Build the Banking Operating Model That Gives AI Room to Deliver
By Jessica Kendall, Contributor at The Financial Brand
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Every account opening, loan approval, payment, and service request depends on hundreds of operational decisions customers never see. When those processes work well, experiences feel effortless. When they don’t, delays, duplicate requests, and manual handoffs become painfully visible.
A new whitepaper from Boston Consulting Group (BCG) argues that advances in agentic AI give banks an opportunity to rethink those operational foundations rather than simply automate existing workflows. By redesigning work around customer journeys and layering AI onto simpler processes, institutions can reduce costs, improve responsiveness, and create capacity for higher-value customer interactions.
Key takeaway: BCG points to “zero ops” — a three-pronged approach to modernization built on straight-through processing, real-time prioritization, and agentic AI — to transform operations into a strategic advantage. In fact, the whitepaper estimates that organizations can capture 15% to 25% of addressable operational costs in an initial zero ops transformation cycle while generating benefits that continue to compound over time.
Need to Know:
• Operational excellence is becoming a competitive differentiator that influences growth, customer experience, and profitability.
• Agentic AI expands automation beyond repetitive tasks, allowing institutions to automate work that previously required judgment and document-intensive review.
• BCG projects automated processing will require 75% less human effort by 2030, with organizations increasingly structured around customer journeys rather than product lines.
• Banks should redesign customer journeys and simplify operations before deploying AI to maximize long-term value.
• Organizing work around customer journeys instead of product silos improves speed, flexibility, and resource allocation across the organization.
• Early modernization creates advantages that compound over time as each transformation cycle makes future improvements faster and less expensive.
• Fully implemented Zero Ops programs typically capture 15%–25% of addressable operational costs during the first transformation cycle.
• Straight-through processing can handle 60% to 70% of retail banking volumes, while simple consumer lending can reach automation rates approaching 90%.
Operations Becomes the Growth Engine
Most banking executives think about operations as infrastructure. Customers rarely notice it when everything works, but everyone notices when something breaks. Historically, that has made operational improvement an exercise in cost reduction, efficiency gains, and incremental process optimization.
BCG argues those assumptions deserve another look. According to its latest whitepaper, operational performance increasingly determines how quickly banks can launch products, respond to customers, manage risk, and compete with both digital challengers and larger institutions. Rather than treating operations as a support function, leading organizations are beginning to view them as a strategic capability that directly influences growth.
This shift is happening because AI has fundamentally expanded what can be automated. Earlier automation initiatives typically focused on repetitive, rules-based work. Banks deployed robotic process automation to eliminate individual manual tasks, yet many workflows still required employees to move documents between systems, review exceptions, and coordinate across departments. As a result, automation often improved isolated processes without meaningfully changing how work flowed through the organization.
Agentic AI changes that equation.
Unlike traditional automation tools, agentic AI systems can interpret context, make decisions, coordinate actions across multiple systems, and adapt as situations change. That allows banks to automate more complex activities such as commercial loan documentation, onboarding reviews, underwriting support, fraud analysis, and exception handling — areas that previously depended almost entirely on human judgment.
The result is faster workflows that create better customer experiences and reduce costs. Employees spend less time on repetitive administrative work and more time handling complex customer conversations. Capacity becomes easier to shift across business lines as demand changes throughout the day. Those improvements affect revenue as much as efficiency.
Key insight: For retail banks facing margin pressure and growing competition, the conversation is no longer just about doing the same work with fewer people. It’s about building an operating model that allows the institution to respond faster than competitors.
Why Incremental Modernization Falls Short
Many banks would argue they have already invested heavily in operational modernization. They’ve digitized forms, implemented workflow tools, introduced robotic process automation, and migrated portions of their infrastructure to the cloud.
BCG’s assessment is that those investments often improve individual functions without changing the underlying operating model. Manual handoffs remain. Product silos persist. Customer journeys still cross multiple departments, creating delays, duplicate work, and inconsistent experiences.
Instead of layering new technologies into existing processes, BCG recommends redesigning operations around complete customer journeys.
That sounds like a subtle distinction, but it changes how work is organized. Rather than optimizing mortgage operations separately from deposit operations or commercial banking, institutions examine every step required to complete a customer objective. Teams, workflows, and technology are then organized around delivering that outcome efficiently from beginning to end.
This approach also creates a stronger foundation for AI.
Key takeaway: BCG describes three operational models that increasingly coexist within modern financial institutions:
Routine work should move toward fully automated straight-through processing.
More complicated files should receive targeted human intervention only when necessary.
Highly complex situations continue to rely on multidisciplinary teams with end-to-end ownership.
Instead of treating every request the same way, institutions route work according to its complexity and business value.
That structure allows banks to automate a much larger percentage of work without forcing every customer interaction into identical workflows. It also ensures employees spend their time where judgment, relationships, and expertise create the greatest value.
Building the Foundation Before Deploying AI
BCG also challenges another common assumption: that AI should be the starting point for operational transformation.
Instead, BCG argues banks should first simplify the organization itself. Centralizing operations, reducing unnecessary management layers, and redesigning workflows around customer journeys create the conditions that allow automation to deliver meaningful results. Introducing AI before addressing fragmented processes simply automates inefficiencies.
That sequencing matters because transformation compounds over time.
Key insight: Institutions that simplify processes first can continuously expand automation into increasingly complex work. Those that postpone foundational changes often discover AI inherits every manual exception, disconnected system, and process bottleneck already built into the organization. The technology performs exactly as designed, but the underlying workflows still limit performance.
This means modernization should begin with operational diagnostics rather than technology selection for most financial institutions. Understanding where work slows, where exceptions occur, and where customers experience friction provides a clearer roadmap than simply identifying AI use cases.
BCG recommends beginning with a focused assessment of one or two business lines rather than attempting enterprise-wide transformation immediately. A modular approach allows banks to demonstrate measurable results, build organizational confidence, and fund future initiatives through early savings. Many organizations may be able to identify meaningful improvements within six months while spreading larger transformation efforts over the following 12 to 24 months.
The Leadership Challenge Extends Beyond Technology
Technology may enable zero ops, but leadership determines whether it succeeds.
BCG found successful transformations consistently share several organizational characteristics. Executive sponsorship begins with the CEO and board treating operational modernization as a strategic business priority rather than an IT initiative. Chief operating officers play an equally important role by maintaining momentum after early improvements and ensuring organizations continue making difficult structural decisions.
BCG also emphasizes that workforce transformation deserves as much attention as technology investment. Employees are not simply learning new software — they’re transitioning into new roles that increasingly focus on oversight, judgment, and customer engagement instead of repetitive processing. Performance metrics must evolve as well, shifting emphasis from activity levels to outcomes such as straight-through processing rates, first-time-right accuracy, service-level performance, and exception resolution.
Perhaps the most practical recommendation is to balance buying and building. Commodity capabilities such as document extraction, intake automation, and standard workflow management are increasingly available through mature vendor platforms. Internal development should focus on areas that create competitive differentiation, including proprietary decision models, institution-specific workflows, and integrated customer intelligence that competitors cannot easily replicate.
Key insight: For many retail banks, this guidance may be especially relevant as AI vendors continue flooding the market with specialized solutions. Success will depend less on acquiring the latest technology than on integrating the right capabilities into a modern operating model.
The Next Competitive Battleground
Banking has historically competed on products, pricing, branch networks, and digital experiences. Increasingly, however, competitive advantage may be determined by how effectively institutions operate behind the scenes.
As customer expectations continue to rise, operational performance influences nearly every interaction — from how quickly accounts are opened and loans are funded to how efficiently service requests and fraud investigations are resolved. Customers may never see the operational model itself, but they experience its outcomes every day.
Institutions that redesign work, simplify operations, and organize around customer journeys will be better positioned to capture the full value of AI innovations. Those improvements extend beyond efficiency, creating greater organizational agility, better customer experiences, and additional capacity for innovation.
For retail banking executives, the question is becoming less about whether AI belongs in operations. Rather, it’s more about whether today’s operating model is ready to support the next generation of banking.
Bottom line: Banks that treat operations as a strategic capability rather than a cost center will likely find themselves better equipped to compete, regardless of how quickly technology continues to evolve.
