After extensive experimentation and application, American banks have conclusively demonstrated that artificial intelligence (AI) is not just a promising technology, but a crucial business capability. As AI becomes more integrated in core banking operations, figures show that the US AI in banking market is predicted to grow from $14.5 billion in 2022 to a whopping $31.2 billion by 2031. The driving forces behind this growth are practical and clear: cost optimization, bolstering cybersecurity, and enhancing the customer experience. It’s evident that banks are increasingly deploying AI-powered solutions to automate their operations, detect and prevent fraud, and provide real-time, personalized customer services.
US banks are at the forefront of a global trend
While US banks are pioneering this transition, the maturity of AI is advancing across the global banking industry. The Infosys Bank Tech Index reveals that banks worldwide are making more calculated technology choices, with a clearer understanding of where AI can deliver a significant impact. This shift in approach is reflected in changing strategic priorities. In 2023, cost reduction was a dominant factor influencing AI investment decisions. Today, while cost remains a crucial driver, it is closely followed by innovation and business growth.
Banks are no longer experimenting indiscriminately and are increasingly exiting projects with weak ROI. The latest edition of Infosys Bank Tech Index found that participating banks had canceled 6,100 AI initiatives before deployment. That figure rose 33% to 8,100 in the latest study. However, cancellations after deployment declined, indicating a stronger upfront discipline. As a result, approximately 59% of deployed AI initiatives among participating banks are now generating measurable business value.
Business value and cost optimisation go hand-in-hand
Customer service has emerged as the most valuable AI use case for banks. Intelligent virtual assistants, automated service workflows, and personalized engagement are reducing service costs while improving satisfaction among digital customers. AI-powered solutions have considerably reduced call volumes at Citizens Bank, for example, and significantly cut call times at Danske Bank.
Sales and marketing, cybersecurity, and business operations are other areas where banks are realizing significant value from AI. These functions also offer some of the greatest opportunities for cost reduction. One notable exception is software engineering, which ranks lower in near-term business value. However, AI-assisted coding and testing are expected to reduce the unit cost of software development over time.
Scaling remains the hardest challenge
Despite growing AI maturity, scaling AI across the enterprise remains a challenge. Legacy systems, poor data quality, unclear business cases, and high operating costs are some of the obstacles that banks need to overcome. Achieving scale with AI requires close collaboration among AI engineers, data scientists, business leaders, and compliance teams. However, talent shortages, limited AI fluency, particularly at senior levels, and cultural resistance continue to slow adoption.
For US banks, addressing these barriers is urgent. AI is already a powerful differentiator in banking, but its benefits accrue unevenly. Meaningful returns emerge only when AI is scaled across the enterprise. Institutions that fail to move beyond experimentation risk leaving significant cost, productivity, and profitability gains unrealized.
The gap between leaders and laggards is widening. The ten most AI-mature banks are advancing far faster than their peers and capturing measurable business value through early, disciplined investments in AI. The next phase for US banks will be converting that maturity into enterprise-wide execution at scale.
Ajay Bhandari is SVP and Regional Head of US Banking at Infosys
Source: Here
