By Levent Ergin, Chief Climate, Sustainability & AI Strategist at Informatica
Agentic AI has huge potential in financial services. But getting it out of the lab and into production is where most firms stumble. The real challenge isn’t the technology; it’s the balancing act: moving fast enough to innovate while keeping risk under control. It’s no surprise then that almost two-thirds of European data leaders admit fewer than half their pilots ever make it into production.
Not only this, proving ROI of AI projects is also holding the industry back. 35% are struggling to secure exec buy-in for AI projects, as they’re unable to demonstrate the value.
However, overcoming these barriers could prove to be very lucrative. Research suggests that AI could deliver up to $1 trillion in additional value each year for global banking alone. And, fortunately, there is a path forward.
Light at the end of the tunnel
However, some financial services organisations have already found the sweet spot between innovation and risk management. They’re focusing on a land and expand approach.
Organisations seeing success are starting small, focusing on where they can prove the value of their agentic AI deployment. Such use cases include real-time AI tools for fraud mitigation, credit risk evaluation and customer service routing. Once, the technology has proven effective, safe, and to deliver genuine business value, then a case can be made for its wider roll out.
The best part about this strategy, is that there are a number of places to start. Below, I’ve listed three possibly ways a land and expand approach can be used to roll out agentic AI in financial services organisations.
1. Start with a proof of concept
Showing measurable results from the offset is imperative in rolling out agentic AI. Leadership isn’t likely to buy into solutions that could prove valuable in the future; they want to see impact now.
To meet business leaders where they are, those in charge of implementing new solutions can start with single-function AI tools that will make a clear impact in a short timeframe. Doing so allows for the efficacy and safety to be easily conveyed to leadership, unlocking budget and forging the path for more sophisticated tools.
2. Improve data quality
AI is only as good as the data behind it. That’s why 77% of data leaders in Europe are increasing investment in data management this year, with almost half (45%) saying their top priority is getting data ready for AI.
The good news is that AI can also help tackle the very data challenges that hold it back. In accounts receivable, for example, financial institutions are using AI to resolve mismatched records and outdated entries. This gives teams a clearer view of what’s owed and allows for more timely follow-ups.
The return is twofold: stronger collections today and a foundation for future automation. Once the records are clean, firms can expand into automated follow-ups and real-time tracking, reducing manual workloads and freeing staff to focus on exceptions instead of repetitive tasks.
3. Reduce the compliance burden
Regulatory compliance consumers significant time and resources. It often requires sourcing a great deal of data and manually inputting it into checklists and forms. Gathering data and manually completing reports for BCBS 239, SOC-2, or DORA is both slow and costly.
AI can take on a lot of this work. With a human-in-the-loop, reporting becomes faster less error-prone, reducing the manual workload significantly. AI can easily gather the relevant data and fill in the required forms, with a final review from a trained compliance employee.
To do so though, depends on accurate and up to date data. If businesses are confident in their data, then they can charge ahead. But if not, AI should be used here first to ensure the best results.
The land-and-expand path
Starting with small, achievable steps is the best way to complete any task. And rolling out agentic AI is no different. By starting with contained, measurable use cases, financial institutions can prove value quickly, gain buy-in, and build the foundations for wider adoption. Those that take a land and expand approach will move faster, manage risk more effectively, and pave the way for future innovation.
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