As artificial intelligence models become more accessible, real differentiation no longer lies in the algorithm, but in data. It is it which structures the experience, conditions the results, and determines the value created.
The growing sophistication of AI models, and their rapid dissemination via APIs or open source services, has radically changed competitive dynamics. While the technical debate formerly focused on network architectures, the size of the training corpora or the speed of inference, the real stake has now moved: The quality of the data used becomes the main differentiating factor.
The model is universalized, the data remains singular
Accessing an efficient LLM is no longer a barrier. Most large companies can now use comparable models, at a reasonable cost. What now makes the difference is the context in which these models runin other words: the data that is provided to them.
Three criteria structure this rocking:
-
- Business relevance Data: unrelated to internal processes, the answers remain generic.
- Structuring : poorly modeled, incoherent or dispersed data reduces the effectiveness of reasoning.
- Freshness and traceability : to guarantee decisions aligned on operational reality.
An AI assistant in human resources, for example, will be able to recommend mobility or adjust a salary grid if the skills data, career history and internal policies are reliable, updated, and properly connected.
A new form of user experience
In traditional applications, user experience (UX) is focused on the interface: fluid navigation, visual organization, intuitive interactions. With AI, it is no longer enough. The experience is now based on the system’s ability to produce useful, credible and personalized results. And it depends directly on the quality of the input data.
A well -trained conversational AI can fail to respond useful if the data is:
-
- Absent or partial,
- Incoherent between departments,
- Inaccessible because of technical silos or poorly managed access rights.
The system may seem fluid – but produce hollow responses.
Data becomes an invisible interface
The issue of “the interface by the data” deeply transforms the responsibilities of the data, product and IT teams:
-
- Data becomes a layer of experiencein the same way as the graphic user interface.
- Its structuring is a design workwith consequences on the usefulness of the agent.
- His governance becomes a strategic issueat the crossroads of safety, compliance and operational efficiency.
In other words: Data is no longer a passive asset. It becomes a living object, manipulated by autonomous entities, and whose quality conditions the relevance of algorithmic reasoning.
Three requirements for effective use of AI
-
- Curated Data
The training or inference of an AI agent gains in efficiency as soon as the data is selected, cleaned, enriched and documented.
- Business contextualization
Data must be attached to specific processes : the simple accumulation of raw data is not enough.
- Mastered access and traceability
The opening of data at AI must be accompanied by precise control mechanisms : access rights, logs, audits, automatic update.
- Curated Data
Without that, The most efficient models remain black boxesunder-exploited or, worse, generators of errors.
Modeling is no longer sufficient, you must orchestrate
New generation AI obliges companies to reconsider the strategic role of their internal data. These are no longer only used to generate reports or supply dashboards. They become the raw material of autonomous reasoningautomated labor fuel, and the invisible interface between humans and agent. In this context, The quality of the data becomes a condition of experience. And this, in turn, determines confidence, use and performance.
