The era of AI efficiency puts data back at the heart of value creation

Chronicle by Arnaud Chiffert Snowflake October 26, 2026 10:56 The challenge for companies is no longer to choose the most efficient AI model, but to exploit it effectively to generate concrete and measurable results.

In recent years, artificial intelligence has continued to push the boundaries of technology and appears to be following a trajectory comparable to that of the innovations that preceded it. As the cloud, storage and even computing power have shown, as these technologies mature, they become more efficient, more accessible and more deeply integrated into the functioning of businesses. But faced with the meteoric acceleration of AI, the challenge is no longer so much to have access to it as to be able to derive real value from it for the company and concrete results.

So far, much of the debate around generative AI has focused on the models themselves. Which will be the most powerful? The most reliable? The fastest? Which one to adopt to prepare for the future? However, discussions are evolving quickly. It’s no longer just about determining which model performs best, but understanding how AI can accelerate new product development, improve customer experience, increase operational efficiency or reduce costs.

Large generative AI models are multiplying, their performance continues to improve and businesses now have unprecedented choice. This growing range of capabilities gives them more options in how they can use AI to support their businesses. In the future, companies will differentiate themselves less by the intelligence they have access to than by how they exploit it.

Competitive advantage therefore increasingly depends on the ability of companies to anchor AI in reliable data and business context. Models provide general capabilities, but it is business-specific data that provides them with the insights needed to produce relevant results. This evolution is transforming the way organizations think about their data assets. To create value, they must be easily exploitable, within a framework of trust, both by business teams and by artificial intelligence systems. Siled data limits what AI can deliver, while governed, contextualized and accessible data can significantly increase its impact.

Leaders therefore need architectures capable of evolving as quickly as the models themselves. Building a strategy around a single model risks locking the company into a constantly evolving technological environment. On the contrary, the challenge is to put in place an architecture capable of integrating innovations as they appear, while guaranteeing consistency and data governance. It is also in this context that the automatic routing of models takes on its importance. A suitable platform can direct each workload towards the most relevant model based on criteria such as performance, cost or complexity, in order to use AI more effectively without systematically relying on the most powerful model for each task.

Machine-translated from French