Artificial intelligence is more than just a fad, it is a promise of efficiency, innovation and competitive differentiation. And yet, beyond the hype, many companies are beginning to take stock: while some initiatives are achieving notable success and promise, many are struggling to move beyond the prototype stage.
Indeed, AI, fascinating in its effects, remains implacable in its requirements, and many projects fail to industrialize for lack of the essential foundations: quality, well-governed and sustainable data. An AI project therefore begins not just with an algorithm, but also with a question: are my data "AI-Ready"?
Testing is not building: the illusion of Proof of Concept without foundations
The first step towards AI often takes the form of a proof of concept (POC): show quickly, convince strongly, produce a first tangible effect. It's a necessary step to get people on board, but if it's not backed up by a solid data base, it's just a flash in the pan. The success of a POC in no way prejudges the ability to industrialize. On the one hand, we build a promising technology; on the other, we sometimes forget that the raw material - data - can be scattered, heterogeneous and poorly governed. The result: when it comes to industrialization, the obstacles multiply: difficult access to data in production, data quality flaws invisible on a small scale, hidden costs of upgrading to industrial scale.
The order is not to choose between technological innovation and data preparation, but to orchestrate the two in parallel. The more technology advances without a foundation, the more it exposes itself to crumbling foundations. The more data is structured without a usage objective, the more it remains a dead letter. Building AI doesn't mean piling up experiments. It means simultaneously building a relevant algorithm and available, reliable, governed data. Testing, yes, but testing with structure, trajectory and scaling in mind is even better.
The fundamentals of AI-ready data
AI-Ready data is not simply data made available to the tool. It must be accessible, of high quality, documented, secure and, above all, projected over time. Every AI project requires irreproachable raw material to ensure that model learning is reliable and sustainable. These five pillars play a key role in preventing models from degrading, biases from taking hold and errors from silently proliferating.
Because AI doesn't create anything: it extrapolates and amplifies what it's given. Biased or degraded data will therefore lead to biased AI reactions and proposals, and progressive performance losses. Only constant monitoring, combined with active governance of the data base, can maintain trust.
Preparing professions to think AI: beyond data, understanding
While data quality is essential, it is not enough. Sustainable AI is first and foremost that which is monitored and guided by the business. They are the ones who have a detailed understanding of the uses and subtleties of the field. Their involvement from the outset of the project is a sine qua non for guaranteeing the relevance of models and their alignment with real needs.
A successful AI project is one in which the professions are not simply spectators, but players in their own right: trainers of the machine, guarantors of meaning, monitors of relevance. This means raising their awareness and training them in new skills: understanding how AI learns, how data influences it, and how to detect aberrations.
It's not a question of transforming every job into a Data Scientist, but of installing a shared AI culture: the ability to dialogue with the machine, interpret its decisions and correct its trajectories. This hybridization of skills becomes the key to a harmonious and virtuous cohabitation between humans and AI.
Responsible AI starts with well-supported data
At a time when AI questions as much as it fascinates, one thing is clear: responsible AI begins with well-supported data. Quality, governance, traceability: these pillars are not only technical, but also based on operational ethics.
Data is a strategic asset, just like talent or products. It conditions an organization's performance, resilience and capacity for innovation. But it also determines the appropriateness of technological choices: because not all use cases need generative AI, especially when a simpler, more frugal model can produce the same business value. In times of economic and environmental tension, asking the question of the right technological level becomes an act of responsibility.
Making your data "AI-Ready" is therefore not a one-off project: it's about initiating a paradigm shift, reinventing processes and developing the ability to intelligently arbitrate between impact, value and use. You can't build sustainable AI on fragile foundations.
For those who are still wondering, there's a simple answer: your AI will never be better than the data you entrust to it. And this data, too, deserves to be thought about, governed and valued with all the seriousness that the promise of artificial intelligence demands.
Pascal Anthoine
Director - Data Governance & Data Management
Micropole, a Talan company
