The idea of automation is probably as old as mankind. Back in Ancient Greek, Homer was the first to use the word “automation” to describe automatic movement of wheeled treepods. Then, the advent of the First Industrial Revolution with its mechanized manufacturing and factory systems started to make a significant impact on peoples’ lives.
Fast forward to today, we now see advanced technologies and AI-driven automation permeate every aspect of our life and work. From warehouse robots picking our orders to chatbots doing customer service to smart homes taking care of our routines, the list can go on. The Covid-19 pandemic has shown the detrimental effect of business disruption and created an even stronger incentive to automate.
And as the global economy rebounds from the pandemic, many employers are not rushing to hire workers back but continue to step up their automation game. In fact, according to PwC analysis, up to 30% of jobs could be automated by the mid-2030s. But while machines are replacing humans across a variety of industries, what about the IT industry — the industry that deals with creating and programming machines?
IT jobs that can be automated
The general rule is that any process that is monotonous, repetitive, and time-consuming is a perfect candidate for automation. Keeping that in mind, here’s our take on jobs that can and should be automated.
1. Data entry clerks
Data entry is a manual process of extracting and entering valuable information into a company’s system. This is a tedious procedure that requires a great deal of concentration and immense amounts of time. According to Gartner, human data errors in finance-related works lead to 25,000 hours of rework at a cost of $878,000 per year. That’s why data entry automation is a perfect solution for businesses that don’t want to waste money on hiring a data entry clerk or outsourcing the process. By leveraging techs like optical character recognition and robotic process automation, automated data entry software quickly extracts data from a variety of sources, sorts it and presents it in an easy-to-understand way.
2. Database administrators
A database administrator (DBA) is a person responsible for managing and maintaining a highly performant and secure database environment. But up to 80% of DBA’s time is spent on manual administration tasks that can be easily automated. In addition, modern cloud databases like Oracle use machine learning to redefine database management. From automated provisioning to dynamic scaling for specific workloads, these cloud databases seamlessly handle all administration tasks to help developers build and deploy applications rapidly and cost-effectively.
3. Tier 1 IT support specialists
In general, there are three tiers or levels of support, with each tier handling specific issues and requests. Tier 1 technical support provides triage of support tickets and deals with the most simple and common problems of users. And since these issues are repetitive, they can be resolved using intelligent automation, for example in the form of AI-powered chatbots. This way, tech support specialists will have more free time to focus on more complex problems that require in-depth incident analysis and critical thinking.
Source: Youtube
IT jobs that cannot be automated
Since RPA and AI-driven automation can easily take over repeatable, mundane and high-volume IT processes, what responsibilities will remain?
The short answer is a lot of them. Everything that requires a creative approach, interpersonal communication skills, and out-of-the-box thinking is difficult to automate.
1. Software engineers
On July 13, 2020, Sharif Shameem, Debuild CEO, announced that he used the GPT-3 tool to build a program that allows users to automatically create a website by describing how it should look and work.
Although it sounds impressive, does it mean that AI-driven automation is taking over programming? In fact, it doesn’t. Tools like GPT-3 are currently able to produce rather simple code that is not free of bugs that can range from trivial to catastrophic. When Hammond Pearce, a computer engineering professor at New York University, was studying Copilot, another AI-based pair programming tool, he discovered that 40% of the code generated was vulnerable to malicious attacks.
Only human software developers can precisely understand specifications and business requirements, prioritize tasks and features, and ensure that the code is of high quality and up to the security standards.
2. Designers
Fundamentally, design is about conveying meaning and adding value through color, shape, technology, and functions. A good design is always human-centered as it focuses on users’ needs and ways to solve complex problems. And since machines will never be truly human (or at least not in the foreseeable future), high-quality design is hardly achievable.
That said, it doesn’t mean that AI-driven automation is of no use to designers. On the contrary, there are many repetitive and time-consuming tasks that can and should be automated so that a designer can get down to the nitty gritty of creative work. These tasks can be as simple as removing backgrounds or resizing in bulk or more complex like creating layout options and product mockups. There are even tools like Wix, aOne example of such automation is an AI website-building platform, that analyzes best performing sites and then uses this data to create custom websites tailored to unique user needs.
3. Manual QA engineers
With the recent advancements in automated testing, it’s very easy to buy into the hype of thinking that it will soon make the job of manual testers obsolete. This is however far from the truth because despite the many benefits of automated testing, it basically boils down to running pre-written test scripts over and over again without stepping off the path.
Manual testing brings immense value to a project as there is nothing like inductive reasoning and human intuition to pick up on any inefficiencies, flaws, and defects. Manual QA professionals are much better at exploratory testing and catching new things, assessing product usability and accessibility — something that automation can’t truly deliver yet.
4. Cybersecurity experts
Today’s threat landscape is rapidly evolving and expanding, with dozens of thousands of cyber attacks happening every day. To stay on top of them, cybersecurity experts often turn to automation — according to Statista, 35.9% of responders rely on a high level of automation for security operations and event or alers processing.
Level of IT security automation in organizations worldwide 2021
Source: Statista
However, embracing automation does not mean removing cybersecurity experts from the equation but empowering them with the right tools to be more productive in their jobs. In fact, a recent research by Exabeam showed that 80% of cybersecurity professionals aged 45+ believe automation would simplify their work. But human input is still vital for a number of cybersecurity tasks, from validating alerts to performing penetration testing.
5. Video game developers
Game development is one of the fastest-growing sectors in the world, and in no small part due to the pandemic. Between 2019 and 2021, the global video games market expanded by 26% as people were battling lockdown isolation. And by 2026, the global video games market is expected to be worth $321 billion.
Modern video games are also becoming increasingly sophisticated and detailed. Designing characters, narratives and entire worlds require human input and no automation technology at the existing level is able to accomplish that. However, intelligent automation is already used to generate adaptive behavior of non-player characters, create dynamic maps, and more.
The bottom line
Automation and RPA are moving full steam ahead, but it’s not all doom and gloom for tech jobs. As we have clearly seen, AI-driven automation is gradually changing the IT jobs landscape, streamlining most mundane and time-consuming processes while also laying the foundation for greater productivity and efficiency. What is needed today is for companies to start investing in workforce upskilling and learning to make the most out of intelligent automation and prepare for the future of work.
