With automation technologies being applied to business processes across organizations, society is at the dawn of a new era, akin to what we saw in the industrial revolution. But while there is so much more to come, is the level of favor and interest dropping? One can quickly see an incredible surge in the automation space. Still, some of the business applications are clouded by noise like an autonomous van in an accident on its first day, or a smart suitcase fleeing its handler.
So what does the future look like? The path might be rocky, but there is some promise of good things ahead, especially when we move the lens toward the IT teams facilitating AI’s innovation.
Automation Technology, Like Machine Learning, Is Bracing for Backlash
This is not a new experience for companies that work in emerging technologies. Automation is facing a similar gauntlet of expectations and backlash that all innovative products have seen. Gartner’s famous “Hype Cycle” illustrates this best.
As technology first enters a scene, it makes noise in the space, described as a “trigger.” This is where it secures initial headlines and investors take notice. The buzz and attention, while exciting at first, create an overly optimistic atmosphere that travels all the way up into Gartner’s “peak of inflated expectations.”
This peak is where reality sets in, and some may argue that automation technology finds itself at this very point. Here is where the backlash and criticism land with a heavy hand, causing some brands to actually fail. But if the technology has managed to overcome the negativity, it enters a time of level-setting and more manageable expectations.
Yet, there is a unique situation here facing automation technologies like AI and ML. A second failure option presents itself: with automation, results are rapidly shared and it’s difficult to determine why that specific result had been generated. This can be a point of frustration when attempting to troubleshoot and is especially problematic when the technology leaves the lab and is placed in real-world environments.
The IT Department Finds Agility and Innovation in Purpose-Driven Solutions
When working with large-scale enterprises, software companies are being put into a robust ecosystem of hybrid environments and existing workflows. It’s asking a lot for a company to rip out systems previously put in place and, in general, implementing a product that adds more headache isn’t going to win a customer. So, is AI and ML worth the hassle? Despite the obstacles, automation technology is proving itself each day in the IT department. It’s doing so in a way that assists an enterprise’s current ecosystem and eliminates common pain points. One reason for this is that, to be successful, automation systems need data. Both quantity and quality matter, as they need to be trained with the information to make accurate assessments.
IT infrastructure generates huge quantities of data and, while formats are diverse, data is already machine readable. This obstacle has set the foundation for “AIOps,” (Algorithmic IT Operations), coined by Gartner Research in recent papers by analyst Colin Fletcher. Furthermore, Gartner reports that by 2020, approximately 50 percent of enterprises will be actively using AIOps platforms to provide insight into both business execution and IT operations, which is an increase from fewer than 10 percent today.
The current state of IT is this: teams of undervalued people in a dark corner of the office, away from everyone else, who are simply expected to keep the lights on and respond to every single IT problem. With automation and machine learning, the goal is not to eliminate people from the workforce, but to give them the chance to be more strategic and provide better customer experiences for their colleagues and customers. IT professionals can focus on what actually matters — like providing business value — rather than spending countless hours trying to make sense of endless service tickets.
Feature image via Pixabay.