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

Artificial intelligence (AI) and generative technologies are no longer just buzzwords in banking—they are rapidly becoming the backbone of operational transformation for financial institutions worldwide. As banks move from isolated pilots to enterprise-wide adoption, the focus is shifting toward internal, non-customer-facing use cases that drive efficiency, innovation, and competitive advantage. This page explores how banks across regions are leveraging AI, the pace and focus of adoption, the main barriers they face, and what sets transformation leaders apart.

From Experimentation to Enterprise-Wide Adoption

Globally, banks recognize that digital capabilities are mission critical. The COVID-19 pandemic accelerated digital adoption, exposing gaps in both customer experience and operational agility. Today, 83% of banks report having a clearly articulated digital transformation strategy, yet more than half admit they have yet to make significant progress on execution. The gap between aspiration and action is especially evident in the adoption of AI and generative technologies.

While early AI initiatives often focused on customer-facing chatbots or fraud detection, the current wave of investment is centered on internal, transactional use cases. Banks are deploying AI to automate and enhance processes such as:

• Credit analysis and underwriting
• Risk measurement and management
• Document automation (e.g., legal contracts, RFPs, pitch documents)
• Portfolio management and optimization

In major markets, 50-66% of banks are actively pursuing these internal generative AI applications, with the U.S., U.K., Germany, France, Canada, and Australia all reporting a strong focus on non-customer-facing AI to improve operational efficiency and decision-making.

Regional Trends: A Comparative View

United States

• AI and emerging technologies are the top priority for 53% of banks over the next three years.
• 65% are focused on internal generative AI use cases, such as credit analysis and portfolio management.
• Main barriers: legacy technology (38%), operational agility (38%), and regulatory complexity (36%).

United Kingdom

• 45% cite AI and emerging technologies as their number one priority, just behind cybersecurity.
• 76% will prioritize non-customer-facing generative AI to improve internal capabilities.
• Barriers include budget constraints (36%), legacy tech (32%), regulation (31%), and skills gaps (29%).

France

• 67% will prioritize non-customer-facing generative AI over the next three years.
• 61% are already pursuing transactional generative AI use cases.
• Key challenges: lack of access to data (39%), unified strategy (37%), and regulatory/technology hurdles (36%).

Germany, Australia, Canada, Southeast Asia

• Similar patterns emerge, with 46-55% of banks prioritizing generative AI for internal use.
• Legacy technology, regulatory issues, and skills gaps are common barriers.
• Southeast Asia stands out for its high commitment to diversity, equity, and inclusion (42%) and ESG-driven transformation (65%).

What Sets Transformation Leaders Apart?

Banks making the most progress—transformation leaders—share several traits:

• Customer-led culture: 95% say customer centricity drives key decisions, even for internal transformation.
• Agile operating models: 99% have embraced agility at scale, enabling rapid deployment of AI solutions.
• Platform-based, data-driven approaches: Leaders invest in cloud, AI, and analytics to enable real-time insights and innovation.
• Partner ecosystems: 98% have broad partner networks to compete with digital-first challengers and scale AI adoption.
• Talent and culture: Investment in upskilling, reskilling, and cultural change is as important as technology itself.

Overcoming Barriers: Legacy Tech, Skills, and Regulation

Despite the momentum, banks face persistent challenges:

• Legacy technology: Outdated systems hinder the integration and scaling of AI solutions. In Canada, 48% cite legacy tech as a top barrier; similar figures are seen in the U.S., U.K., and Australia.
• Skills gap: The shortage of digital and AI talent is a universal concern. Banks are investing in both upskilling existing staff and hiring new talent with digital skill sets.
• Regulatory complexity: Especially acute in Southeast Asia, France, and Australia, where evolving regulations around data privacy and AI use require constant adaptation.

Use Cases: Efficiency, Innovation, and Competitive Edge

The most common internal AI use cases include:

• Credit analysis and risk measurement: Automating data gathering and analysis to speed up decision-making and improve accuracy.
• Document automation: Using generative AI to draft, review, and manage legal contracts, proposals, and compliance documents, reducing manual workload and errors.
• Portfolio management: AI-driven insights for asset allocation, risk assessment, and performance optimization.
• Operational process automation: Streamlining back-office functions, from compliance checks to internal reporting.

Banks report that the greatest value from AI lies in making processes more efficient, profitable, and faster—rather than simply doing them better or more accurately. For example, 75% of U.K. banks and 83% of French banks believe AI’s biggest potential is in efficiency and speed.

The Road Ahead: Accelerating AI Adoption

To accelerate the journey from experimentation to enterprise-wide AI adoption, banks should:

1. Benchmark against global peers: Identify gaps in customer experience, operational agility, and technology adoption.
2. Prioritize data and AI: Invest in modern data architectures and AI capabilities to enable personalization, efficiency, and innovation at scale.
3. Accelerate cloud migration: Modernize core banking systems to unlock agility and support new digital business models.
4. Foster a culture of agility: Break down silos, empower cross-functional teams, and invest in talent development.
5. Embed ESG and DEI: Move beyond intention to action by developing robust data and processes for ESG measurement and DEI commitments.

Conclusion: Defining the Future of Banking

The rise of AI and generative technologies is fundamentally reshaping banking operations worldwide. While the pace and focus of adoption vary by region, the direction is clear: banks must become more agile, data-driven, and innovative to thrive. Those who act boldly—investing in technology, talent, and culture—will define the future of banking, delivering not only superior customer experiences but also operational excellence and sustainable growth.

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