Additionally, despite the desire for AI initiatives to reduce manual processes, these tasks must still be carried out by talent in the organization. A lack of expertise is often cited as a barrier to AI initiatives because there is not enough expertise to push the efforts forward. Alternatively, of those surveyed, more than a quarter said they haven’t defined a business case yet and close to half said they “have no idea” what effect AI may have on their businesses.
Part of the problem with AI is the broad nature of its concept. Much of what we thought we knew AI was is now standard practice. For example, early AI efforts like IBM’s chess computer, Deep Blue, is part of our computing world now. Currently, demand or desire for AI capabilities is still aspirational, but usually is conflated with “machine learning”—where a computer or programming provides examples for how a machine can “learn” how to complete said task. Machine learning has huge potential to help societies automate previously manual systems. Through this, new economies will be developed and work (and workers) will evolve. Certain jobs, eventually, will become minimized and even discontinued in favor of new process and “ways of doing things.”
We have seen these cycles before. In the United States, from a macro level, there’s been a major shift from a farming/agricultural society to one of more urban-based manufacturing/industry; into the post-industrial; then global-focused; and web-connected economies. These can be drilled down further but the point is made—major variations in our economies and jobs have taken place in a relatively short period of time. Based on these changes, we’ve had to change; jobs and workers have had to change. Machine learning is another disruptor for the new global economy that will force change.
The hype of machine learning—a technology with huge potential—may not actually be overhyped. That said, how will this technology actually impact our everyday lives, and will machine learning change the work we do in service management? Perhaps we’re now starting to get a better idea of what those realities may look like.
As far back as 2017, Gartner said that machine learning hype peaked, but expectations remain high. For service managers, there’s going to be more than intelligent chatbots to worry about but for now, much of the anticipation likely will continue. Machine learning can be a beneficial tool for identifying trends and changes in trends. In service management, machine learning means quick responses can help recognize when there is an above average number of calls logged, for example, or an unusual number of calls within one category, which can be acted upon.
For most pressing trends—a specific cloud service that may no longer be available, for example—meaning machine learning-driven technology can recognize this breakdown by sending a signal to a specialized member of the service team, who can then react to this issue. Long-term problems also can be addressed through machine learning technology, spotting urgent disruptions. This may be particularly important during problem management. A labor-intensive process, machine learning has the capability of being able to recognize patterns in these calls, making the discovery of structural problems much easier.
Misconceptions About Machine Learning
Misconception surrounding AI and machine learning is rampant from replacing most of our jobs and replacing massive amounts of humans in the workforce to their surpassing our pre-programmed uses for the technology, in which the machines rise up and enslave us all. The more realistic version of things is a bit more benign. The most common current misconception is that chatbots and virtual assistants will make service desk employees redundant in short order. But this won’t likely be the case for the better part of a half decade or more because the technology isn’t sophisticated enough. Most chatbots are pretty expensive and don’t always work well.
For now, machine learning will likely only help “answer” direct questions more quickly. More complex issues must still be handled by people. The realized benefit here is that because machine learning technology can help solve the easiest questions service desks face, there’s more time for service desk team members to solve the more complex calls.
Someday Soon …
Soon, machine learning technology will become better at finding the right information and offering solutions that actually benefit humans. Then, service desk employees will be able to focus more on the overall customer experience in ways that machine learning likely never could.
While AI is maturing, excitement surrounding this experiment may need to be tempered for a bit. The technology is in the act of making possible for machines to learn from “experience” while adjusting to new input and stimuli so they can perform human-like tasks, but more complete automation in many traditionally man-powered processes is still needed.