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In recent years, generative artificial intelligence has aroused both fascination and skepticism. But is it really as banal as people claim? Let us look at this question with a critical and investigative eye.
The Hype Around Generative AI
Technology consultant Jean-Baptiste Bouzige believes that generative AI has been the subject of intense media hype without producing the expected results. This phenomenon was illustrated by financial analysts who insisted on the gap between expectations and economic reality.
However, in the last quarter of 2023, tech sector giants like Microsoft And Google have finally managed to generate substantial revenue from their cloud services incorporating generative AI capabilities.
However, these figures do not yet reflect a generalized value for all companies. According to the firm QuantumBlack, less than 10% of companies have seen a tangible impact of generative AI on their pre-tax financial results, a worrying indicator that highlights the difficulties faced by the sector.
Economic promises and reservations
On March 13, during the presentation of the report of the interministerial commission on AI, its president, the economist Philippe Aghion, estimated that AI could increase the gross domestic product French from 250 to 420 billion euros by 2034. However, he chose not to quantify this forecast for generative AI, because the recent popularity of this technology makes it difficult to have sufficient perspective.
This caution is not without reason. Although the potential of this technology is indisputable, the slowness of its implementation and the lack of concrete financial returns explain this measured approach.
Specific use cases
An article from the specialized media The Information published on March 12 revealed that leaders of large companies like Amazon And Google are beginning to temper their expectations of generative AI. According to them, this technology, although promising, requires a methodical approach to create value.
To take advantage of generative AI, it is essential to identify use cases with high added value. It is about focusing on the apps likely to strengthen the competitiveness of a company by automating repetitive tasks and optimizing internal processes.
Implementation challenges
Despite the enthusiastic talk surrounding generative AI, it should be recognized that its implementation presents considerable challenges. These challenges include technical complexity, cost of integration and the need for specialist skills.
For companies wishing to carry out generative AI projects, it is crucial to:
- Evaluate the potential benefits of each use case
- Invest in suitable infrastructure
- Train staff in new technologies
Conclusion and outlook
Ultimately, it is premature to characterize generative AI as mundane. While it offers exciting prospects, its implementation and value creation remain complex and require a structured and pragmatic approach.
Businesses must adopt a focused strategy to maximize the impact of generative AI, while keeping in mind the technical and economic challenges surrounding it. Generative AI is certainly not the panacea announced, but it remains a disruptive technology with significant potential provided it is exploited judiciously.
