The Rise of AI and Generative Technologies in Banking: Internal Use Cases and Strategic Priorities

Introduction

The global banking sector is undergoing a profound transformation, driven by the rapid rise of artificial intelligence (AI) and generative technologies. While much attention has been paid to customer-facing innovations, banks are increasingly prioritizing internal, non-customer-facing use cases—such as credit analysis, risk measurement, document automation, and portfolio management—to drive operational efficiency, reduce costs, and build competitive advantage. This page explores how banks worldwide are adopting and scaling AI, the strategic priorities shaping their investments, and the challenges and opportunities that lie ahead.

AI and Generative Technologies: From Experimentation to Enterprise Adoption

Across all major banking markets, AI and generative technologies have moved from the periphery to the core of digital transformation strategies. In the United States, 53% of banks cite AI and emerging technologies as their top priority for the next three years, with similar focus in the United Kingdom (45%), Germany (47%), and Australia (31%). Internal use cases are at the forefront: 65% of U.S. banks, 61% of French banks, 60% of U.K. banks, and 56% of Canadian banks are actively pursuing generative AI for transactional, non-customer-facing applications.

Key Internal Use Cases

These use cases are not only improving efficiency but also freeing up skilled staff to focus on higher-value activities, accelerating time-to-market for new products, and strengthening compliance and auditability.

Adoption Rates and Investment Priorities: A Global Perspective

Banks’ commitment to AI is reflected in their investment priorities. In the U.S., 40% of banks are prioritizing generative AI for internal use, while 46% of Canadian banks and 67% of French banks are making similar investments. In the U.K., 76% of banks say they will prioritize non-customer-facing generative AI over the next three years. Across Germany and Australia, roughly half of banks are focused on internal generative AI use cases.

Operational transformation priorities consistently include:

Impact on Operational Efficiency

The impact of AI on operational efficiency is already tangible. Banks that have embraced automation, AI, and cloud at scale are seeing lower cost-to-income ratios, faster decision cycles, and improved accuracy in core processes. For example, 83% of French banks and 75% of U.K. banks believe AI’s greatest potential lies in making processes more efficient, profitable, and faster. Transformation leaders—those making the most progress—are characterized by their operational agility, platform-based approaches, and deep investment in intelligent technologies.

Regional Nuances and Benchmarking

While the direction of travel is clear, the pace and focus of AI adoption vary by region:

Challenges in Scaling AI Initiatives

Despite strong momentum, banks face several challenges in scaling AI:

Preparing the Workforce for AI-Driven Transformation

Leading banks recognize that technology investment must go hand-in-hand with cultural and organizational change. Transformation leaders are:

Opportunities Ahead

The shift to AI-powered operations is creating significant opportunities:

Conclusion: Charting a Path Forward

The rise of AI and generative technologies marks a new era in banking operations. Banks that act boldly—investing in intelligent technologies, modernizing their data and technology platforms, and preparing their workforce for change—will define the future of the industry. The most successful banks are those that:

Publicis Sapient partners with leading banks worldwide to accelerate digital transformation, helping them navigate local complexities while adopting global best practices. As the industry continues to evolve, those who act decisively will shape the next generation of banking.