If you listen to how people usually talk about artificial intelligence, you might think that it works on its own – quietly, quickly, and almost magically. But any “smart” system has a downside: thousands of people who check responses, mark up data, talk to customers, fix errors, and keep it all in working order. They are rarely seen. They are hardly mentioned. But they are the foundation of the very economy of the future, which is so much talked about.
The paradox is that these same people are standing on the edge of change today. Their work is the first to change under the influence of AI – and they are the ones who are most often asked to adapt faster than others, often without much preparation. The question is not whether technology will replace them. The question is whether they can rise to a higher level, where man and machine work together in a more balanced and meaningful way.
When “simple tasks” stop being simple
Routine work has long been considered something stable: repetitive processes, clear instructions, and understandable results. But with the advent of AI, it is precisely such tasks that are automated the fastest. This is not only applicable to production but also to the office environment: it could involve processing applications, basic customer support, and analyzing standard requests.
The loss of routine, however, does not imply the loss of work. Rather, it begins to “shift.” For example:
- The support operator no longer just answers questions, but interprets AI responses and manages complex cases.
- The data entry specialist begins to monitor the quality of automatic processes.
- The administrator becomes the coordinator of digital tools.
The work becomes less mechanical and more meaningful. And this is good news – if there is a possibility of retraining.
New Literacy: Not just about code
It is a certain myth that is propagated: in order to live in the era of AI, it is necessary to learn programming immediately. But in real life, it is more complex and interesting.
Yes, technical skills are important. But more often than not, other abilities come to the fore:
- the ability to critically evaluate the result of an algorithm;
- communication – especially in hybrid human + AI teams;
- adaptability: the ability to learn quickly and change your approach;
- understanding processes, not just individual tasks.
This is where a new form of literacy arises – not digital in the narrow sense, but contextual. An individual should be able to learn the impact of technology on his/her occupation, the limits, and the way of utilizing it to their benefit.
From routine to value: How do retraining programs work?
The companies are being presented with a simple choice between automating their employees or spending on human resources. The latter is more conducive to the environment, in addition to being cost-effective, in the long ru,n when experience and internal knowledge begin to accumulate.
There are three principles upon which good upskilling (advanced training) programs are based:
1. Learning on the job
People learn not in abstract courses, but directly in their tasks. For example, a call center operator gets conversation analysis tools and learns to see patterns of customer behavior. Over time, this turns everyday interactions into a source of insight rather than just routine work. Employees begin to notice tone, timing, and intent — things that are impossible to grasp from theory alone.
2. Transition from task to role
The employee is not merely being assisted to master a more complex and purposeful role as opposed to accelerating the old functions. This may also involve increased responsibility: making judgment calls, exception management, and even mentoring others. Over time, performance starts being centered on the achievement of results, rather than on the accomplishment of tasks, and this shift alters the perspective of the members of the organization.
3. Feedback as a basis for growth
Without regular feedback, training does not work. And here technology plays an unexpectedly important role.
When companies decide to invest in call monitoring, they actually create a continuous training system. Instead of occasional reviews, feedback becomes part of the daily workflow. Analyzing conversations helps:
- identify weaknesses in communication;
give personalized recommendations;
track the employee’s progress.
It also allows managers to spot recurring challenges across teams and adjust training accordingly. This is no longer control for the sake of control, but a development tool. The employee sees how he is getting better and understands where to go next, which makes growth feel tangible rather than abstract.
Think about any large company that implements AI
Who handles non-standard customer requests?
Who checks the correctness of automatic solutions?
Who trains the system on new data?
The answer is almost always the same: people in “operational” roles.
These teams become a kind of bridge between technology and reality. They adapt algorithms to real-world scenarios, identify errors that are not visible to developers, and shape the user experience. That is why their development is not a secondary task, but a strategic priority. Without them, digital transformation remains at the presentation level.
Why training often doesn’t work – and what to do about it
Despite all the talk about the importance of retraining, many programs turn out to be ineffective. The reasons are quite prosaic:
- learning is divorced from real-world tasks;
- The content is too general;
- lack of time to apply new skills;
- employees’ fear of change.
To change this, companies are starting to act differently. They:
- reduce the distance between learning and practice;
- use real cases instead of theory;
- introduce micro-learning – short, regular modules;
- create a culture where mistakes are part of the process.
It is especially important that employees feel that they are not just being “retrained”, but are really invested in their future.
Small steps that change everything
Not always does the process of transformation start with tremendous reforms, but with minor changes. For example:
- implementing a conversation analysis tool in the support team;
- regular case reviews;
- exchange of experience within the team;
- access to short training materials.
Such steps gradually change the perception of work. An individual is no longer a performer, but he starts perceiving himself as a participant in the process.
And also the psychological moment plays a role here: in case an employee gains growth, he is much more accepting of changes.
Humans and AI: not competitors, but collaborators
There is a lot of talk about machines “taking away” work. However, the truth is not so dramatic and so pronounced. AI is effective in working with a great amount of data, repetitive work, and in a rapid search for information.
An individual is better placed to understand the context, empathy and make a decision during uncertainty. When these abilities combine, a new model of work appears. Not “either-or”, but “together”. And here is the main problem: to educate people to work with AI as a tool, but not to regard it as a threat.
Who benefits from this transformation?
If everything is done correctly, everyone wins. Employees get more interesting and sustainable jobs. Companies get a more flexible and qualified team, and the customers receive the best service.
But the keyword here is “if.” Without a systematic approach, change can increase inequality: those with access to learning move forward, while others remain behind.
A quiet revolution that can only be heard from the inside
The most important changes rarely look like revolutions. They are not accompanied by big headlines and do not happen overnight.
They happen on regular business days:
- when is the first time a support operator analyzes their own conversation and sees how it can be improved?;
- When an employee stops being afraid of automation and starts using it as an assistant;
- when a team finds a way to work faster – and at the same time more meaningfully.
A technologically advanced economy is not the only one that is enhanced by AI. It depends on individuals who are ready to learn, change, and develop.
And maybe the real question today is not: Which professions will be eliminated?
But rather: How many people can we support on the way to the next level?
Editor’s Note: The opinions expressed here by the authors are their own, not those of Impakter.com — In the Cover Photo: Humans at the table, working with their artificial intelligence workforce. Cover Photo Credit: RonaldCandonga
