In the 1980s, automation was the first great revolution of modern work. At the time, the main goal was to mechanise production processes, reducing costs and increasing productivity.
Automation was synonymous with industrial robots, numerically controlled machines and automated production systems: rigid tools, programmed to perform repetitive operations with greater precision and speed than humans. The change was visible above all in factories and production departments, where human intervention was progressively replaced by machines able to replicate gestures and operational sequences.
The main impact was therefore physical and organisational: people were assisted or replaced in manual work, while cognitive and office work remained largely unchanged. The paradigm of the time was clear: "man programs, the machine executes."
This automation had a deterministic logic. Machines executed what they were programmed for, with no ability to adapt or learn. Data was structured, limited, stored locally, and decisions remained firmly in human hands. Those years saw the birth of the culture of productivity, but also the first mass fear that "the robot might steal jobs".
"Yesterday technology replaced manual work; today it replaces conceptual work, and paradoxically protects human work in its most authentic aspect — the one made of relationship, experience and presence."
Today we live in a completely different phase. Contemporary automation no longer concerns only physical machines, but also and above all intelligent machines. It is the era of cognitive automation, of artificial intelligence and systems able to learn, analyse, generate and even decide.
Today's technologies do not just replace people, they collaborate with them, expanding their analytical, communicative and creative abilities. The introduction of machine learning algorithms, robotic process automation (RPA) and large language models (LLM) has made it possible to automate not only actions, but part of reasoning too. Automation is no longer confined to industry, but extends to services, finance, healthcare, public administration and intellectual professions.
The impact on people is radically different
In the 1980s, automation mainly affected manual work; today it touches cognitive work. The roles involved are no longer assembly-line workers, but analysts, project managers, technicians, IT professionals, consultants and creatives. People are no longer only those who program machines, but those who dialogue with them, interpret their answers and steer their decisions.
The paradigm has changed: "man collaborates, the machine amplifies." In this new balance, the machine does not replace but augments human abilities, making it possible to handle volumes of information and complexity unthinkable in the past.
However, this progress also brings a new vulnerability. If in the 1980s the fear was losing physical work, today the risk is subtler: the loss of cognitive autonomy. Delegating too much to artificial intelligence can lead to dependence on tools that decide and reason in our place. This is why the challenge of the present is no longer "automating man", but humanising automation.
From an organisational standpoint, the differences are just as profound. In the 1980s a hierarchical and rigid model prevailed: clear chains of command, fixed roles, standard hours. Today organisations become horizontal, data-driven and flexible. Professional value no longer lies in efficient repetition, but in the ability to adapt, learn and combine different skills.
"Operational professions (artisans, technicians, maintenance workers, field operators, personal carers) survive and transform, because they require physical presence, empathy and contact."
On the social level
The automation of the 1980s was an industrial revolution; the current one is a cognitive revolution. The first touched muscles, the second touches the mind. The risk then was physical alienation, today it is intellectual alienation. But both eras share a fundamental truth: every technological advance changes not only work, but people's perception of themselves in work.
Ultimately, past automation mechanised people, making them part of the production system. Today's automation augments them, integrating them into an ecosystem of human and artificial intelligences that cooperate. The shift from "executing machine" to "thinking machine" imposes a new responsibility: learning to manage collaboration, not substitution.
The challenge of our time, then, is not to ask how much better AI can do than us, but how it can make us better at doing what only humans can do: give meaning, choose, imagine, build value.
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