The Role of Generative AI in Banking: From Internal Efficiencies to Customer Experience Transformation

The banking industry stands at a pivotal moment. Generative AI (Gen AI) is no longer a distant promise—it is rapidly becoming the engine driving both operational efficiency and customer experience transformation. As banks worldwide accelerate their digital journeys, the adoption of Gen AI is reshaping how institutions operate internally and how they engage with customers. This evolution is not without its challenges, but the opportunities for those who act decisively are profound.

Internal Efficiencies: The First Wave of Gen AI Adoption

For most banks, the initial focus of Gen AI has been on internal, non-customer-facing use cases. This is a pragmatic approach, especially in an environment where budgets are tight and regulatory scrutiny is high. According to recent industry research, a significant majority of banks—ranging from 50% to 66% in major markets—are prioritizing Gen AI for transactional and operational tasks. These include:

• Credit analysis and risk measurement: Automating the review of credit applications, analyzing risk profiles, and supporting underwriting decisions with AI-driven insights.
• Portfolio management: Enhancing investment strategies and monitoring with AI-generated reports and recommendations.
• Document automation: Streamlining the creation, review, and management of legal contracts, proposals, RFPs, and pitch documents.
• Process optimization: Reducing manual effort in compliance, reporting, and back-office operations, leading to cost savings and improved accuracy.

This internal focus is not just about efficiency. It is about building the foundational capabilities—data, cloud infrastructure, and talent—that will enable banks to scale Gen AI across the enterprise.

Barriers to Scaling Gen AI: Regulation, Data, and Talent

While the potential of Gen AI is clear, banks face several barriers to scaling its adoption:

• Regulatory complexity: Compliance remains the top concern. Banks must ensure that AI models are explainable, auditable, and aligned with evolving regulations. The need for robust guardrails and threat modeling is paramount.
• Legacy technology and data silos: Many banks struggle with outdated systems and fragmented data, which limit the effectiveness of AI initiatives. Access to high-quality, unified data is essential for training and deploying Gen AI models at scale.
• Talent and culture: The shift to AI-driven operations requires new skills and mindsets. Upskilling existing employees and attracting digital talent are as critical as technology investments. Banks that prioritize talent development and foster a culture of agility are better positioned to succeed.

The Shift Toward Customer-Facing Applications

As banks gain confidence and capability with Gen AI internally, attention is turning to customer-facing applications. The next wave of transformation will see Gen AI powering:

• Personalized, omnichannel experiences: Banks are leveraging AI to create tailored marketing, personalized savings tips, and seamless journeys across digital and physical channels. In the U.S., 44% of banks cite personalized customer journeys as a top priority, with similar trends in the U.K., Australia, and Germany.
• New products and services: Gen AI enables rapid innovation, from digital financial advice to proactive fraud detection and beyond. Banks are moving from a product-centric to a customer-centric model, using AI to anticipate needs and deliver value in real time.
• Community engagement and financial literacy: AI-driven tools and educational content are helping banks engage underserved segments, such as children or customers with limited digital skills, strengthening relationships and trust.

Building the Right Foundations: Data, Cloud, and Talent

Unlocking the full potential of Gen AI requires a holistic approach:

• Modern data architectures: Banks must invest in breaking down data silos and building platforms that enable real-time insights. Unified, high-quality data is the fuel for effective AI.
• Cloud migration: Moving to cloud-based, coreless architectures provides the scalability and flexibility needed to deploy Gen AI at scale. Cloud also accelerates innovation and reduces time to market for new solutions.
• Talent transformation: Developing digital skills, fostering cross-functional collaboration, and embedding a culture of experimentation are essential. Leading banks recognize that technology and talent investments go hand in hand.

The Path Forward: From Experimentation to Enterprise-Wide Impact

The journey with Gen AI is just beginning. The most successful banks are those that move beyond isolated pilots to enterprise-wide adoption, integrating AI into their business models, workflows, and customer strategies. Four strategic moves set transformation leaders apart:

1. Know the competitive landscape: Invest in digital innovation to keep pace with fintechs and tech giants.
2. Transform people and culture: Prioritize talent development and organizational agility alongside technology.
3. Build partner ecosystems: Collaborate with fintechs, technology providers, and other partners to scale and innovate rapidly.
4. Embrace AI and intelligent technologies: Use Gen AI to drive efficiency, personalization, and new value propositions.

Conclusion: The Future of Banking is AI-Powered and Human-Centered

Gen AI is not just a technology upgrade—it is a catalyst for reimagining banking. By starting with internal efficiencies and building the right foundations, banks can unlock new levels of agility, innovation, and customer engagement. The winners will be those who act boldly, invest in both technology and talent, and put the customer at the center of every transformation. As the industry evolves, Gen AI will define the next era of banking—one that is digital by default, but always human at heart.