Cyril Cymbler, Head of Financial Services EMEA & Strategic Customers, Databricks
Financial institutions increasingly view generative AI, and now AI agents, as essential tools for their future. These autonomous systems, capable of collecting data and performing self-directed tasks, are moving beyond experimental concepts and becoming fundamental drivers of transformation. Recent KPMG research highlights that, positively, half (51%) of the financial sector say AI is reshaping their business. On the other hand, almost three-quarters (72%) are concerned about data quality – a massive hurdle in the adoption of the technology because it doesn’t just present a technical problem, it leads to strategic risk.
Those financial institutions that effectively close this gap by establishing a foundation of secure, adaptable infrastructure and unified data governance will be the ones who benefit from a competitive edge from data and AI. But how can financial organisations successfully deploy AI at scale and overcome the adoption gap?
A practical roadmap for scaling AI
Most AI pilots fail both because the data beneath them are fragmented, poor quality or locked away in silos and because their AI agents do not have a focus on measuring and improving quality and accuracy. In order to successfully deploy AI, the infrastructure must be set up correctly to harness results.
For leaders in the financial sector to close the AI adoption gap, a structured roadmap should be in place to enable their business to move from experimentation to scaled impact. The first step is to unify data silos under a single platform to eliminate duplications, reduce inefficiencies and build reliable, trusted models from a single source of truth.
From there, governance must be embedded to manage lineage, access and audit trails. For AI agents, governance is far more than a mere compliance exercise. A unified governance model treats agents with the same rigour as human staff, applying robust access controls and security measures.
Prioritising explainability is equally crucial. In a highly regulated market, businesses need accessible, transparent models that demonstrate how results are produced. Additionally, adopting a “start small, scale fast” strategy demonstrates impact early, fosters internal trust and establishes a replicable model for safely and responsibly expanding AI across the company.
The promise vs reality
Leaders in the financial industry are no longer asking where AI works, but instead, where it can deliver the most impact. The potential is enormous, but the gap between ambition and execution is slow to close. At present, hype is outpacing reality. A recent Gartner survey shows that finance AI adoption jumped from 37% in 2023 to 58% last year, however momentum is now slowing, showcasing the gap between experimentation and enterprise scale.
Despite varying regulatory environments, firms across banking, payments, capital markets and asset management align on the same strategic objectives driving AI adoption. Businesses must acknowledge that to deliver these ambitions consistently at scale, the challenge is not in the vision, but in bringing together fragmented data assets and legacy infrastructure.
Driving growth and revenue
The financial industry recognises the value that AI technology can offer by boosting efficiency and driving growth, we can see that in the uptick of adoption of the technology. Smarter customer segmentation and hyper-personalisation allow enterprises to differentiate their brand and elevate customer experience, creating a significant advantage over their competitors. In payments and mortgages for example, AI-powered product innovations such as real-time fraud prevention and property valuation models are transforming journeys and reshaping how institutions deliver their services.
However, implementing individual use cases is not enough to translate these skills into long-term revenue development; a clear business strategy is also necessary. Financial organisations must prioritise use cases with quantifiable ROI, align AI operations to particular business goals, and make sure that data foundations enable models to be constantly refined.
Managing risk and resilience
In financial services, risks can appear in minutes, from cyber threats to fraud disruption. The speed, complexity and sheer volume of these problems are too much for traditional manual methods to handle.
AI agents are quickly becoming the new competitive frontier to improve quality and accuracy. Unlike static models, these systems can act almost like virtual employees that take actions autonomously. In mission-critical areas such as fraud detection, anti-money laundering (AML) and cybersecurity, agents monitor, orchestrate and conduct checks with far greater speed and reliability than manual teams. Mastercard, the global technology payment card service, is a great example of this. Since combining AI systems with strong governance and a human-in-the-loop approach, the company has improved fraud detection and provided more efficient tools for their stakeholders.
Operating in one of the strictest regulatory industries, AI agents provide a means for organisations to keep ahead of the risks while preserving the integrity of key operations. Rather than replacing human judgement, AI agents enhance it; enabling teams to react with greater assurance.
Boosting operational efficiency
Advanced AI tools are changing the game for financial services, driving innovation and agility. AI agents can automate repetitive business processes allowing institutions to “do more with less”, reducing workloads which allow teams to focus on higher-value, customer orientated work.
AI-driven customer service assistants are also already delivering measurable impact. Trained on enterprises’ own data, they can answer questions accurately and automate much of the triage process. The results are fewer manual bottlenecks, elevated customer experiences and a more resilient operational model.
The blueprint for the future of finance
Many financial institutions understand why they need AI, but few have mastered how to use it effectively and efficiently. To get there, they must treat governance and incorporate robust data foundations as core pillars of their data and AI strategy. They must embed rigorous monitoring and auditability into the agent lifecycle, and ensure every system is built from a consistent business context.
The winners will be those who start small, scale fast and treat these pillars as strategic imperatives, not afterthoughts.
