Competitive advantage and performance in banking have long hinged on how effectively institutions use technology and data. AI represents the next step change, reshaping how banks develop products, manage risk, and serve customers. Those banks that configure their organizations to absorb and exploit each wave of advances in AI will compound their advantage. Recent discussions with more than 30 senior executives of technology-savvy global banks converged on a shared view: Building an AI-infused engine for innovation, simplification, and modernization, not simply deploying AI tools, will create sustained advantage.

Moving beyond efficiency

The goal is a radically different model of banking, characterized by highly personalized customer engagement, hyper-responsive product management, and structurally transformed operations. The performance profile could be dramatically different from today’s banking model, aspiring to 10 times greater productivity, 100 times more experimentation throughput, 90% shorter time to market, and a 10-percentage-point improvement in the cost-to-income ratio.

The strategic prize does not center on efficiency. Rather, it involves velocity in time to market and experimentation. Faster trumps slimmer.

Most institutions can envision these performance goals but remain constrained by legacy estates, skills shortages, inadequate data foundations, capital allocation pressures, talent, and operating model. And institutions that treat AI as a set of tools layered onto existing operations will underinvest in the changes that create enduring advantage.

Banks that currently capture the most value from AI treat it as a lever for growth. An AI-native modern bank requires investment and enduring commitment across five elements:

1. Agentic experiences and products
2. Real-time, autonomous workflows
3. Trust and security as a differentiator
4. Modern technology and data stack
5. Talent, workforce, and operating model

1. Agentic experiences and products

The AI-native bank does not just improve existing customer journeys; it reimagines them from the customer's intended outcome back to what the experience, product, and underlying process should look like when built from scratch. Hyper-personalized experiences will increasingly be orchestrated by intelligent agents rather than delivered through standardized products and predefined channels.

Agentic customer acquisition and servicing models should be proactive, aware of context, and responsive to a customer’s major life events. They will resolve issues quickly at first contact, rather than routing through multistep human workflows.

To that end, several banks have formally abandoned process-mapping exercises, reframing the design question from "How does this work today?" to "What should it look like if we built it now?"

Early examples illustrate the shift. Bradesco Bank placed a big bet on AI with embedded AI for payment initiation, allowing customers to initiate instant payments in Brazil through WhatsApp and agentic AI. Chime reimagined customer service to be AI-first, leveraging voicebots and chatbots to serve 70% of support interactions, with a roughly 75% chatbot resolution rate and 66% voicebot resolution rate for calls that choose self-service. NPS Prism^® research finds Chime ranked first in NPS^® among consumer banking peers in the first quarter of 2026, winning particular praise for speed, delivering the desired outcomes with the least amount of effort, and smartly moving customers from AI to human support when warranted.

These banks demonstrate that reinvented agentic processes done right offer better customer experience with better controls than the processes they replaced.

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2. Real-time, autonomous workflows

Autonomous workflows are emerging across functional domains ranging from marketing and lending to product operations and beyond. NatWest illustrates the power of reimagining workflows. Frustrated by the complexity of going from good idea to reaping value, the bank decided to rip up the customer engagement experimentation process. Its new AI-enabled process reduced idea-to-value campaigns from a 60-plus-day process involving 40 full-time equivalents and 10 handoffs to a 1-day process requiring just 4 to 5 FTEs and 0 handoffs. For any bank, a reasonable goal here is 80% to 90% autonomous process execution.

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How NatWest Is Scaling Customer Engagement with AI

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3. Trust and security as a differentiator

Trust and security in an AI-native bank stem from architectural choices made at the outset. Built well, they enhance a bank’s competitive position rather than serving as compliance requirements retrofitted afterward. Risk and compliance should run continuously as self-executing and predictive features that catch issues in real time. Attaining this state requires treating observability, auditability, and resilience as first-order design characteristics from the start.

Two related organizational changes are emerging. Leading banks have more tightly embedded risk, legal, and compliance within delivery teams rather than maintaining them as an end-of-pipeline gate. One bank has embedded audit checks directly into AI agents—an architectural feature of the workflow that ensures compliance. On agent governance, another bank automatically shuts down agents after a predefined time frame to force security discipline.

4. Modern technology and data stack

There is no viable path to being AI-native without a flexible, modern technology and data stack. Agentic AI cannot orchestrate effectively against fragmented legacy architecture, and the strategy of wrapping legacy systems with digital veneers has run its course. Most banks face decisions about what belongs in the core, what moves outside it, and whether to push vendors to adapt for AI-native demands or keep the core narrow and build orchestration independently. The architecture required above the core is covered in Bain's brief “Why Agentic AI Demands a New Architecture.”

One of the most important architectural decisions for AI-native banks is determining where deterministic systems end and probabilistic systems begin. The deterministic layer covers systems in which regulators require auditability, customers cannot tolerate variable outcomes, and the bank must reconstruct exactly what happened. This includes areas such as ledger integrity, payments execution, entitlements, and regulatory controls. Agentic experiences, fraud intelligence, and workflow orchestration sit in the probabilistic layer. Tools and application programming interfaces should behave deterministically; the orchestration layer deciding which to invoke can be probabilistic. Where this holds, the boundary is defensible, but where it blurs, regulatory exposure can accumulate.

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5. Talent, workforce, and operating model

AI significantly weakens the traditional link between headcount and output. The main scarce resource shifts from capacity to judgment. Teams will be smaller and flatter, with product and engineering roles converging. History suggests that efficiency gains through technology often increase demand rather than reducing it, creating new requirements for governance, orchestration, and oversight. There are structural implications for how spans and layers evolve, how accountability is redesigned, and how new roles emerge (see the Bain Brief “An Operating Model for the Age of AI”).

Building the AI-native modern bank

Defining a vision for the AI-native modern bank is the easy part. What’s trickier—and therefore distinguishes leaders from laggards—is ensuring high-quality execution, realizing value, and building the engine for “always on” innovation, modernization, and simplification. While the five elements discussed above define the destination, successful execution depends on sharpening capabilities in three areas:

• High-functioning talent, culture, and operating model. These three components are in the early days of a generational shift with AI. Their importance will only grow in the AI-native era. Building the talent base, culture, and operating model will be what sustains advantage over the long term.
• World-class data assets. Many banks spend too much time and energy choosing models, copilots, and the surrounding software, when the more important focus should really be clean, useful data. The AI-native modern bank will be “machine readable,” in which data is standardized, relationships are mapped, policies are encoded, and processes are observable. This will require real-time enterprise data fabric, data lineage, high-quality metadata and knowledge graphs, machine-readable process data, and a policy layer.
• Modern, flexible technology platforms. Scaling up agentic AI will require new capabilities for agent orchestration, artificial intelligence for IT operations (AIOps), memory, observability, and evaluation. The core deterministic engines that power banking products, ledgers, transactions, and decisioning will need to be modern, flexible, and interoperable with agent-executed experiences and processes.

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Across every major technology transition in banking, success has depended on sustained senior leadership commitment and sponsorship, and execution speed. AI appears no different. Banks building AI-native capabilities today are already beginning to open a lead over those still in planning stages. Waiting does not preserve optionality; it cedes it.