We’re in a world of increasing financial choice, where the human relationship is the single most valuable competitive asset for any bank. Yet, procedural friction still consumes the time of even the sector’s most skilled relationship managers.
The highest-value talent is being used for some of the lowest-value activities.
Artificial intelligence has the power to solve this, but it’s facing a bit of a PR problem. It’s become synonymous with automation: systems designed to think for humans, not with them. Its very name implies something synthetic, something that replaces rather than reinforces our own intelligence.
The Friction Eating Relationship Banking from the Inside
Relationship managers (RMs) tell us they spend the vast majority of their week on data preparation, chasing documents, navigating complex internal workflows, and preparing information for various committee reviews. Data from BCG backs this – in commercial banking, relationship managers can spend up to 60% of their working hours on administrative work.
That’s where AI comes in.
Trust is the ultimate currency in banking. Many assume AI will inevitably create distance between banks and their clients. The underlying fear isn’t about the technology itself, but about decisions that customers cannot see, question, or influence. It’s the fear of the black box.
AI as Relationship Enabler, Not Relationship Replacement
Far from creating distance, the intentional adoption of AI offers the greatest opportunity for banks to reclaim and recenter the human relationship. Integrating with AI allows RMs to eliminate procedural clutter that currently prevents meaningful connection.
This administrative friction that’s making relationship banking more difficult didn’t start with AI; it began slowly eroding since the late 90s when the everyday work of an RM shifted away from clients and toward screens and internal systems.
RMs want to be visiting their clients, understanding the nuance of their business and operations, and in turn advising them on the best financial services to achieve their goals. This is how RMs build trust and drive commercial outcomes both for their clients and for the bank. Yet, the majority of RM time is spent on procedural friction.
When a highly skilled professional spends their time on clerical or procedural tasks, they’re left with little ability to offer thoughtful, timely advice or genuinely build rapport. Beyond the frustration this creates, it’s also a significant commercial drain.
The Two Foundations: Data Lineage and Explainability
To get to a place where AI is improving bankers’ relationships with their clients, both parties must trust that AI will improve operations. That’s dependent on two foundations: data lineage and explainability.
Data lineage. If a system offers a recommendation, we need to know the source of the data, how it was prepared and which model processed it. This creates responsibility. It allows bankers to check the basis of a decision rather than taking it on faith.
Explainability. If you cannot explain a result in clear language, it will not hold up in front of a customer. Being able to walk through the “why” of a decision is just as important as the decision itself.
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In practice, that could mean that if a banker is suggesting their client optimises payables to increase working capital, they need to be able to show the client exactly where that change would come from and how they came up with that logic. This will not only help the RM gain credibility but makes it actionable for both the banker and the client.
When Trust is Present, the Conversation Changes
When these two ideas are present, attitudes toward AI shift. The conversation becomes less about replacing people and more about empowering them. Relationship managers become more proactive instead of reactive because they have the tools and time to better understand their customer. Customers feel respected because they can feel that the advice from their RM is meticulously researched and tailored to them.
Bringing Banking Back to Relationships
Relationships have always been core to banking, and the integration of AI does not change that – if anything, it makes the statement even truer. Especially in today’s world where customers have more choice and less patience. Across regions with very different financial systems, one thing is always consistent: people want someone they trust to help them navigate important decisions.
The aim is not to create technology that thinks for bankers, but rather to remove the distraction that keeps bankers from thinking about customers. I believe AI can bring us back to a more human approach to banking. It can clear space to ask better questions, give better advice, and build confidence over time.
There is an old idea that technology pushes people apart. In banking, the opposite can be true if we use AI with intention. By giving people more time, more clarity and more context, we can bring relationships back to the center of banking where they belong.
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