Sometimes the biggest changes don’t come with a new technology, but when it starts working in the background. This is what the relationship between the labor market and artificial intelligence looks like today. Instead of spectacular revolutions, we are seeing the quiet automation of everyday tasks. The ones that juniors started with for years.
Not so long ago, AI was the digital assistant. Today, it is increasingly taking over parts of processes and making some responsibilities simply disappear. That’s why questions about the future of work in the AI era and whether it’s worth continuing to learn programming are no longer theoretical. It’s a change in the way one starts a career in a world where operational tasks are increasingly less likely to go to humans.
If you’re interested in the broader context of human-technology relations and how our interactions with AI are changing, I’ve covered this in more detail in a separate article about AI agents and human-technology relations. This text is about something a little different. Specifically, aboutwhat these changes look like in everyday work, career structure and requirements for professionals.
The biggest transformation is not that companies no longer need people. Rather, the task structure is changing. Many organizations today are moving from a support model by chatbots to an automation model by AI agents.
The difference is like between an assistant and an autopilot. A chatbot responds to commands, an agent works in the background and automates a piece of the business process. A good example is the work of a salesman: reports, data entry or offer generation can be taken over by the agent, while a human focuses on customer conversations and decisions.
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The biggest change is not the elimination of jobs, but the shifting of the workload. Mostly repetitive tasks and those that do not require extensive context or decision-making are being automated.
It’s a bit like a car with autopilot. You can give the system control of driving on the highway, but you still decide where you’re going and watch for safety.
In terms of work, this means that more and more people are stop performing operational tasks and start making decisions based on data prepared by AI.
The traditional career path has most often been to start with the simplest tasks. The problem is that these are the tasks that are most susceptible to automation today.
Juniors often:
This makes it possible for an AI agent to take over some of the responsibilities faster than for more experienced professionals. This doesn’t mean the end of a junior career, but it does mean a change in starting point, meaning less operational work and more expectations right from the get-go.
One of the most visible examples is customer service. Increasingly, companies are introducing a two-line model:
This means that most requests are solved by the system, and a human comes in only when a decision or context is needed. Reducing teams by up to tens of percent is no longer a theoretical scenario.
An apt comparison might be the Parcel Machine hotline. AI tries to help first, and only then does the call go to a human.
Automation does not mean full autonomy of systems. The human-in-the-loop model assumes that humans are still part of the process, especially at the beginning or end.
An agent can prepare a report or analysis, but the strategic decision belongs to a human. It is the ability to understand context and communicate with others that is an area where AI still needs human support.
In the world of AI agents, the profile of the specialist is changing. People who combine technical and soft skills are increasingly valuable.
The ideal specialist today is someone between an engineer and a consultant. A person who can build solutions and explain their meaning.
Naturally , it’s worth it, but with a different mindset than a few years ago.
With the development of AI agents, the need for people performing only basic tasks is diminishing. Programming increasingly means designing solutions, integrating systems and making decisions, not just writing code.
This makes the competencies of the future of IT today encompass much more than knowledge of a single programming language.
The biggest transformation is not about AI replacing humans. It lies in the fact that automation is pushing the boundary of entry into the industry.
Not long ago, it started with performing tasks. Today, it is increasingly starting with understanding the context.
And it is this change that defines the future of work in the AI era.
Mostly repetitive tasks, which were often the first stage of a career, are being automated. This changes the work structure, but does not eliminate juniors completely.
Understanding of technology, communication, and ability to work in a broader business context.
Of course, but it is worthwhile to develop more broadly and combine technical competencies with analytical and communication skills.
Today, AI is automating parts of processes, but strategic decisions still belong to humans.
