For a while, artificial intelligence in the workplace was presented as an option: a useful tool for anyone who wanted to experiment, help to achieve productivity, a kind of voluntary contribution. This phase ends.
In many technology companies, especially in the United States, artificial intelligence has become an accessory and needs to be turned into a tacit or explicit obligation: if it is supported, encouraged, controlled, and in some cases integrated into the evaluation and development processes, countermeasures. The message is sometimes less ambiguous: It’s not about whether you can use AIuntil you have to.
This completely changes the nature of the debate. We’re not talking about manufacturing or automation until before redefining the “valid” worker.
If you already know Excel, PowerPoint or CRM, start now the next need arises: knowledge of working with co-pilots, automatic editors, code assistants, internal agents and generative systems connected to the flows of society.
For example, Google has made the use of artificial intelligence central to the discussion of the future of work in Europe, and Salesforce describes a very strong acceleration in the daily use of artificial intelligence among office workers and a clear expectation of recognition in human resources.
The promise that AI will “step up” to the worker to replace them is not distributed evenly
The point is that this transition is not neutral. When a piece of equipment is changed from optional to mandatory, it only needs to be tech and converted to work discipline.
It is no coincidence that simultaneously with OECD and ILO They pay more attention to algorithmic work management: systems that not only help production, but also organize, recommend, prioritize, monitor and, in practice, condition worker autonomy.
When incorporated as a performance requirement, AI can also act as an infrastructure for oversight, standardization and monitoring permanently competitive.
Furthermore, the promise of AI “augmenting” the worker instead of replacing it is not shared evenly. The ILO points out that clerical and clerical workers are most vulnerable to generative AI, while the IMF warns that AI can increase demand and complement the salary and qualifications of mayors to the rest. Let me put it another way: benefits are not distributed automatically.
Those who leave with better skills, better posture and better adaptability have a better chance of getting the best productivity, but who don’t, risks being demoted, supervised or removed outright.
The OECD follows, reporting deep gaps in productivity, continuing education and workforce quality
Ahí is where this topic becomes particularly relevant for Spain. Our country has improved in various digital indicators and the European Commission recognizes a fairly solid position in basic connectivity and citizens’ capabilities.
At the same time, we continue to draw attention to the delay in the digitization of business, especially in pyms, which are the backbone of the Spanish staff.
Yes, that’s the problem: yes, a great business can do that internalize the trainingunpacking their own hardware, negotiating licenses and designing processes, the workforce is often asked for from the bar, with no resources, no time and no dedicated staff.
The implication is quite obvious: if ability is to be defined by familiarity with artificial intelligence, but the ability to acquire this knowledge depends on the type of company one works in, one opens up not just digital disruption, but laboratory disruption.
Not between those who “know the technology” and those who don’t, but between the workers of organizations capable of making AI routine and the workers of organizations. demasiado small, uncertain or saturated to that.
The OECD, in a specific specification on generative artificial intelligence and people, states that it can improve performance with artificial intelligence, but also that its adoption is limited by a lack of capacity, resources and governance.
Also in Spain this shocking transition with a labor market that has improved, yes, but which retains strong structural fragilities.
The OECD continues to report deep gaps in productivity, continuing education and the quality of the workforce, and Eurostat recognizes that time continues to matter, especially among young people and in the most vulnerable occupations.
In this context, the introduction of artificial intelligence as a new work literacy with a massive retraining strategy can be translated into something quite simple: the transformation of the business enterprise into a new social filter.
And I ask that, at the very least, we stop repeating the childish rhetoric that “you have to adapt”. Of course you have to adapt. The question is whether this adaptation pays off, such is the organization and so much has been the case.
Because companies require the use of artificial intelligence but do not offer time, training, clear criteria or guarantees regarding privacy, intellectual property or performance evaluation, the Lamanian innovation appears to be sufficient to transfer costs to the worker.
Learn what you’re talking about, experiment outside of class, get to know your mistakes or fallacies, and accept that your professional value depends on a mastery that doesn’t control bargaining power, nor is it an upgrade: it’s an asymmetry.
Spain should read this trend with sufficiently more seriousness than is usually applied to all cases concerning the future of work. No more celebrating who we are “digital” or with announcement strategies full of adjectives.
If the artificial intelligence goes through optional hardware according to the complexity of the work, the response cannot be individual. It is important to have ongoing truth training, specific training for staff, work criteria and indications for use and supervision, and a political discussion about this for adults. that is exactly what it means to be “exemplary” in algorithmic economics.
Because the real change isn’t AI entering the workplace. The real change is that work allows her to reorganize herself.
And when it does, it has no access, no context, no ability to follow a rhythm that is not simply “rezagado”: which is quickly classified as essential.
***Enrique Dans is Professor of Innovation at IE University.
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