Introduction
At the beginning of your professional life, you may be in an early stage. Here we will discuss how artificial intelligence can impact the way you learn. In the 1980s, when Alexandra Samuel began her career, she followed what was at that time one of the few pathways available for women seeking to work in white-collar jobs: secretarial positions. In high school, her mother urged her to learn typing, and Samuel spent her high school and college years producing correspondence, reports, and memos. She said, “While I was working in those jobs, there were little bits of things that weren’t secretarial that I was exposed to, until I reached the end of college and had a resume that showed I was capable of landing a small professional job.”
However, in today’s technology-focused offices, this type of secretarial role has completely vanished. Samuel stated, “There was a lot of paper in the world I grew up in, and paper is no longer very important. That wiped out a whole category of entry-level work.”
Today, with the emergence of artificial intelligence, it is not just one category of entry-level work that is being disrupted, but hundreds. Writing unit tests and core code, sending cold emails, and drafting legal memos are currently the bulk of training in the early careers of junior software engineers, sales representatives, and lawyers. But these are also the tasks most likely to be automated first.
At first glance, the implications may seem daunting: new graduates may find it very difficult to secure jobs, not to mention the risk of an oversupply of qualified candidates for compensation. On the other hand, companies will now be more eager than ever to rapidly develop the skills of junior workers.
Samuel, who is now a journalist and speaker focusing on remote work topics, said, “We will definitely have people capable of accelerating the learning curve and moving into what we now consider to be mid-level jobs faster and earlier in their careers.” In other words, the way we learn on the job may change drastically, but disruption may offer innovations in thinking about training, learning, and development.
The Importance of Mentoring and Training
The human learning process, as we currently understand it, relies on pattern recognition and repetition. Each time we repeat a task, the neural pathways associated with that task are strengthened. In the past, the early training years of knowledge workers were often filled with relatively mundane and repetitive tasks for this reason – whether it was building presentations for junior marketers or taking meeting notes and organizing calendar schedules for entry-level administrative assistants.
However, even the simplest tasks can benefit from having someone else guide you through the process. Imagine sending a piece of mail: you could place someone in front of a pile of mail and let them figure it out on their own, or you could provide actual instructions. The latter would certainly be faster than the former. For this reason, the number of repetitions it takes to learn something is not heavily reliant on whether the learner is engaging in deliberate and challenging practice, but it also depends on the presence of mentorship and guidance.
Samuel strongly supports organizations transitioning to a model that is more focused on mentoring and actual training. It makes sense that as hybrid or remote work environments increase, the training methods we use should evolve and adapt to the landscape. She says, “The reason we are so obsessed with this idea of learning in the workplace is that we are really bad at providing real learning opportunities, where you actually have to take 20 minutes out of your day to teach people something.” That can happen just as easily remotely as it does in person.
Impact
Artificial Intelligence and Early Career Training
The modern work culture is shaped by tools like artificial intelligence – and vice versa – with the evolution of new technologies and flexible work environments. It can be challenging to accompany a coworker throughout the day or join a meeting simply by chance when everyone works at different hours and is scattered around the globe.
With fewer jobs requiring even access to a physical office, young knowledge workers at the start of their careers miss out on these seemingly insignificant yet crucial points, often entering the labor market without context or concrete examples of workplace training. “Onboarding” is usually just a series of static program presentations and compliance videos. However, these introductory programs can be updated to meet the challenges of remote work through tools like artificial intelligence.
Suppose you are a data engineer working at a software company. Currently, the only real way to know how to handle data outages is to experience them firsthand. Hopefully, the company you work for has just a few data outages per quarter, so your learning speed will be slow. But what if part of your training included “simulated outages,” where faulty data points are generated by artificial intelligence? This concept could be expanded to other areas: junior therapists could conduct training sessions practicing with virtual voice assistants, radiologists could look at AI-generated scan images of rare diseases, and sales representatives could train with virtual customers having specific interests and needs. These simulations provide contextual learning through practice.
Reinventing Entry-Level Positions
Artificial intelligence is not the first technology to impact entry-level jobs. In the 1970s and 1980s, the advent of ATMs automated most routine tasks for bank tellers – who until then were the primary entry-level job in finance. However, while new technology significantly reduced the overall need for tellers, it did not make the role entirely obsolete. Tellers continued to handle the more complex customer needs that required human interaction. More importantly, automating basic transactions like deposits and withdrawals allowed banks to shift entry-level hiring into roles that better fit the new landscape, such as customer service representatives and risk analysts.
The same has been true over the past decade in the world of IT operations. Server maintenance and internet interface were integrated into “DevOps” roles, where developers can now provision cloud servers themselves. This means the end of the “database administrator” role, but it also made individual engineers more productive and versatile.
David Malan, a computer science professor at Harvard University who teaches the introductory computer science course, stated, “I think we already have ample evidence that even with the advent of new technology, we embrace it and it doesn’t put people out of jobs as much as it gives us more skills ourselves.” The job market is flexible, and employer expectations of entry-level employees are already changing; job applicants say tech companies are asking fewer algorithmic coding questions, which artificial intelligence can solve easily. “It’s more about system design now, I think, where you have a conversation with the other person,” said Vicky Shaw, a college student studying computer science.
As for the secretarial jobs from which Samuel missed the opportunity to learn – they have received a second and even third life. The role of the secretary that evolved in the 1960s and 1970s, primarily involving typing, representation, and administrative support, has transformed into more strategic positions like executive assistant and chief of staff. With less time dedicated to basic administrative tasks due to technology, assistants can take on responsibilities like event planning, client relations, research projects, and employee surveys, and work as executive leader consultants.
These jobs seem different from what they used to be, but the human elements remain the same.
Source: https://blog.dropbox.com/topics/work-culture/how-ai-will-affect-early-career-training
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