Companies cannot hire quickly enough to meet the IT demand due to talent and skills shortages. In this article, Mohan Kompella, VP of product marketing at BigPanda, explains how AI is key to addressing these shortages and best practices to get the most from human-AI collaboration.
There continues to be an ongoing war for IT talent. This is especially true in IT Ops, where the stress of managing an ever-increasing load of digital services makes it hard to retain great people. If you are in IT Ops, you know it can be challenging to keep your mind off work as the next outage is always looming somewhere.
At the same time, there is also a deepening skills shortage in the global workforce. Companies cannot hire quickly enough to meet the demand due to these shortages. To make matters worse, the U.S. is facing a potential recession that could force IT organizations to do even more with already limited resources. This means that companies will not be able to hire the extra bandwidth they need. This is where AI can help.
Once a major concern that it would take jobs away from humans, AI has proven to be a lifesaver in tech because it helps to lessen workloads, supplement skills and increase resources. As organizations increasingly turn to AI to help augment business processes, it is essential that IT teams use AI effectively to get the most from human and AI collaboration.
Let’s take a closer look at three benefits of AI in addressing these shortages and how it can help organizations do more with existing resources.
Augment IT Team’s Bandwidth
Many IT Ops teams are feeling the pressure of not having enough human processing power to sift through all the incoming data. With cloud migration and modern technology, a large amount of data is generated by today’s tech stacks, and companies do not have enough human processing power to analyze it. With AI, companies can sift through large datasets in real time. It can also help organizations collaborate better across departments. By using AI, IT Ops professionals can gather a holistic view of emerging issues buried within data and reduce disconnects that are typical with manual processes and systems. This is a game-changer in helping companies do more with their existing resources.
The possibility of a major incident or an outage can increase exponentially because you have fewer people managing these issues. When team headcount is flat, and the teams left behind have greater workloads, AI can help augment the bandwidth for those teams and help them continue to serve their customers to keep their revenue stream growing.
For example, the fast-food industry is one that continues to experience talent shortages. They are not able to hire enough workers to meet the demand. If restaurants still have people to serve yet don’t have enough employees, automation can help teams complete mundane and predictable tasks to free up time for human staff. Likewise, in tech, AI can help handle issues before they escalate to increasingly scarce human teams.
Reduce Human Errors When Processing Data
With the potential recession on the horizon, companies are also looking for new ways to control or cut costs. One of the reliable levers that they can pull is increased digitization and increased migration to the cloud because it can help reduce IT spend. However, this also significantly increases the amount of data their cloud resources generate. This is where AI should step in.
Companies have three different sets of IT Ops data. They have machine data that’s being generated by systems, tools, apps and services. It tells you if something is healthy or failing within your IT system. They also have topology data. When you’re in the cloud or the internal architecture, the topology for applications and services tends to change continually, sometimes even by the minute or second. And third, there is a change data set that tracks all the constant shifts in cloud architectures. Every time it shifts, there is a change record that is created. Similar to topology data, humans can’t process or keep track of this data manually.
AI is the only way organizations can take all three datasets, combine them together and analyze them. AI can help predict problems early on and tell you the impact of problems within a system. It can also analyze those change datasets alongside machine and topology datasets. From here, it will tell you which of the changes caused an incident and where and what’s impacted. This is the holistic view AI is uniquely equipped to provide.
The fact is that humans naturally tend to make mistakes or skip steps with manual processes. We ultimately face the monotony of sifting through very large datasets. AI never gets tired or bored when it repeatedly sees the same data set. Properly trained AI has the ability to repeatedly encounter vast datasets in any level of detail and still be able to process them reliably.
See More: How AI Will Transform Career Progression
Increase Collaboration And Streamline Workflows
Human-in-the-loop AI can also help address talent and skills shortages. With this, AI and human teams are able to collaborate together effectively. AI can make suggestions as to what IT decision you should make, and then humans follow up on those suggestions. Both humans and AI work together to do more—both better and faster than humans could do on their own.
However, trust must be at the core of human and AI collaboration for it to work effectively in the long run. This is where explainable AI comes into play. A machine learning model’s predictions may be understood and interpreted with the aid of explainable AI tools and frameworks. Unfortunately, the most common or prevalent implementations of AI happen to be “black box” AI models. This means that AI makes decisions or chooses to operate in a specific way. But because that decision-making is a black box, it can create trust issues with humans.
For example, when it comes to IT Ops, you may have 100 potential incidents on your screen. If the AI suggests you focus on the first three because it thinks that they are the most impactful, humans will want to know on what basis the AI made those recommendations. If the AI focuses on the wrong incidents, it could mean significant dollars in lost revenue. This can be avoided if the AI models in IT Ops are explainable. Not only can you see why incidents were chosen, but it also fosters trust between humans and AI and leads to better collaboration. It will also help more people readily adopt AI, allowing companies to receive a greater return on investment.
AI Has A Critical Role In The Future Of ITOps
IT Ops is a 24/7 role that requires diligent tech talent to help keep organizations running smoothly. AI can help your IT Ops teams to drive the business forward through ongoing tech and skills shortages—and even a potential recession. By strategically using AI, companies can augment team bandwidth, reduce data processing errors, and increase efficiency. Further, it will give IT teams more peace of mind to focus on other higher-value projects that will benefit the business.