From hype to value: This is how to apply GenAI step by step in your (financial) organisation.

Many financial organisations and departments are now sitting on a mountain of data, but the step to generating truly relevant insights is often still a bridge too far. They also have to deal with high governance and security requirements. So it is important to proceed carefully when implementing GenAI. How? In this article you will read why GenAI is relevant, which steps are needed to apply it in a valuable way, and how other organisations have already gone before you.

Why is GenAI so relevant for (financial) organisations right now?

The pressure is palpable. Competitors are deploying AI in various areas, such as process optimisation, faster decision-making or better customer interaction. Customers additionally expect real-time insight and quick response. At the same time, many financial institutions struggle with fragmented data and a traditional infrastructure.

GenAI offers solutions here. Think about:

• Summarising (multiple) large documents such as annual reports or consulting the Internet (e.g. for Adverse Media Screening)
• Answering internal policy and compliance questions from employees
• Bringing together structured data (from tables) and unstructured data (from documents)
• Generating reports without involving a BI team
• Interpreting customer queries and preparing an appropriate response.

But to really harness that value, you need more than a tool or a pilot. You need structure and a clear plan.

Step-by-step approach: here’s how to integrate GenAI into your organisation

Integrating GenAI into an organisation requires more than just experimenting.
That is why we have created a concrete roadmap below, which allows you to get started in a valuable, secure and scalable way.

Step 1: Find the right use case

Don’t start with technology, but with a problem. Where is there a lot of repetitive work? Where do employees analyse large amounts of text daily? What question crosses several departments each time? And more importantly, what provides immediate ROI? Think credit ratings, customer queries, policy documents or process improvements. This is precisely where GenAI can deliver immediate value.

Step 2: Develop a proof of concept

Quickly demonstrate the potential with a limited test. One of our clients uses GenAI to summarise reports so reviewers can make decisions faster. The result? Reduced time effort, more consistency and faster decision-making.

Step 3: Check your data foundation

Do you have the right data in the right place and can you access it securely? GenAI works best with textual data (PDFs, reports, emails) that you may want to use in combination with structured data. In that case, however, it is important that the solution knows where and also what it is allowed to take. For example: you don’t want the solution to be able to retrieve salaries of all employees. Many organisations collect data, but that structure is often still missing. That requires a solid data foundation.

Step 4: Build a scalable data platform

A stand-alone tool won’t get you anywhere. If you want GenAI to really work for your organisation, you need a platform that aggregates data, secures it and makes it available to the organisation. Not a proof of concept, but a production-ready solution. A platform solution that can be deployed operationally, but also an environment to continue developing in. That is where the real value lies, for now and for the future.

Step 5: Ensure governance and control

The financial sector has high demands for compliance, data security and traceability. GenAI should not be a black box. Make sure you can trace AI output, that data flows are shielded, and that human validation remains possible. It is also important to keep a grip on your costs. Without that certainty, you will not deploy GenAI with confidence.

Step 6: Operationalise and keep learning

A successful AI application is alive. Use the output, monitor performance and cost, and learn from usage. Keep optimising, add new resources and expand step by step to other domains within your organisation.

GenAI is not future music. It is now.

For organisations, there is huge potential in GenAI. The financial sector works with large volumes of data, has complex rules and high customer expectations. Exactly that combination makes GenAI so relevant, provided you get it right. Start with the question, choose the right use case, and build from there.

At Blenddata, we help you not only with technology, but also with developing your business case. Using our Generative AI Maturity Model, we can quickly assess the complexity of the use case(s), and design and develop a solution that fits this complexity.

Would you like to use the Generative AI Maturity Model or are you curious about what else we do? Then click on one of the buttons below: