Five paths to develop sovereign and sustainable AI solutions

The integration of artificial intelligence in organizations represents a major transformation lever. However, in a context where responsibility and sustainability are at the heart of concerns, it becomes imperative to develop AI responsibly.

In this article, we explore five essential paths to design AI solutions that are both sovereign and sustainable, integrating issues of trust, public interest, and good governance.

It is paramount to understand your data journey from the beginning. For example, when you use an external language model to power a chatbot, ask yourself about the model's origin, the nature of the data used, and the legitimacy of their acquisition.

As Lex Avstreikh from Hopsworks explains, "AI often remains a black box regarding data processing. Making inputs and outputs visible helps strengthen transparency and trust."

Data sovereignty is not limited to their acquisition; it also encompasses their complete lifecycle. It is essential to determine where your data will be stored, in transit and at rest, and to analyze the regulatory implications related to the chosen data centers.

The example of Swedish company Ebbot, which opted for storing and processing its data exclusively in Europe after the Schrems II decision, demonstrates the importance of strategic planning in terms of sovereignty.

The localization of IT resources is crucial, both to guarantee sovereignty and to reduce environmental footprint. Favor data centers located in areas benefiting from renewable energy for intensive tasks such as model training.

Additionally, adapt your backup strategies to minimize environmental impact, opting for example for low-energy consumption backup solutions.

Beyond material efficiency, code optimization is paramount. Efficient and adapted code significantly reduces the load on hardware resources, thus limiting energy consumption.

With the emergence of GreenOps initiatives, IT resource optimization becomes an indispensable lever to reconcile performance and sustainability.

Custom development can sometimes lead to resource overconsumption. Consider reusing existing components or using PaaS offerings, such as AI Endpoints solution, which centralizes access to various AI models via a single API.

This modular approach not only optimizes resource utilization but also promotes an eco-responsible approach in developing your applications.

Developing sovereign and sustainable AI solutions is not only an ethical necessity, it is also a competitive advantage in an increasingly regulated environment concerned about its environmental impact.

By integrating sovereignty from design, meticulously planning data lifecycle, and adopting optimized and reusable practices, companies can secure their AI investments while minimizing their ecological footprint.