For businesses, the question is no longer which AI model is the most powerful, but how to use it effectively to achieve concrete and measurable results. In an analysis published by Journal du Net, the creation of value linked to artificial intelligence is now presented as depending primarily on companies’ data and their ability to mobilize it.
The author notes that AI is following a trajectory comparable to that of cloud, storage, or computing power. As these technologies mature, they become more powerful, more accessible, and more integrated into business operations. Given the current acceleration, the challenge is no longer so much about having access to it, but about deriving real value from it.
Debates Focused on Models
Much of the debate on generative AI has focused on the models themselves: which one is the most powerful, the most reliable, the fastest. The discussions are evolving, turning towards how AI can accelerate product development, improve customer experience, increase operational efficiency, or reduce costs. Large generative AI models are proliferating and their performance is improving, so companies have an unprecedented choice. They will differentiate themselves, according to this text, less by the intelligence they have access to than by how they use it.
Models provide general capabilities, but it is the data specific to each organization that gives them the knowledge needed for relevant results. To create value, this data must be easy to use, within a trusted framework, by business teams as well as by AI systems. Siloed data limits what AI can deliver, whereas governed, contextualized, and accessible data can significantly increase its impact.
An Architecture Capable of Evolving
Leaders therefore need architectures capable of evolving as fast as the models. Building a strategy around a single model risks locking the company into a constantly changing technological environment. The goal is rather to integrate innovations as they emerge, while ensuring data consistency and governance.
The text highlights automatic model routing, which involves directing each task to the most suitable model based on criteria such as performance, cost, or complexity. This approach allows for more efficient use of AI, without systematically resorting to the most powerful model for every task.
The success criteria must also change. The number of queries or tokens, units of text processed by a model, measures usage, but says nothing about the value created. The question to ask is this: does AI enable better decisions, product improvement, stronger customer relationships, optimized operations, or the creation of new growth opportunities?
“For a long time, we thought competition in artificial intelligence would be between models. The reality, however, is that intelligence is gradually becoming commoditized.” Value creation is therefore shifting towards companies’ data and their ability to exploit it to provide AI with the context it needs.
