The agentic AI requires rethinking data architectures. So go from rigid models to agile systems, capable of exploiting fully structured and unstructured.
We are at the dawn of a deep technological transformation. While agentic artificial intelligence is gradually installed at the heart of businesses, a fundamental question arises: do we have the technical architectures necessary for the development of these new intelligent systems? As these autonomous AIs gain in maturity, the old foundations, built for another time, become more insufficient.
The end of a technical model exceeded
For decades, organizations have been based on data structures designed to manage clear, structured, predictable information. But the agenic AI radically upsets this framework. These new systems do not simply seek to classify or store data; They interpret them, contextualize them, dialogue with them to produce nuanced and relevant responses. By continuing to build AI on these old foundations, companies take the risk of erect colossi with clay feet: impressive but fragile, efficient but limited in their real possibilities.
This discrepancy is already manifested in the growing difficulty of companies to satisfy the ambitious promises made around AI. Embarrant latencies, repeated inaccuracies, too rigid systems to evolve at the necessary rate: symptoms accumulate and clearly indicate that the time is in deep reconstruction.
The silent revolution of unstructured data
The transition to agentic AI is accompanied by a second, discreet but decisive revolution: the rehabilitation of unstructured data. For a long time, companies have focused exclusively on well -arranged data in tables. But today, real wealth lies in texts, human exchanges, images, audio and video files. These long -neglected contents carry in them the key to precious contexts and fine understanding, essential for nourishing modern artificial intelligence.
To take advantage of it, technical systems must evolve: go beyond the simple storage capacity, to go towards interpretation and contextualization. It is there, precisely, that companies can create a strategic difference, a decisive advance on their competitors.
New foundations for artificial intelligence
This evolution towards agentic AI is not a simple technical change; It marks the transition to a new era where language – and no longer only raw data – becomes the heart of computer architecture. The large linguistic models are powerful but generalist. They must therefore be combined with smaller, more precise models, trained specifically for each business context. It is only by hybridizing these approaches that one creates an intelligence both wide and sharp, capable of managing the complexity of the real world.
This new approach requires a radical change in the way of designing information systems. It is no longer enough to organize data; You have to give them meaning, consistency, agility.
To a reinvented customer experience
Take the example of retail. Imagine a shop where each customer is accompanied by an intelligent agent capable not only to respond to his immediate requests, but also to offer him a tailor -made, proactive and relevant experience. Such an agent could anticipate customer preferences, understand their needs even before they are clearly formulated, while simultaneously managing thousands of interactions fluidly.
To make this possible, technical architecture behind these agents must be able to learn constantly, to memorize each past interaction, to understand the subtlety of a request, while retaining large -scale performance. The stakes are high: it involves offering users an exceptional, personal, immediate, permanent experience.
A strategic emergency to remain competitive
This new generation of intelligent agents is not a technological luxury reserved for a handful of innovators. It is a strategic necessity. In a few years, it will no longer be a single AI, but whole constellations of specialized, cooperant and autonomous agents, which will populate our businesses.
Organizations that have been able to anticipate and prepare the ground today will be the ones that will dominate tomorrow. They will have a robust, agile infrastructure capable of supporting the growing complexity of these artificial intelligence ecosystems. The others, for lack of not having dared this necessary reconstruction, will risk becoming quickly obsolete.
Rethinking data architecture is therefore not only a technical question: this is a major strategic decision, an essential step on the path of the future.
