Recent advancements in the world of technology automation, like OpenAI’s ChatGPT and DALL-E2, Amazon’s Bedrock service and You.com’s YouChat, have been impressive for increased productivity, efficiency, and more.
We’re witnessing a general transition, thanks to machine learning and Large Language Models (LLMs), where tasks that once required hours to complete can suddenly be done in a few minutes. This increased productivity leads to cost savings and frees up staff to work on higher-value tasks, thus accelerating innovation.
Automation normally requires an upfront investment, but in the long run, it pays off and provides greater long-term gains, competitive advantages, and better customer experience.
Take an IT Help Desk support as an example. Often an employee ends up losing a significant amount of time while solving a trivial issue. Waiting times, inconsistent quality of support, inability to find the right solution or answer – all of us have experienced this many times. Properly built automation solutions reduce the amount of friction, eliminate waiting times and inconsistencies, and increase information discoverability and support availability to 24/7. Computers also happily perform the most tedious tasks without suffering emotional or physical fatigue.
Another example of an IT job that is ripe for automation is web development and basic coding jobs. With the rise of low-code and no-code development platforms such as Zoho Creator and Appian, it is becoming easier for non-technical users to create websites and web applications without the need for specialized IT skills. In addition to that, latest advancements in LLMs trained on immense volumes of publicly available code made it possible for the model to write code based on the description of the objective provided in a form of natural language. One of the most popular tools built for that purpose is GitHub Copilot.
These automation examples are often discussed in two facets: optimization and potential job loss. The most vulnerable to automation jobs in our modern economy, and this is not unique to IT jobs, are those that involve routine and repetitive tasks that one can do almost automatically or by strictly following some sort of a manual.
When facing an at-risk job
If an employee in an at-risk IT job wants to transition to another type of IT job before automation replaces their current role, there are several steps they can take:
- Do not run from it. Face automation head-on. Ask yourself, what can you contribute beyond your current responsibilities? How can you transform the process and make customer experience better? The greatest ideas come from the people “on the ground.” You can apply your experience to define the future and transform your role yourself.
- Learn how to maintain, use and improve the very automation tools that are targeted at your job’s automation. The natural shift in your role would be either towards the maintenance of those solutions or complementary to what those solutions can do, e.g., resolving more complex matters and edge cases.
- Learn more about the company you’re working for or the industry you’re in. What trends are you observing? What skillsets are in high demand? What else would you enjoy doing and would be good at? This exploration will help you decide what skills you’d need to acquire. Leverage online learning platforms to start working on your new skills.
- Build your network: go beyond your immediate team and management chain. Attend industry events, join professional associations, and connect with other professionals. This will help to get a bigger picture of what’s happening in your industry and provide more learning, employment, and mentorship opportunities.
Overall, transitioning to a new IT job requires planning, effort, and dedication. By taking these steps, at-risk IT employees can increase their chances of finding a new role that aligns with their skills and interests before automation replaces their current job.
What is the role of the employer?
The responsibility to help redefine existing roles also partially lies with the employer itself. Listening to the employees in at-risk jobs and giving them the opportunity to accelerate their learning and growth towards a different kind of role should be part of any automation initiative and overall career development path that the company has to offer.
With the recent advances in Conversational AI, for example, a number of solutions emerged to automate the routine part of customer support jobs by deploying virtual assistants as a first line of support, then escalating the case to a human agent when necessary. As the underlying technology matures and becomes more powerful and easier to maintain and deploy, the barrier of adoption lowers. More and more companies become comfortable adopting this automation, resulting in many jobs being redefined or shifted towards a different problem area. At the same time, these virtual agents will become better over time and capable of performing more and more tasks autonomously, thus constantly reducing the amount of human intervention required to complement their imperfections.
In this article, we’re talking about automation of routine jobs, but in a few years from now, we’re going to see automation transforming highly skilled professions like software engineers and data scientists. The same principles outlined above stand for any profession and any level of expertise. We’re responsible for our own growth and development, and we need to be aware and prepared to learn and teach others to adjust to a very quickly changing landscape of IT. Every automation decision needs to come with a plan of how to “upskill” or “shift-skill” existing jobs, and how to quickly translate this changing demand into new education tracks and strategies to ensure our education system keeps up with the change and prepares young professionals with the right arsenal of knowledge for this quickly evolving environment.
It’s important to note that while these jobs are vulnerable to automation, they won’t be completely replaced by machines. Automation may lead to a shift in the nature of these jobs, with IT professionals needing to develop new skills and adapt to new technologies in order to remain relevant in the workforce.
About the Author
Diana Mingels is head of machine learning at Kensho. Kensho is an Artificial Intelligence company that builds solutions to uncover insights in messy and unstructured data that enable critical workflows and empower businesses to make decisions with conviction. With engineers comprising over 75% of our ~100-person team, Kensho is an engineering-driven culture, laser focused on building products that solve our customers’ complex problems. Kensho is headquartered in Cambridge, Massachusetts.
