The Rise of AI and Generative Technologies in Banking: Internal Use Cases and Future Potential

Introduction

The banking industry is undergoing a profound transformation, driven by the rapid rise of artificial intelligence (AI) and generative technologies. As banks worldwide accelerate their digital agendas, the focus has shifted from experimentation to enterprise-wide adoption of AI, with a particular emphasis on internal, non-customer-facing applications. This strategic prioritization is reshaping operational models, unlocking new efficiencies, and laying the groundwork for the next wave of customer-centric innovation.

Why Internal Use Cases Lead the Way

Across major banking markets—including the U.S., U.K., France, Germany, Canada, Australia, and Southeast Asia—banks are prioritizing AI and generative technologies to address internal challenges before turning to customer-facing applications. The rationale is clear: internal use cases offer immediate, measurable benefits in efficiency, risk management, and cost reduction, while also providing a controlled environment to build organizational confidence and capability in AI.

Key Internal Applications

Recent data shows that 50-66% of banks in major markets are actively pursuing these internal generative AI use cases. For example, in the U.S., 65% of banks are focused on transactional generative AI for credit analysis, portfolio management, and document automation. In the U.K. and France, roughly 60% of banks are prioritizing similar applications, with a strong emphasis on efficiency and risk reduction.

Regional Adoption and Strategic Rationale

While the adoption of AI is a global phenomenon, regional nuances shape the pace and focus of implementation:

The Benefits of an Internal-First Approach

Focusing on internal use cases allows banks to:

The Future Trajectory: From Internal Efficiency to Customer-Centric Innovation

As banks gain confidence and capability with AI, the focus will inevitably shift toward customer-facing applications—such as personalized product recommendations, conversational banking, and real-time financial advice. However, realizing the full potential of AI will require significant organizational change:

Regional Differences and Global Best Practices

While the direction of travel is consistent, the pace and focus of AI adoption vary by region. For example, Southeast Asian banks lead in diversity, equity, and inclusion (DEI) commitments and are more likely to embed ESG (environmental, social, and governance) considerations into their transformation strategies. In contrast, U.S. and European banks are more focused on operational efficiency and risk management as immediate priorities.

Transformation leaders—those making the most progress—share several traits: a customer-led culture, agile operating models, platform-based and data-driven approaches, broad partner networks, and a relentless focus on talent and culture. These banks are setting the benchmark for the industry, demonstrating that the journey to AI maturity is as much about people and process as it is about technology.

Conclusion: Charting a Path Forward

The rise of AI and generative technologies marks a new era for banking. By prioritizing internal use cases, banks are building the foundation for sustainable, scalable transformation. As capabilities mature, the shift toward customer-facing innovation will accelerate, unlocking new sources of value and competitive advantage. The banks that act boldly—investing in technology, talent, and culture—will define the future of the industry.

Publicis Sapient partners with leading banks worldwide to accelerate digital transformation, helping them navigate local complexities while adopting global best practices. To learn more about how your bank can benchmark itself globally or adapt best practices from other regions, contact us today.