Embedding AI in Daily Banking Work: From Quick Wins to Scalable Support

The "Low-Hanging Fruit": Immediate Impact

While flashy AI assistants performing complex creative work grab headlines, we took a different, low-key approach: targeting "boring" yet high-volume administrative friction. We focused on embedding LLMs directly into daily workflows for summarization and knowledge retrieval to assist the employees: By deploying tools that summarize client conversations, our customer advisors benefit from a summary of contact histories and support from a knowledge assistant integrated with internal knowledge bases and process documentation systems. “By deploying these tools, we can leverage Large Language Models (LLMs) for what they do best: efficient information distillation and information retrieval. This empowers employees to focus on delivering high-quality customer service.

The proof is in the volume. In December alone, we delivered over a million summaries to more than 20k unique users, supporting over 300k interaction. This means that if we only made the conversations on the helpdesk a single minute more efficient, we would save every person calling us a minute, and our employees combined 6,000 hours of tedious work.

The "Translation Layer" is Critical

Our biggest lesson is that technical capability is secondary to context. Success depends less on the model itself and more on the translation of business needs, connecting pain points and process realities with technical possibilities. Success requires a deep and pragmatic understanding of the human side of banking and mapping AI capabilities to those constraints. In my role as Business Analyst, navigating this intersection continues to be the biggest challenge.

Aligning AI with Business Value

Through Deep Dives and Value Discovery sessions with the operations department, we identified where Generative AI truly serves the business. These explorations were extremely useful, clarifying how this emerging technology can add tangible business value. Observing how we serve and interact with our customers at Rabobank provided critical insights into designing assistants, specifically defining what they can and can’t do effectively.

Innovation in Action: The Case Summarizer

Our latest development is an AI assistant engineered to handle activities in our CRM system. By utilizing prompt chaining, we linked multiple AI components together to and create a summary of the current dossier plus retrieve relevant knowledge for the employee to review. Usually, either multiple human agents work on the same dossier, and/or the history of the request is important. This assistant reduces the time the employee spends “studying” the dossier to understand what they need to do. The case summarizer not only accelerates current workflows, but also establishes the architectural groundwork for future AI assistants, opening new possibilities for developing and scaling our assistants.

Future Outlook

While model innovation continues, the real challenge has shifted toward embedding AI reliably into existing systems, governance structures and daily processes. Our focus is now on scaling what works. Deepening integration, improving trust and ensuring AI consistently delivers business value where it matters most.