The Rise of AI and Generative Technologies in Banking: Internal Use Cases and Transformation Impact

Introduction

Artificial intelligence (AI) and generative technologies are rapidly reshaping the global banking landscape. No longer confined to experimental pilots or customer-facing chatbots, these technologies are now at the heart of banks’ internal operations, driving efficiency, accuracy, and agility at scale. As banks worldwide accelerate their digital transformation journeys, the focus is shifting toward enterprise-wide adoption of AI—particularly for internal, non-customer-facing use cases such as credit analysis, risk measurement, document automation, and portfolio management. This page explores how banks are prioritizing AI, the operational impact across markets, and the challenges and opportunities in scaling these technologies.

AI and Generative Technologies: A Global Priority

Across all major banking markets, AI and generative technologies have emerged as top transformation priorities. In the United States, 53% of banks cite AI and emerging technologies as their number one priority for the next three years, with similar focus in the United Kingdom (45%), Germany (47%), and Australia (31%). Internal use cases dominate current investments: between 50% and 66% of banks in major markets are pursuing generative AI for transactional and operational tasks, including credit analysis, portfolio management, underwriting, risk measurement, and document automation (such as legal contracts, proposals, and RFPs).

Banks recognize that the greatest potential of AI lies in making processes more efficient, profitable, and faster. For example, 75% of U.K. banks and 83% of French banks believe AI’s biggest impact will be on operational speed and efficiency, rather than simply improving accuracy. This is reflected in investment priorities: 76% of U.K. banks and 67% of French banks say they will prioritize non-customer-facing generative AI over the next three years to improve internal capabilities.

Internal Use Cases: From Credit Analysis to Document Automation

The adoption of AI and generative technologies is transforming core banking operations:

Credit Analysis and Risk Measurement: AI models are being used to automate and enhance credit decisioning, risk scoring, and portfolio risk management. These tools enable faster, more consistent, and data-driven decisions, reducing manual effort and improving compliance.
Document Automation: Generative AI is streamlining the creation, review, and management of legal contracts, proposals, pitch documents, and regulatory filings. This not only accelerates turnaround times but also reduces errors and frees up skilled staff for higher-value work.
Portfolio Management: AI-driven analytics are supporting portfolio optimization, scenario analysis, and real-time risk monitoring, enabling banks to respond more quickly to market changes and regulatory requirements.
Underwriting and Transaction Processing: Automated underwriting and transaction processing powered by AI are reducing operational costs and improving accuracy, particularly in high-volume, low-margin business lines.

These internal applications are not only improving efficiency but also enabling banks to redeploy talent to more strategic initiatives, foster a culture of innovation, and respond more nimbly to regulatory and market changes.

Adoption Rates and Investment Priorities Across Markets

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

United States: 65% of banks are pursuing internal generative AI use cases, with a strong emphasis on credit analysis, portfolio management, and document automation. U.S. banks are also prioritizing data and analytics (37%) and talent development (32%) as part of their operational transformation.
United Kingdom: 76% of banks plan to prioritize non-customer-facing generative AI, with 60% already pursuing transactional use cases. Investment in machine learning, AI, and generative AI accounts for 30% of customer experience transformation budgets.
France: 67% of banks will focus on internal generative AI, with 61% already deploying these technologies for credit, risk, and document-related tasks. French banks also cite technology and data platforms (44%) and cloud migration (34%) as key enablers.
Canada and Australia: Over half of banks in these markets are investing in generative AI for internal use, with a particular focus on data-driven insights and operational efficiency.
Southeast Asia and Germany: AI adoption is accelerating, with 50% of German banks and 40% of Southeast Asian banks prioritizing intelligent technologies for internal transformation.

Operational Impact: Efficiency, Agility, and Competitive Advantage

The operational impact of AI and generative technologies is profound. Banks that have embraced these tools report:

Lower cost-to-serve: Automation at scale is driving down operational costs and enabling banks to do more with less.
Faster decision-making: Real-time analytics and automated workflows are reducing turnaround times for credit, risk, and compliance processes.
Improved accuracy and compliance: AI-driven processes reduce manual errors and support more consistent application of regulatory requirements.
Enhanced agility: Banks with agile operating models and modern data architectures are able to scale AI use cases quickly and respond to new opportunities and risks.

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 ecosystems, and a strong focus on talent and culture. These banks are not only ahead in AI adoption but also in their ability to innovate and compete with digital-first challengers.

Challenges in Scaling AI: Talent, Data, and Regulation

Despite the momentum, banks face significant challenges in scaling AI and generative technologies:

Talent and Skills: A lack of workforce skills or willingness to embrace change is a top barrier, cited by up to 33% of banks in some markets. Investment in upskilling, reskilling, and attracting digital talent is as important as technology investment itself.
Data Access and Quality: Many banks struggle to access the data they need, when they need it, and in a usable format. Common challenges include siloed data, inconsistent taxonomies, and concerns over data security and privacy.
Regulatory Complexity: Regulatory challenges are a top-three barrier in most regions, particularly in Southeast Asia, Australia, and France. Banks must ensure that AI models are explainable, auditable, and compliant with evolving regulations.
Legacy Technology: Outdated systems and infrastructure can slow down the deployment and scaling of AI solutions, making cloud migration and modernization a parallel priority.

Opportunities: Charting a Path Forward

Banks that overcome these challenges stand to gain significant competitive advantage. Actionable steps include:

Invest in Talent and Culture: Prioritize upskilling, reskilling, and fostering a culture of innovation and agility.
Modernize Data Architectures: Break down silos, invest in cloud and data platforms, and ensure data is accessible, secure, and high quality.
Embed AI in Core Operations: Focus on high-impact, internal use cases that drive efficiency and free up talent for strategic work.
Strengthen Regulatory and Ethical Frameworks: Build robust governance for AI, ensuring transparency, fairness, and compliance.
Leverage Partner Ecosystems: Collaborate with fintechs, technology providers, and other partners to accelerate innovation and scale.

Conclusion

The rise of AI and generative technologies marks a new era in banking transformation. As banks move from experimentation to enterprise-wide deployment, the focus on internal, non-customer-facing use cases is delivering tangible operational benefits—lower costs, greater agility, and improved risk management. The banks that act boldly, invest in talent and data, and build agile, data-driven cultures will define the future of banking. Publicis Sapient partners with leading banks worldwide to accelerate this journey, helping them navigate local complexities while adopting global best practices. The time to scale AI is now—and the opportunity is immense.