Your Bank’s Oldest Competitive Edge Just Became Your Biggest Liability
By David Evans, Chief Content Officer at The Financial Brand
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Banking’s historic competitive moat — scale — is facing its first genuine challenger in a century. According to PwC research, speed now rivals size as a differentiator: Thanks to AI, smaller institutions can access the same intelligence and decision-making power previously reserved for the largest banks creates fundamentally different competitive dynamics.
The shift from efficiency-focused AI to what Sean Viergutz of PwC (in a recent episode of the Banking Transformed podcast) called “agentic teammates” — autonomous systems orchestrating tasks across organizations — will determine which institutions defend their positions versus which lose ground.
According to Viergutz, success requires three elements working together: executive leadership treating AI as a strategic imperative, workforce transformation that positions technology as enhancement rather than replacement, and measurement frameworks assessing productivity gains invisible to traditional efficiency metrics.
Need to Know:
- AI democratizes competitive capabilities across institution sizes: Smaller banks now access the same intelligence, personalization tools, and infrastructure modernization capabilities that previously required the massive discretionary budgets of the largest institutions.
- TAgentic AI multiplies workforce productivity without headcount changes: Organizations treating AI as teammate augmentation rather than replacement achieve 2x to 5x productivity gains from existing teams through autonomous task orchestration.
- Infrastructure agility becomes a resilience factor: Banks that modernized to composable, data-organized architectures gain dual advantages — they can rapidly deploy new solutions and harness AI more effectively than those maintaining legacy systems.
Scale Loses Its Monopoly
For more than a century, banking operated on a simple principle: the largest institutions with the most discretionary capital to invest in technology, customer acquisition, and infrastructure maintained insurmountable competitive advantages. That equation has changed.
“Scale has been the moat in banking for forever,” explains Sean Viergutz, Bank and Capital Markets Advisory Leader at PwC. “Those that have had the largest discretionary dollars to invest in technology, to invest in acquiring customers, to invest in all of the different areas that drive a competitive advantage have typically come from the largest banks.”
PwC’s research shows 58% of banking leaders identify generative and agentic AI as the industry’s most transformative force over the next three years, with 55% already considering it their top investment priority. AI democratizes capabilities previously exclusive to institutions with massive technology budgets.
Consider infrastructure modernization. Historically, replacing core systems required offshore centers employing thousands working over years. “Do you need 1,000 to 10,000-to-20,000-person offshore centers when you’re able to harness AI to write code for you, to test that code, to deploy that code?” Viergutz asks. “It doesn’t require the massive investments that we’ve seen to date.”
What it means A $2-billion community bank can now modernize infrastructure at speeds approaching those of institutions ten times its size. Speed, measured in months rather than years, becomes the differentiator.
AI as Teammate, Not Replacement
Most banking executives still conceptualize AI primarily as an efficiency mechanism, automating tasks to reduce headcount. This framework misunderstands the technology’s potential and creates the workforce anxiety that prevents successful adoption.
“There’s the hammer looking for the nail,” Viergutz observes. “Basically, the exec looks at a function, says, ‘Hey, how do we take 30%, 40% of our workforce out of this area?’ And that’s very hard to do without breaking the chassis.”
The alternative? Reframe the equation. “Make it less around, ‘Hey, how do I do more with less,’ and make it ‘How do I do more with the same amount of people because now I have 1,000 workers with the power of 2,000 to 3,000 to 4,000 to 5,000 because I’ve got these agentic teammates there.”
The distinction between generative AI and agentic AI matters. Generative AI creates content from human prompts. Agentic AI orchestrates entire workflows autonomously. A Stanford professor Viergutz cites advises corporations: “Don’t treat AI like a search engine, treat it as a teammate. Ask it, ‘If you were going to do this job, how would you do it differently?'”
What it means Organizations adopting this teammate framework report fundamentally different outcomes. Employees engage rather than resist. Productivity multiplies without organizational trauma. “You’re not competing against AI, you’re treating it as a teammate, a leverage point, something to learn from,” Viergutz explains to podcast host Jim Marous. This distinction separates institutions making measurable progress from those stalled in early adoption.
Three Prerequisites for Success
PwC’s work across financial institutions reveals consistent patterns separating fast movers from laggards. Three factors determine trajectory.
1. Executive leadership must treat AI as strategic imperative. “It’s a leadership imperative whether that’s at the board or that’s at the op-co/ex-co, it’s at their table and it’s being discussed routinely,” Viergutz notes. This means regular executive attention, resource allocation decisions, and strategic integration — not occasional board presentations.
2. Institutions need defined strategies aligned with business objectives. Many organizations start by asking, “Can you tell me where I go and deploy AI to get the biggest bang for the buck?” Viergutz calls this “the hammer looking for the nail.” While identifying high-impact use cases matters, the approach lacks the strategic framework for sustained transformation. “It doesn’t create the groundswell cultural shift that is really necessary to make sure that AI has staying power within an organization.”
3. Talent determines long-term success. Companies behind leading language models engaged in unprecedented talent wars precisely because they recognize that individuals who deeply understand these technologies drive their capabilities. Yet PwC found 90% of bank executives acknowledge winners will be those investing in capabilities they currently lack, while only 25% consider workforce reinvention their top strategic priority.
“That stood out as a stark data point,” Viergutz observes. “One does not equal the other, and there’s a miss there.”
Banks Will Need New Metrics of Success
Traditional banking metrics increasingly obscure rather than illuminate AI’s impact. Efficiency ratios, return on equity, and return on assets fail to capture productivity transformations occurring within organizations.
“Efficiency ratios are measured as X today. And there’s a top quartile, middle of the pack, bottom quartile — that’s going to look very different in a handful of years,” Viergutz predicts. “The efficiencies that used to be top and best class, maybe bottom quartile going forward.” Institutions that successfully implement AI will reset industry performance standards.
Consider an engineering organization maintaining 30,000 employees. Traditional measurement would assess headcount reduction as success. “If that 30,000 people is now generating 3x lines of code that they did before and modernizing at a faster pace, then you are doing it more efficiently and it is working,” Viergutz explains. “You’re just not measuring the productivity output differently.”
What’s required Organizations addressing this develop parallel measurement frameworks. They maintain traditional metrics for external reporting while implementing internal systems tracking output per employee, time-to-market for new capabilities, and speed of infrastructure modernization — operational metrics revealing transformation progress invisible to conventional financial measures.
Infrastructure Modernization Can Turbocharge AI’s Impact
Organizations that invested in composable architectures and modern core systems gain advantages beyond their initial goals. They can harness AI more effectively than institutions maintaining legacy infrastructures.
“Those clients that have already moved or started selecting those packages really stand to benefit the most from this new technology,” Viergutz notes. The connection runs deeper than timing. “All of those solutions, these headless cores, all require a bank to have understood their data sets and organized in a way that harnesses the data before you can implement those, so does AI.”
Key insight Data organization becomes the common foundation. Banks that structured data to implement modern cores simultaneously prepared themselves for AI deployment. Those maintaining legacy systems face compounding challenges: they must modernize infrastructure while simultaneously developing AI capabilities, each depending on the other. This creates a widening gap between early movers and those on traditional paths.
Go Beyond Back Office Efficiency
Current AI implementation concentrates in back-office functions: document processing, compliance, risk assessment, coding. These applications deliver efficiency gains while minimizing customer-facing risk. The next wave moves AI into direct customer interactions.
“It’s going to start in the back office, in the middle office, and non-customer-facing applications,” Viergutz acknowledges. “Just because it’s safe, you don’t want to do any kind of undo customer harm.” But the back-office focus represents a beginning phase. “I do think though it will shift very soon within the next year or two.”
The coming phase will finally deliver genuine personalization. Banking will evolve from offering product variations to enabling the kind of customization already visible in other industries. “Can you open the ecosystem of opportunities and adjacent industries around the customer, that’s really going to be the differentiator,” Viergutz argues. The institution that combines comprehensive data understanding with AI-enabled orchestration across banking and adjacent services creates differentiation impossible to replicate through product features alone.
