As AI becomes permanently established in business processes, the question of its alignment with the company’s vision and culture becomes a priority.
For businesses, AI automates, recommends and arbitrates. We can no longer analyze AI solely through the prism of its technical capabilities. We must understand the logic that guides its decisions, the frameworks in which it operates and the responsibilities it involves. This is precisely where the notion of alignment becomes central.
Alignment, a blind spot in the AI debate
Alignment is often presented as a theoretical, even philosophical, concept. In reality, this is a very concrete problem. An AI is said to be “aligned” when it pursues objectives consistent with those who designed, deployed and used it, while respecting a given ethical and regulatory framework. In other words, an aligned AI does not just produce effective results: it acts in a way that is understandable, controlled and acceptable.
This subject is becoming critical with the rise of generative AI and, above all, so-called autonomous agents. The transition from AI that responds to requests to AI capable of acting on systems, chaining together decisions or interacting with other tools profoundly changes the nature of risk. The more autonomy an AI has, the more the optimization of a poorly defined objective can produce undesirable effects. A system designed to maximize a given metric, without sufficient safeguards, mechanically tends to ignore anything that does not fit into that metric. The example of philosopher Nick Bostrom is often cited, in which an AI is programmed solely to maximize the production of a paperclip factory. The AI ends up consuming all the resources at its disposal and destroying humanity simply to fulfill its mission of producing ever more paperclips. But in business use, non-alignment can be more sensitive. A chatbot AI can defend inappropriate political positions, an image generation AI can produce stereotypical characters, an AI producing marketing content can give visibility to a competitor, etc.
This is where ethics comes into play, as a trade-off issue. AI models inherit biases from their training data, the implicit choices of their designers, and the cultural contexts in which they are developed. They reproduce, sometimes amplify, existing imbalances. Above all, some decisions do not have a universal answer. In many cases, there is no “good” algorithmic decision, only compromises to be made explicit.
The illusion of algorithmic neutrality
Faced with this reality, the entry into force of the AI Act marks a turning point. This regulation does not aim to slow down innovation but above all seeks to structure a market that has become too opaque, by forcing players to clarify their role, their uses and their responsibilities. The AI Act introduces a classification of systems according to their level of risk, clearly distinguishes between providers, deployers and professional users, and imposes requirements for transparency, control and documentation.
Even if the term alignment is not always explicitly used in the texts, it is omnipresent implicitly. Documenting an AI system, explaining how it works, informing users of its limits, or training teams in its uses, are useful steps to ensure its alignment.
For European companies, regulation is not only a constraint. It becomes an essential framework of trust for deploying AI on a large scale. In an environment where automated systems influence sensitive decisions (recruitment, moderation, rating, customer relations, reputation), the absence of a framework is a much more important obstacle than compliance.
The AI Act, a catalyst for assumed alignment
Finally, this question of alignment cannot be dissociated from that of sovereignty and control of data. In fact, not all EU companies will have the opportunity to self-host European open source models.
Sovereignty is never absolute and is based on technological, economic and sometimes geopolitical compromises. But there is a major difference between an informed compromise and an imposed dependence.
For companies, the real risk is not just using external technological bricks, but above all doing so without understanding or control. This opens up an essential angle of analysis: questioning not only what AI does, but also in what framework it is designed, deployed and governed. Who sets their goals? Who takes responsibility for their mistakes? Who is responsible?
Concerning non-aligned AI productions which “go off the rails”, the new European directive “Product Liability Directive” on the liability of defective products has been extended to artificial intelligence, to prevent actors from exonerating themselves while passing the blame on themselves.
As AI becomes an invisible digital infrastructure, alignment is emerging as a new legitimate concern for all companies that produce, but also use, artificial intelligence services. It is no longer just a question of whether a model is powerful, but whether it is understandable, controllable and responsible. The future of AI in business will depend on the ability of organizations to align their tools with their objectives, values and obligations.
