Industrialise GenAI and prepare for agentic AI

This summary highlights the main findings from our second edition report on industrialising generative AI in financial services and preparing for Agentic AI. Based on use case analysis across 8 European countries, it outlines the trends, priorities, and challenges shaping AI adoption in financial services today. 

Why generative AI and agentic AI in financial services matters today?

From onboarding to customer support, GenAI and agentic approaches are beginning to reshape multi-step processes across financial services. At the same time, organisations are still working to strengthen governance, measurement, and scaling capabilities. 

Financial services are building, piloting, and industrialising generative AI in banking and broader financial services environments, while also preparing for what comes next with agentic AI and agency AI models. 

What you’ll learn about GenAI and Agentic AI in the summary?

  • How agentic AI investments are progressing in Europe 
  • How spread the use of agentic AI in banking truly is   
  • Generative AI use cases in financial services  
  • 3 main messages taken from our use case and market analysis   
  • Who to contact to know more about the full report

FAQs:

Generative AI is a form of artificial intelligence that creates new content such as text, images, video, audio, or code based on patterns learned from data. It is often used through prompts in the natural language.

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.

Download the full 2-pager "Industrialise GenAI and prepare for Agentic AI"

 

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