The Case for Putting AI and ML to Work in the IT Department
February 23, 2018

Phil Tee

Share this

With 2017 behind us, the news cycle is still stirring up stories on artificial intelligence (AI) and machine learning (ML), but has some of the excitement worn off? We're witnessing a surge of activity in the space, with unexpected names like Ferrari throwing their hat into the ring, or some notable failures like a smart suitcase fleeing it's handler. Can actual examples of AI in the enterprise rise among some of the noise that's inundating the market and hindering the credibility of everyone?

What Comes Up, Must Come Down

This has happened before. Emergent technology faces a gauntlet, and Gartner's famous Hype Cycle model can help illustrate this point.

First, technology makes waves with a “trigger” that garners media attention or investor buy-in. This usually creates an overly-optimistic projection of what's possible, which aligns with Gartner's “peak of inflated expectations.”

At the top of that hill is where people start to question whether the technology that was introduced is truly capable of delivering what it claims. This is where harsh criticism hits from multiple angles, and the negativity can be so strong that some companies or technologies actually fail at this point.

Yet, if the technology weathers the storm, there is now a time for level-setting and a more realistic understanding of the market. We're seeing that artificial intelligence and machine learning are two technologies at various points in the hype cycle, but both will follow a similar path.

What's unique here, however, is that, due to the nature of AI and ML, there is a second failure option. With this type of technology, results are rapid and it is generally not possible to determine why a specific result was generated. Another frustration is that the speed of execution usually creates the inability to troubleshoot failures in detail. When put into a real-world environment outside of general deployment and testing, potential problems that might not have been obvious in the lab start to appear.

We can point to some failures — like a smart fridge misreading expiration dates or smart thermostats mistaking temperature readings — but these are minor compared to what else can happen. We've seen this in racist algorithms or facial-recognition incorrectly misidentifying someone at the scene of a riot.

But There is Proof it Can Work, Just Check Out the IT Department

So, is AI and ML worth the hassle? Has it hit rock bottom on the hype cycle without anyway to pick itself back up?

AIOps has a huge potential to transform IT and help streamline enterprise operations

Despite the obstacles, AI is proving itself each day and is key for a better future. Experts in the field are taking note of what works, and we've found that, to be successful, AI systems need data. Both quantity and quality matter, as they need to be trained with the information to make accurate assessments. A challenge today is not that data isn't available, but at times it's difficult to access, analyze and organize … yet the IT department has found itself an ideal center of operation by using the technology for “AIOPs.”

IT infrastructure generates data by the second, and while formats are diverse, data is already machine readable. Thankfully, it turns out that computers are already translating information from one representation to another.

Once this data is applied to an AI or ML system, it can apply various algorithms to try to make sense of them. This means it's seeking what information is significant or what is interconnected in the vast resources — something that an IT team currently does manually at the cost of countless man hours. AI and ML systems can take these huge swaths of data and order them in near real-time, focusing IT teams on what is truly mission critical. Not only does this free up valuable man hours of the IT team, but it elevates them to expand their daily work into new activities that can enhance the overall agility of the enterprise, rather than acting as a constant ticket desk.

AIOps has a huge potential to transform IT and help streamline enterprise operations, by presenting human specialists with actionable events, helping them collaborate more effectively, and learning and improving over time.

Phil Tee is CEO of Moogsoft
Share this

The Latest

March 16, 2018

The State of the Mainframe report from Syncsort revealed an increased focus on traditional data infrastructure optimization to control costs and help fund strategic organizational projects like AI, machine learning and predictive analytics in addition to widespread concern about meeting security and compliance requirements ...

March 15, 2018

The 2018 Software Fail Watch report from Tricentis investigated 606 failures that affected over 3.6 billion people and caused $1.7 trillion in lost revenue ...

March 14, 2018

Gartner predicts there will be nearly 21 billion connected “things” in use worldwide by 2020 – impressive numbers that should catch the attention of every CIO. IT leaders in nearly every vertical market will soon be inundated with the management of both the data from these devices as well as the management of the devices themselves, each of which require the same lifecycle management as any other IT equipment. This can be an overwhelming realization for CIOs who don’t have an adequate configuration management strategy for their current IT environments, the foundation upon which all future digital strategies – Internet-connected or otherwise – will be built ...

March 13, 2018

Many network operations teams question if they need to TAP their networks; perhaps they aren't familiar with test access points (TAPs), or they think there isn't an application that makes sense for them. Over the past decade, industry best-practice revealed that all network infrastructure should utilize a network TAP as the foundation for complete visibility. The following are the seven most popular applications for TAPs ...

March 12, 2018

Organizations are eager to adopt cloud based architectures in an effort to support their digital transformation efforts, drive efficiencies and strengthen customer satisfaction, according to a new online cloud usage survey conducted by Denodo ...

March 09, 2018

Globally, cloud data center traffic will represent 95 percent of total data center traffic by 2021, compared to 88 percent in 2016, according to the Cisco Global Cloud Index (2016-2021) ...

March 08, 2018

Enterprise cloud spending will grow rapidly over the next year, and yet 35 percent of cloud spend is wasted, according to The RightScale 2018 State of the Cloud Survey ...

March 07, 2018

What often goes overlooked in our always-on digital culture are the people at the other end of each of these services tasked with their 24/7 management. If something goes wrong, users are quick to complain or switch to a competitor as IT practitioners on the backend race to rectify the situation. A recent PagerDuty State of IT Work-Life Balance Report revealed that IT professionals are struggling with the pressures associated with the management of these digital offerings ...

March 06, 2018

Businesses everywhere continually strive for greater efficiency. By way of illustration, more than a third of IT professionals cite "moving faster" as their top goal for 2018, and improving the efficiency of operations was one of the top three stated business objectives for organizations considering digital transformation initiatives ...

March 05, 2018

One of the current challenges for IT teams is the movement of the network to the cloud, and the lack of visibility that comes with that shift. While there has been a lot of hype around the benefits of cloud computing, very little is being said about the inherent drawbacks ...