The Rise of AI and Generative Technologies in Banking: Global Trends and Use Cases

Artificial intelligence (AI) and generative technologies are rapidly transforming the global banking sector, moving from experimental pilots to enterprise-wide adoption. As banks face mounting pressure from digital-first challengers, evolving customer expectations, and regulatory demands, AI is emerging as a cornerstone of operational transformation—especially in internal, non-customer-facing domains. This page explores how banks worldwide are prioritizing AI and generative technologies, compares adoption and investment trends across regions, and highlights the challenges and opportunities that define the next era of banking transformation.

Why AI and Generative Technologies Now?

Across all major banking markets, digital capabilities are no longer optional—they are mission critical. The COVID-19 pandemic accelerated digital adoption, exposing gaps in both customer experience and operational agility. While 83% of banks globally report having a clearly articulated digital transformation strategy, more than half admit they have yet to make significant progress on execution. The gap between aspiration and action is particularly evident in the adoption of AI and generative technologies, which are now seen as essential to driving efficiency, innovation, and competitive advantage.

Internal, Non-Customer-Facing Use Cases: Where AI Delivers Value

Banks are increasingly focusing their AI investments on internal, transactional, and non-customer-facing applications. These use cases are delivering tangible benefits in efficiency, risk management, and decision-making. Key areas of adoption include:

Credit Analysis and Underwriting: AI models are automating the assessment of creditworthiness, enabling faster, more accurate lending decisions while reducing manual effort and bias.
Risk Measurement: Machine learning algorithms are enhancing risk modeling, stress testing, and scenario analysis, allowing banks to respond more dynamically to market volatility and regulatory requirements.
Document Automation: Generative AI is streamlining the creation and review of legal contracts, proposals, RFPs, and pitch documents, significantly reducing turnaround times and operational costs.
Portfolio Management: AI-driven analytics are supporting portfolio optimization, asset allocation, and real-time risk monitoring, empowering banks to make data-driven investment decisions at scale.

Globally, between 50% and 66% of banks in major markets are actively pursuing these internal generative AI use cases, with the U.S., U.K., Germany, France, Canada, and Australia all reporting strong momentum in this area.

Regional Trends: Adoption Rates and Investment Priorities

While the imperative to adopt AI is universal, the pace and focus of investment vary by region:

United States: 53% of banks cite AI and emerging technologies as their top priority for the next three years, with 65% focusing on internal generative AI use cases. Operational agility and legacy technology remain key barriers, but U.S. banks are leading in enterprise-scale AI adoption.
United Kingdom: 45% of banks prioritize AI and emerging tech, with 76% planning to invest in non-customer-facing generative AI. Efficiency, speed, and process automation are seen as the greatest potential benefits.
France and Germany: Both markets are investing heavily in AI for internal transformation, with 67% of French banks and 50% of German banks focusing on generative AI for credit, risk, and document automation. Data access and regulatory complexity are notable challenges.
Canada and Australia: Canadian banks are prioritizing generative AI for internal use (46%), while 55% of Australian banks are focused on custom AI tools for operational efficiency. Both markets cite legacy technology and data access as significant hurdles.
Southeast Asia: 40% of banks are investing in intelligent technologies, with a strong emphasis on using AI to deepen customer understanding and drive operational efficiency. Regulatory and legacy tech barriers are more pronounced in this region.

Perceived Benefits: Efficiency, Innovation, and Competitive Edge

Across regions, banks see the greatest potential for AI in making processes more efficient, profitable, and faster. For example, 83% of French banks and 75% of U.K. banks believe AI’s biggest impact will be in operational speed and cost reduction, rather than simply improving accuracy. Transformation leaders—those banks making the most progress—are distinguished by their ability to:

Embrace agile operating models (99% of leaders have done so)
Invest in platform-based, data-driven approaches (cloud, AI, analytics)
Build broad partner ecosystems to scale and innovate rapidly
Prioritize talent development and cultural change alongside technology

Challenges to Scaling AI: Data, Talent, and Regulation

Despite the clear benefits, banks face persistent challenges in scaling AI and generative technologies:

Data Access and Quality: Many banks struggle to extract and combine siloed data, lack trust in data provenance, and face inconsistent taxonomies and standards. This limits the ability to deploy AI at scale and realize its full potential.
Talent and Skills Gap: A shortage of AI and data science talent, combined with the need for upskilling and cultural change, is a major barrier. Leading banks are investing in both new talent and the development of existing teams to close this gap.
Regulatory Compliance: Navigating complex and evolving regulatory environments—especially around data privacy, model explainability, and ethical AI—remains a top concern, particularly in Europe and Southeast Asia.
Legacy Technology: Outdated core systems and fragmented IT architectures slow down AI adoption and integration, making modernization a parallel priority.

What Sets Transformation Leaders Apart?

Banks that are leading in AI adoption share several key traits:

Customer-Led Culture: 95% say customer centricity drives key decisions, even for internal transformation.
Agility at Scale: 99% have embraced agile operating models, enabling rapid experimentation and deployment of AI solutions.
Data-Driven Platforms: Leaders invest in cloud, AI, and analytics to enable real-time insights and innovation.
Partner Ecosystems: 98% have broad partner networks, allowing them to access new technologies and scale quickly.
Talent and Culture: Investment in upskilling, reskilling, and fostering a culture of innovation is as important as technology itself.

The Path Forward: Accelerating AI-Driven Transformation

As AI and generative technologies become central to banking operations, the most successful banks will be those that:

Benchmark Against Global Peers: Assess progress relative to global leaders and identify gaps in AI adoption, operational agility, and data maturity.
Prioritize Data and AI: Invest in modern data architectures and AI capabilities to enable efficiency, personalization, and innovation at scale.
Accelerate Cloud Migration: Modernize core banking systems to unlock agility and support new digital business models.
Foster a Culture of Agility: Break down silos, empower cross-functional teams, and invest in talent development to drive transformation at pace.
Navigate Regulation Proactively: Build compliance and ethical considerations into AI strategies from the outset.

Conclusion

The rise of AI and generative technologies marks a pivotal moment for global banking. While the journey is complex and regionally nuanced, the direction is clear: banks must become more agile, data-driven, and innovative to thrive. By focusing on high-impact internal use cases, investing in talent and technology, and learning from transformation leaders, banks can harness AI to drive efficiency, resilience, and sustainable growth in a rapidly evolving landscape.

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.