Across the banking sector, a pattern is emerging. Boards are reshuffling leadership, appointing Chief AI Officers and competing aggressively for scarce AI and data talent.

These moves are often framed as evidence that banks are finally getting serious about artificial intelligence. But most of this activity is focused inward. Banks are using AI to optimise existing operating models (driving productivity, reducing costs and improving control) rather than preparing for how banking itself is about to change.

While banks need to drive short-term productivity improvements to compete, the market will ultimately reward those that achieve differentiation through opportunity creation, not just efficiency.

The real AI shift will happen when customer expectations change. Technologies only become transformative once they transform customer behaviour, not when they become technically viable. Smartphones, for example, existed well before the app economy, and for a time they were little more than better phones. The real shift occurred only when customer expectations evolved — reshaping how people interacted with services, not just devices.

Financial services face a similar transformation. AI agents will soon become the connective layer of banking. Software will act on behalf of customers and increasingly manage financial decisions: understanding a person’s goals, preferences and constraints, and searching for the best products to meet such needs.

At the same time, banks will deploy their own AI systems to support institutional priorities such as risk, pricing, policy and regulatory compliance. Increasingly, value will be created through the interaction between these two systems: one representing customer intent, the other representing the bank’s risk, pricing, policy and compliance.

In that environment, financial services become less about customers navigating bank interfaces and more about whether a bank’s automated decision processes select the right products on the customer’s behalf. The opportunity for banks becomes clear; competitive advantage will be found in the ability to meet the customer where they’re going.

AI strategy needs to consider AI as a catalyst for a shift in how customers discover, engage and make decisions. Banks don’t win markets by cutting costs alone: growth requires a differentiated proposition, not just a more efficient operating model.

The shift to machine-to-machine banking

As agentic systems improve, customers will increasingly delegate financial decisions to personal AI agents that monitor markets, reassess risk and negotiate on their behalf.

These agents won’t use apps or interfaces designed for humans. Instead, they will connect securely to banking systems, query products in real time, compare pricing continuously, and move capital automatically when conditions change. Customers will engage with banks far more frequently; those who previously assessed their credit position only periodically could start monitoring it continuously. Banks can then adjust pricing more rapidly in response to real-time market shifts, while ensuring that AI agents operate within regulatory frameworks rather than bypassing them.

Document-heavy processes will fade. Processes built around static documents will become less relevant when agents can exchange verified data directly.

Banks that cannot interact directly with customer agents or offer real-time personalised information will struggle to compete, regardless of how polished their customer-facing apps appear. Put simply, banks need to consider how AI impacts their customers before they think about what it can do for their internal systems.

What retail banking now needs to rebuild

To remain relevant in this environment, retail banking strategy needs to refocus on three critical capabilities that cannot be bolted on at the edges:

● Dynamic risk verification, shifting away from retrospective models built on periodic snapshots of data toward live, predictive models built on real-time data streams, allowing customers, or their agents, to access products in the moment.

● Personalised products, likely in the form of a real-time modular assembly of pre-approved components. Customers will expect access to hyper-personalised financial products (adjusting terms, offsets, and features) tailored to their needs.

● Responsive pricing, the middle ground while “stock-ticker” mortgage pricing remains a bridge too far for current regulators. If a customer’s liquidity or LVR improves, they will instantly want to access a more favourable rate.

Banks also know from decades of operating physical branch networks that customer adoption progresses at different rates across different segments. To ensure equitable access to financial products, banks must balance legacy expectations with emerging, AI-driven customer journeys. The challenge is to transform rapidly without disrupting the legacy experiences many customers still rely on.

This is the existential pivot facing banking leaders today. The question is not only what AI can do inside the bank — it’s also how banks use AI to be ready to meet customers when behaviour shifts, as decision-making moves to customer agents that evaluate, compare and switch continuously.

This responsibility cannot be delegated to a lab or addressed through isolated pilots. Progress depends on banks, regulators and policymakers coming along for the ride, not reacting after the fact.

The organisations that lead in this future will be those equipped with AI strategies centred on customer value, ready for the moment customer preference shifts. Those singularly focused on AI’s internal use will realise too late that the ship has already sailed without them aboard.

Abigail Holman, Partner and AI & data banking sector lead, Deloitte Australia.

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