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

Artificial intelligence (AI), machine learning (ML), and generative AI are no longer experimental technologies in banking—they are now at the heart of digital transformation strategies for leading banks worldwide. As customer expectations evolve and competition intensifies from digital-first challengers and technology giants, banks across all regions are accelerating their adoption of intelligent technologies to drive efficiency, innovation, and growth.

AI as a Top Transformation Priority

Across the globe, banks are prioritizing AI and generative technologies as mission-critical investments. In the United States, 53% of senior banking leaders cite AI and emerging technologies as their number one priority for the next three years. The United Kingdom follows closely, with 45% of banks placing AI at the top of their transformation agenda. Similar trends are seen in Germany (47%), France (where 19% of customer experience investment is earmarked for AI/ML), Australia (31%), and Canada (46% prioritize generative AI for internal use). Southeast Asia is also rapidly advancing, with 40% of banks focusing on intelligent technologies to deepen customer understanding.

This global momentum is driven by a shared recognition: digital capabilities are no longer optional. The COVID-19 pandemic accelerated digital adoption, exposing gaps in customer experience and operational agility. Today, 83% of banks worldwide report having a clearly articulated digital transformation strategy, yet more than half admit they have yet to make significant progress on execution. AI is seen as the lever to close this gap between aspiration and action.

Most Common Use Cases: From Credit Analysis to Document Automation

Banks are moving beyond pilots and proofs of concept to deploy AI and generative technologies at scale, particularly in internal, non-customer-facing applications. The most prevalent use cases include:

Credit Analysis and Risk Measurement: Nearly two-thirds of banks in the U.S. (65%), U.K. (60%), France (61%), and Canada (56%) are leveraging generative AI for transactional tasks such as credit analysis, portfolio management, underwriting, and risk assessment. These applications enable faster, more accurate decision-making and free up human talent for higher-value work.
Document Automation: AI is streamlining the creation and review of legal contracts, proposals, RFPs, and pitch documents. This not only reduces operational costs but also accelerates time-to-market for new products and services.
Portfolio Management: Banks are using AI to optimize investment strategies, monitor market trends, and personalize recommendations for clients.
Process Efficiency: In France, 83% of banks believe AI’s greatest potential lies in making processes more efficient, profitable, and faster, rather than simply doing them better or more accurately. Similar sentiments are echoed in the U.K. and Australia, where banks see AI as a catalyst for operational transformation.

While internal use cases currently dominate, banks are also laying the groundwork for more customer-facing AI applications, such as personalized marketing, tailored savings advice, and omnichannel servicing. For example, 44% of U.S. banks, 40% of U.K. banks, and 43% of Australian banks cite personalized customer journeys as a leading priority, enabled by AI-driven insights.

Internal vs. Customer-Facing Applications

Globally, the initial focus of generative AI investment is on internal transformation. In the U.K., 76% of banks say they will prioritize non-customer-facing generative AI over the next three years to improve internal capabilities. In France, 67% of banks are following a similar path. This approach allows banks to build trust in AI, address regulatory and data privacy concerns, and demonstrate quick wins in efficiency and risk management before expanding to customer-facing innovations.

However, the long-term vision is clear: AI will underpin both operational excellence and differentiated customer experiences. Banks are investing in data and analytics platforms to combine customer data across systems, enabling a 360-degree view that supports hyper-personalization, real-time engagement, and seamless omnichannel journeys.

Regional Trends and Challenges

While the direction of travel is consistent, each region faces unique challenges and priorities:

United States: Banks are focused on AI, customer experience, agility, and cybersecurity. Legacy technology and operational agility are major barriers, but 61% believe they are ahead in customer experience transformation, and 91% in innovation.
United Kingdom: AI is seen as a driver of efficiency and speed. Budget constraints, legacy systems, and skills gaps are key obstacles. 75% of banks see AI’s greatest potential in process efficiency, and 76% are prioritizing internal generative AI.
France: ESG and sustainability are top priorities alongside AI. Data access and unified strategy are significant challenges. 67% of banks are prioritizing non-customer-facing generative AI.
Germany and Australia: Both markets are investing in AI for internal use, with a strong focus on operational agility and cloud migration. Budget and data access remain persistent barriers.
Southeast Asia: Banks are leveraging AI to improve customer experience and community engagement, but face regulatory and legacy technology hurdles. 40% are focused on intelligent technologies, and 42% are combining customer data for richer insights.
Canada: Banks are prioritizing generative AI for internal use (46%) and focusing on customer centricity by combining data across systems (56%). Legacy technology and regulatory challenges are the main barriers.

Moving from Experimentation to Enterprise-Wide Deployment

The shift from experimentation to enterprise-wide AI adoption is marked by several key trends:

Agile Operating Models: Transformation leaders are embracing agility at scale—99% have adopted agile models, enabling rapid innovation and cross-functional collaboration.
Platform-Based, Data-Driven Approaches: Leaders invest in cloud, AI, and analytics to enable real-time insights and innovation. Modern data architectures break down silos and support scalable AI deployment.
Talent and Culture: Investment in upskilling, reskilling, and cultural change is as important as technology. Banks recognize that building a data-driven, customer-led culture is essential for AI success.
Partner Ecosystems: 98% of transformation leaders have broad partner networks, allowing them to scale and innovate rapidly by leveraging fintech and technology partnerships.

Overcoming Barriers to Scale

Despite the progress, banks face persistent challenges in scaling AI:

Legacy Technology: Outdated systems hinder data access and integration, slowing AI adoption.
Skills Gaps: The shortage of digital and data talent is a universal concern, with banks in all regions investing in both existing and new talent development.
Regulatory Complexity: Data privacy, security, and compliance requirements vary by region and can slow the rollout of AI solutions.
Budget Constraints: Especially acute in Europe and Australia, budget limitations can delay or limit the scope of AI initiatives.

The Path Forward: Actionable Insights for Banks

To realize the full potential of AI and generative technologies, banks should:

Benchmark Against Global Peers: Assess progress relative to global leaders and identify gaps in customer experience, operational agility, and technology adoption.
Prioritize Data and AI: Invest in modern data architectures and AI capabilities to enable personalization, efficiency, 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.
Move Beyond Pilots: Focus on scaling successful AI use cases across the enterprise, with clear metrics for business impact.

How Publicis Sapient Can Help

Publicis Sapient partners with leading banks worldwide to accelerate digital transformation and unlock the value of AI. With deep industry expertise and a proven track record, we help banks move from pilot projects to enterprise-wide deployment—navigating local complexities while adopting global best practices. As the industry continues to evolve, those who act boldly and decisively will define the future of banking.

Ready to accelerate your AI journey? Contact Publicis Sapient to learn how we can help you move from experimentation to real business impact with AI and generative technologies.