CREDIT AGRICOLE at AI for Finance by Artefact – AI at the Heart of the Group’s Strategy

The discussion highlights the bank’s journey with AI, focusing on its decentralized structure and how AI adoption is being approached across various entities. While the advent of ChatGPT in November 2022 marked a shift in public awareness, Crédit Agricole had already been engaged with AI well before, accelerating its focus on the technology in early 2023. By April 2023, a strategy was defined, emphasizing pedagogy, data protection for both the bank and its clients, and experimentation to understand AI’s practical applications across a decentralized ecosystem.

Crédit Agricole’s approach to experimentation involves setting rules to ensure secure and efficient implementation, assessing the business relevance and performance of AI initiatives. The bank has considered the technological impact of AI, including vigilance against technological dependence. The experimentation phase, with around 150-200 ongoing projects, is not an end but a pathway to larger, scalable applications. This led to the bank’s strategy of scaling AI at the group level, transitioning quickly from prototyping to industrial-scale use cases.

The strategy, known as “deployment at scale,” aims to accelerate AI initiatives from experimentation to industrialization. The main goal remains consistent: leveraging data and AI to reinforce human relationships, a distinctive feature of Crédit Agricole’s customer model. This entails simplifying processes, saving time, and focusing on high-impact customer interactions. By setting collaborative frameworks for use cases, the bank seeks to advance to high industrial standards, with the involvement of its executive committee (Comex) overseeing 36 specific AI use cases, each sponsored by top management.

These use cases are categorized into families that align with Crédit Agricole’s strategic priorities, such as enhancing collaborative tools within the bank, improving client support with human oversight, content creation for marketing and communication, and assisting middle and back-office functions like customer verification (KYC) and environmental, social, and governance (ESG) compliance. Additionally, AI assists control functions by improving customer-centric regulatory processes and is widely adopted in IT development, especially since coding is a language that AI can efficiently assist with.

Addressing challenges such as mastering new skills, cost management, and adoption, Crédit Agricole emphasizes multidisciplinary collaboration and continuous acculturation across all levels. The bank also prioritizes building a technological foundation that aligns with rapidly evolving AI developments while maintaining security and risk management standards.

With generative AI, the bank aims to enhance human functions while ensuring environmental responsibility, considering not only cost-efficiency but also energy consumption and resource allocation. As generative AI evolves, the focus may shift from enhancing model power to simplifying user interactions and achieving coherent, reliable results. The future of AI at Crédit Agricole might center on conversational contexts while maintaining a balanced approach to AI’s place in information management and daily operations.