economic challenges

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In the current landscape of artificial intelligence (AI), economic issues are central, with implications reverberating across various sectors. The "Innovate Service Centric" report from L'Échangeur BNP Paribas Personal Finance, published on December 20, 2023, highlights the growing impact of generative AI on commerce. This report underscores a trend towards increased individualization of customer relations, transforming retail strategies into a fusion of digital and physical experiences. Brands like Adidas in Berlin illustrate this evolution, offering digitized and integrated customer experiences. In a context where inflation prompts companies to reassess their profitability, AI emerges as a catalyst for innovation, enabling brands to stand out with personalized and inclusive offerings while optimizing costs. This dynamic is essential to navigate a market where consumers constantly seek the best deals, as evidenced by initiatives such as Primark's anti-inflation basket and Carrefour's "find cheaper" button.

The deployment of AI in business, according to a Gartner survey from November 2023, shows that 70% of executives are actively exploring AI, with 19% having already integrated these technologies into their operations. Success in this area depends on companies' ability to clearly define their objectives and leverage the right data, while considering ethical and legal considerations. European AI regulation, notably the EU AI Act nearing finalization in early December 2023, plays a crucial role in this balance. It aims to protect users while encouraging responsible innovation. This regulation poses economic challenges, particularly in distributing responsibility between foundation model developers, such as ChatGPT, and their industrial users. The debate on the potential impact of this regulation underscores the need for a balanced approach, where citizen protection does not hinder technological innovation.

The French national AI strategy, articulated by Guillaume Avrin in August 2023, emphasizes the diffusion of AI in the economy. Priority areas such as trustworthy, frugal, and embedded AI are at the heart of this strategy, with increased support for the national ecosystem to foster local innovations. Simultaneously, collaboration with international partners, like the EU and Singapore, strengthens Europe's position on the global stage. These alliances aim to harmonize regulatory approaches and promote common standards, facilitating digital commerce and data innovation. These efforts demonstrate a willingness to adapt to the rapid evolution of AI technologies while ensuring a framework of trust for businesses and consumers.

Finally, the rise of deep tech, a constantly evolving field, illustrates the complexity of current economic issues in AI. With sectors like biotechnology, nanotechnology, and robotics, deep tech pushes technological boundaries. However, it also raises significant ethical and economic questions, particularly regarding employment and data security. Equity in access to these technologies is crucial to avoid widening existing inequalities. In France, initiatives such as the CNRS's RISE program aim to support the creation of deep tech companies, highlighting the importance of structural support to maximize the economic and social benefits of these innovations. In this perspective, companies must navigate between regulation and innovation to fully exploit the transformative potential of AI while addressing societal concerns.