Artificial Intelligence (AI) is rapidly moving from isolated pilots to the core of banking strategy. Faced with margin pressures, rising compliance costs, and customer expectations shaped by digital-first industries, financial institutions are betting big
on AI to drive efficiency and unlock growth. A recent HFS and Infosys study indicates that 2025 budgets for AI are set to increase by 25%, representing 16% of total technology spend. This reflects not just optimism, but urgency. To remain competitive in a
rapidly evolving market, banks now see AI as essential.
AI use cases that deliver value
Customer experience remains a central battleground. Banks recognize the need to be faster, more predictive, and more responsive than competitors, and AI provides new ways to make this possible.
In areas like client service workflows, AI can automate time-consuming tasks, such as drafting responses, attaching relevant documentation, or applying knowledge across jurisdictions, while keeping humans in the loop for oversight. Processes that can take
a human hours, can be completed in minutes, giving organizations more flexibility in balancing speed, accuracy, and customer experience.
AI also brings clear value in regulatory monitoring. For global banks, keeping pace with shifting regulations is a constant challenge, with the risk of missed updates carrying serious compliance implications. Algorithms capable of analyzing text for subtle
semantic changes can flag potential issues far quicker than manual review. Applied in this way, AI allows banks to interpret regulatory shifts and adapt processes with greater agility.
Another pressure point is corporate banking, where the push to deliver digital experiences that reflect consumer expectations is accelerating. Many client journeys are still weighed down by manual steps and fragmented processes. AI can streamline these interactions,
reduce friction, and support a more modernised experience.
Data as the foundation
AI is, at its core, the automation of prediction. Its accuracy depends on high-quality, structured, and context-rich data. In complex, multi-jurisdictional environments, the ability to collect, integrate, and govern this data consistently is critical.
In KYC, for example, AI can predict what information is required, where it can be found, and how to connect disparate inputs into a complete picture. But without consistent, auditable, and contextualized data, models risk producing unreliable outcomes. The
combination of automated data collection and AI-driven correlation offers an opportunity to unify fragmented datasets and improve accuracy, but human oversight remains essential to validate anomalies and maintain trust.
Beyond silos: agentic AI
Until now, many AI deployments in banking have operated in silos, supporting specific tasks but requiring additional integration effort to connect workflows. This can be time-consuming and costly.
Agentic AI represents a potential shift, embedding models within broader ecosystems to enable systems to communicate more efficiently. Unlike generative AI, which often tackles large and unstructured prompts, agentic AI structures tasks into smaller, more
precise steps that map onto existing or redesigned processes. The result is better orchestration, more efficiency, and workflows that still keep humans in the loop for oversight and accountability.
Keeping data safe
As AI adoption grows, so does scrutiny of its security. Concerns around data privacy and integrity are central to adoption decisions. While risks exist, AI can also strengthen security by helping to detect anomalies, identify fraud patterns, and monitor
for vulnerabilities.
Governance is key. Many banks limit the use of personal productivity tools to vetted providers and operate AI within controlled environments behind the firewall. At the same time, the adoption of emerging standards such as ISO/IEC 42001, focused on responsible
AI management, points towards a framework for safe and ethical deployment.
ROI: signals of progress
The full ROI of AI is still emerging, but early signals are encouraging. Banks replacing manual, repetitive processes with automation report gains in agility, improved customer service, and reduced operational cost. In some cases, AI is helping predict customer
churn, reduce documentation burdens in KYC, and deliver more personalized engagement.
However, ROI is not universal. It depends heavily on the quality of data infrastructure, the maturity of integration, and the clarity of objectives. Measuring impact beyond cost savings, such as customer satisfaction, speed to market, or risk management,
is becoming just as important.
The case for action
The conclusion for senior banking executives is clear: waiting to see how AI plays out is not a viable option. Competitors are already making progress, and customer expectations are evolving fast. While the journey to maturity requires robust data foundations,
strong governance, and a clear understanding of risk, the opportunity for those who act is significant.
AI is no longer an experiment at the edges of banking. It is becoming a defining capability for growth, resilience, and competitive advantage.
