Discover insights from the Raise Summit 2023 in Paris through our expert Lionel Regis-Constant, covering major data and AI challenges
In addition to the presence of a number of stars from the field, including the CEOs of Perplexity, Groq, Mistral and Dataiku, the R.AI.SE trade show provided an opportunity for high-level discussions on the potential and challenges posed by GenAI. Data architecture, AI ready, product focus, AI governance, transformation, adoption: these were the main themes addressed. In terms of architecture, the discussions were wide-ranging, from the development of specialised chips by Groq, promising processing speeds up to ten times faster, to the issues of deployment and choice of models.
Several sessions emphasised the need for companies to experiment with AI, but also to support employees in the development and deployment of this technology.
The demand for skills development and awareness-raising is considerable, both for employees and for senior management, who must support this initiative given its cross-functional nature.
The Transformative Impact of GenAI
A recurring theme throughout the conferences was the profound impact of GenAI on the optimisation and transformation of business processes, customer support, development practices, and even government functions. Companies and speakers, including industry giants such as Dassault Systèmes, Orange, Aveva, IBM and various startups, shared insights on how GenAI not only accelerates development and reduces costs, but also expands the capabilities of organisations to deliver new services, improving the scope of what is possible: it's not just about optimising, it's about creating.
Challenges and Opportunities in the Public Adoption of GenAI
The integration of GenAI into public services, despite its potential, seems to be encountering significant challenges. The notes highlight a general rethink among governments to adopt GenAI, highlighted by issues around infrastructure, skills gaps, difficulty of experimentation. This highlights a broader narrative within the AI industry: the need for a core ecosystem that supports the seamless integration of this technology, involving playgrounds for experimentation, robust infrastructure, skills development and a balanced regulatory framework that encourages innovation while ensuring ethical considerations.
The international and industrial dimension of GenAI
The demand for a diverse dataset that spans multiple languages and cultures to reduce bias and improve model efficiency was highlighted, alongside the need for a stable legal environment that supports the growth of AI technologies. In the industrial sector, the challenges of deploying production-ready GenAI solutions, particularly in regulated businesses or where sudden behavioural changes can disrupt operations, were discussed. The RAG STACK proposed by Datastax, aimed at streamlining the deployment of GenAI applications with a focus on reliability, security and performance, underlines the industry's move towards making GenAI more accessible and sustainable for enterprise use.
Governance of AI
AI governance also received considerable attention, with discussions focusing on the need for transparency, auditing and oversight to mitigate the risks associated with self-learning and self-improving AI systems. The dangers of data poisoning and the replication of biased or erroneous information highlight the importance of establishing robust governance frameworks that oversee the entire lifecycle of AI models.
Conclusion
In sum, the insights from these conferences paint a landscape where GenAI is seen as a transformative force across sectors, poised to redefine how businesses operate, how public services are delivered, and how environmental challenges are addressed. However, this transformation is conditioned by having to overcome a number of significant technical, regulatory and ethical challenges, requiring a collaborative effort between technologists, businesses, governments and society at large.
Over and above these considerations, one factor seems to be asserting itself: France is the leader in AI in Europe. To consolidate this position, it will be necessary not only to invest, but above all to retain talent and attract new talent.