The banking industry has made remarkable strides in digital transformation. Automated workflows and zero-touch processes have streamlined operations and reduced friction for customers. But in this push for efficiency, something essential has been lost: the personal touch that once defined the customer-bank relationship during the branch era.
With efficiency largely achieved, can banks now introduce the human element into digital banking experiences to improve the customer experience?
It may seem counterintuitive, but AI/ML could be the key—particularly generative AI and advanced language models. These technologies have the potential to make sense of unstructured customer data and enable transformative, personalized interactions at scale.
Introducing the human touch to digital banking—with AI
Modern AI tools, including generative AI and large language models, can help create personalized banking experiences in several ways, including:
- Understanding the voice of the customer. AI can process unstructured data, such as emails, voice messages, and images, to gain a deeper understanding of customer needs and concerns.
- Semantic search. Unlike traditional keyword-based searches, semantic search interprets the intent behind a customer’s query, enabling banks to respond more accurately and intuitively.
- Personalized interactions at scale. AI empowers banks to deliver highly tailored solutions, much like a trusted branch manager might have done in the past. This builds customer loyalty and enhances profitability.
For customers, this means a banking experience that feels truly supportive, intuitive, and human—even in a digital environment.
The risk of inefficient AI deployment
While the benefits of AI are clear, poor implementation can erode any gains. AI systems that are not optimized for scale often consume massive amounts of compute resources, leading to inefficiencies and escalating costs.
For banks, this inefficiency poses serious threats:
- Rising compute costs. In a landscape of slowing revenue growth, high operational costs can undermine your ability to compete.
- Missed opportunities. Inefficient AI systems limit a bank’s ability to innovate and deliver the experiences customers expect, as resources are committed.
To avoid these pitfalls, banks must take a measured approach to AI deployment, focusing on optimization and integration into existing workflows.
Best practices for scaling AI effectively
Banks should follow some guidelines for leveraging AI:
- Prioritize data readiness. Ensure that structured and unstructured data is well organized and accessible. Generative AI and language models rely on clean, comprehensive data to deliver accurate insights.
- Leverage semantic search. Invest in AI tools that prioritize semantic understanding. This will improve customer interactions and reduce the need for human intervention in routine queries.
- Optimize compute usage. Work with AI platforms designed to minimize compute costs while maintaining high performance. This ensures scalability without jeopardizing profitability.
- Focus on personalization. Embed AI into workflows with the aim of reintroducing personalized, humanlike interactions into digital banking.
- Start small, scale strategically. Begin with targeted use cases, measure success, and expand AI applications incrementally.
A game-changing opportunity
AI isn’t just a tool for efficiency—it can be the game-changer to redefine the customer-bank relationship.
McKinsey outlines how banks achieving enterprise-wide AI integration—moving beyond isolated experiments—are realizing substantial gains in customer satisfaction, loyalty, and profitability through improved decision-making, personalized interactions, and operational efficiencies.
By leveraging the transformative potential of generative AI and language models, banks can deliver the best of both worlds: efficiency, through streamlined operations and lower costs, and effectiveness, with personalized, meaningful customer experiences.
Building the future with Signal-oriented banking
The path forward lies in embracing signal-oriented banking, where actionable AI insights are seamlessly integrated into workflows to enhance both operational efficiency and customer experience. When done right, this approach allows banks to:
- Bring back the personal touch customers crave
- Optimize costs in a way that ensures long-term competitiveness
Banks that master this balance will not only survive the challenges ahead but thrive.
