Agentic AI In Finance

Agentic AI holds massive potential for financial services, but most firms struggle to get it out of the lab and into production. he opportunity cost of leaving agentic AI in the lab is enormous. McKinsey estimates that AI and advanced analytics could unlock up to  in additional annual value for global banking, spanning revenue uplift through personalization, cost reduction through automation, and better risk decisions.  Other analyses similarly see up to  in incremental banking revenue pools by 2030, with cost‑to‑income ratios improving through lower compliance, operations, and service costs.

Generative and agentic AI expand this value pool beyond traditional predictive models by enabling multi‑step reasoning and task orchestration, such as automatically gathering context, proposing actions, and coordinating workflows with humans in the loop.  Institutions that operationalize these capabilities securely and reliably stand to move from isolated efficiency gains to structurally different customer experiences, risk postures, and operating models.

The real hurdle isn’t the technology itself; it’s the delicate balance between innovating quickly and controlling risk. It’s no surprise that nearly two-thirds of European data leaders say less than half their AI pilots ever see the light of day, as a recent survey conducted by Informatica found.

Another major roadblock is proving the return on investment (ROI). Many in the industry can’t demonstrate the value of their AI projects and as a result, 35% have trouble getting buy-in from senior executives.

But, overcoming these challenges could be incredibly profitable. Some research suggests that AI could generate up to $1 trillion in extra value each year for the global banking industry alone. The good news is, there’s a clear path forward.

Land And Expand

Some financial institutions have already discovered how to strike the right balance between innovation and risk. They use a land and expand strategy, starting small and proving the value of their agentic AI deployments in a focused way.

These successful organizations begin with specific, high-impact use cases where they can quickly show results. Think about real-time AI tools for fraud detection, credit risk assessment or customer service routing. Once the technology proves to be effective, safe and genuinely valuable, they can make a strong case for a wider rollout.

This strategy offers multiple starting points. Here are three ways financial institutions can use a land-and-expand approach to deploy agentic AI:

Start With A Proof Of Concept

When rolling out new technology, you have to show measurable results from the very beginning. Leaders won’t invest in solutions that might be valuable in the future; they want to see the impact right now.

To meet this demand, teams implementing new solutions can start with simple, single-function AI tools that deliver a clear impact in a short amount of time. This approach makes it easy to show leaders the technology’s effectiveness and safety, which in turn unlocks more budget and paves the way for more sophisticated tools down the line.

Improve Data Quality

An AI is only as good as the data it’s built on. That’s why 77% of European data leaders are increasing their investment in data management this year, with nearly half (45%) making data readiness for AI their top priority.

The good news is that AI can also help solve the very data problems that hold it back. For example, financial institutions are using AI to fix mismatched and outdated records in their accounts receivable departments. This gives teams a clearer picture of what’s owed and helps them follow up more quickly.

This strategy offers a twofold return: you get stronger collections today while building a solid foundation for future automation. Once the records are clean, firms can expand into automated follow-ups and real-time tracking, which reduces manual work and allows employees to focus on exceptions instead of repetitive tasks.

Reduce the Compliance Burden

Regulatory compliance consumes a huge amount of time and resources. It often requires teams to manually gather data and input it into countless checklists and forms. Manually preparing reports for standards like BCBS 239, SOC-2 or DORA is both slow and expensive.

AI can take on a lot of this work. With a human-in-the-loop, AI can quickly gather relevant data and fill in the required forms. A trained compliance employee can then perform a final review, making the reporting process faster and less prone to errors while significantly reducing manual effort.

However, this depends on accurate and current data. If a business is confident in its data, it can move forward with this strategy. If not, using AI to improve data quality first will ensure the best results.

The Path Forward

Starting with small, achievable steps is always the best way to accomplish a large task and deploying agentic AI is no different. By starting with contained and measurable use cases, financial institutions can prove value quickly, get executive buy-in and build a strong foundation for wider adoption. Those who take this land-and-expand approach will move faster, manage risk more effectively and set themselves up for future innovation.