Industrialise GenAI and prepare for agentic AI

It relies on generative models trained on large amounts of data. The competitor pages mention techniques such as transformers, GANs, and variational autoencoders.

Agentic AI refers to AI systems that can pursue goals autonomously across high-volume, multi-step, and rule-intensive workflows, with limited human intervention. In practice, it goes beyond isolated prompts to support more continuous processes while helping improve speed and accuracy.

Agentic AI is important because financial institutions are moving beyond standalone GenAI experiments toward process reengineering at scale. It can help organisations streamline complex workflows, improve operational efficiency, and identify new opportunities across areas such as customer management, finance, loans, and insurance. The report also suggests that agentic approaches are becoming increasingly relevant as institutions look to industrialise AI in a more structured way.

The financial services industry refers to the organizations that manage money, payments, lending, insurance, investment, and other financial activities for individuals and businesses.

Generative AI in banking is used to improve operational efficiency, customer experience, employee experience, knowledge management, and compliance through capabilities such as document intelligence, text and content generation, employee agents, and more advanced agentic AI workflows.

AI in banking can be integrated through secure architectures, strong data foundations, core-system integration, auditability, governance, and human oversight. It is most effective when tied to clear business value, introduced in well-defined processes, and deployed with supervision and responsibility controls for critical decisions. Sopra Steria can help you take the first steps and follow you along the way.