AI Reshapes Banking: When Scale Becomes a Liability, Not an Advantage

For over a century, the banking industry’s competitive landscape was dominated by one powerful factor: scale. Larger institutions, with their massive capital and resources, held an insurmountable advantage. Today, this foundational principle is being challenged by artificial intelligence (AI), ushering in a new era where speed and intelligence rival size as key differentiators.

PwC research highlights this monumental shift: AI empowers smaller financial institutions with access to the same advanced intelligence and decision-making capabilities once exclusive to the largest players. This fundamentally alters competitive dynamics, creating a level playing field previously unimaginable.

The transition from AI focused solely on efficiency to what Sean Viergutz, Bank and Capital Markets Advisory Leader at PwC, calls “agentic teammates” — autonomous systems capable of orchestrating complex tasks across organizations — will define future industry leaders. Institutions that embrace this strategic evolution are poised to defend their positions, while others risk losing significant ground.

According to Viergutz, successful AI integration hinges on three critical elements:

• Executive Leadership: AI must be treated as a core strategic imperative at the highest levels.
• Workforce Transformation: Technology should enhance rather than replace human roles, fostering a collaborative “teammate” environment.
• Measurement Frameworks: New metrics are needed to assess productivity gains that traditional efficiency measures often overlook.

The Democratization of Competitive Power

AI’s most profound impact is its ability to democratize capabilities. What once required vast discretionary budgets from mega-banks — advanced intelligence, personalized customer tools, and robust infrastructure modernization — is now accessible to institutions of all sizes.

Sean Viergutz explains, “Scale has been the moat in banking for forever. Those that have had the largest discretionary dollars to invest… have typically come from the largest banks.” This equation is rapidly changing.

PwC’s research indicates that 58% of banking leaders identify generative and agentic AI as the most transformative force for the industry over the next three years, with 55% already prioritizing it as a top investment. This is particularly evident in infrastructure modernization. Historically, replacing core systems demanded thousands of personnel and years of effort.

Viergutz poses the question: “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?” The answer is clear: AI significantly reduces the need for massive, time-consuming investments. Consequently, a $2 billion community bank can now modernize its infrastructure at a pace comparable to institutions ten times its size, making speed, measured in months rather than years, the ultimate differentiator.

AI: Teammate, Not Replacement

Many banking executives initially view AI primarily as a tool for efficiency, aiming to automate tasks and reduce headcount. This narrow perspective often misunderstands AI’s full potential and, critically, creates workforce anxiety that can hinder successful adoption.

Viergutz observes this common pitfall: “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.”

A more effective approach reframes the equation: instead of “doing more with less,” focus on “doing more with the same amount of people.” By leveraging AI as an “agentic teammate,” existing teams can achieve two to five times their previous productivity. Agentic AI, unlike generative AI which creates content from prompts, autonomously orchestrates entire workflows.

As a Stanford professor cited by Viergutz advises, “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?'” Organizations that adopt this “teammate” mindset report fundamentally different outcomes: employees engage with the technology rather than resist it, leading to multiplied productivity without organizational upheaval. This distinction is crucial for institutions making tangible progress in AI adoption.

Three Pillars for Strategic AI Implementation

PwC’s extensive work with financial institutions reveals consistent patterns distinguishing leaders from laggards in AI adoption. Three key factors determine an institution’s trajectory:

1. Executive Leadership as a Strategic Imperative: AI must be a regular agenda item at the board and executive committee levels, driving resource allocation and strategic integration, rather than being relegated to occasional presentations.
2. Defined Strategies Aligned with Business Objectives: Simply asking “where to deploy AI for the biggest bang for the buck” lacks a strategic framework. While identifying high-impact use cases is important, a sustained transformation requires a cultural shift and a clear strategy linked to overarching business goals.
3. Investment in Talent: The companies behind leading language models fiercely compete for talent because they understand that individuals with deep AI expertise are the true drivers of capability. However, PwC found that while 90% of bank executives acknowledge the need to invest in new capabilities, only 25% consider workforce reinvention a top strategic priority. This “stark data point” highlights a critical disconnect.

New Metrics for a New Era

Traditional banking metrics, such as efficiency ratios, return on equity, and return on assets, increasingly obscure AI’s transformative impact. Sean Viergutz predicts, “The efficiencies that used to be top and best class, maybe bottom quartile going forward.” Successfully implemented AI will fundamentally reset industry performance benchmarks.

Consider an engineering organization with 30,000 employees. Conventional measurement would deem headcount reduction as success. However, if those 30,000 employees, empowered by AI, generate three times the lines of code and accelerate modernization, the organization is indeed operating more efficiently, even without headcount changes. The challenge lies in accurately measuring this enhanced productivity.

To address this, organizations must develop parallel measurement frameworks. While traditional metrics remain essential for external reporting, internal systems should track operational metrics such as output per employee, time-to-market for new capabilities, and speed of infrastructure modernization. These internal benchmarks provide invaluable insights into true transformation progress.

Infrastructure Modernization Turbocharges AI

Banks that have already invested in composable architectures and modern core systems gain a significant advantage in harnessing AI. These institutions are better positioned to deploy AI effectively compared to those maintaining legacy infrastructures.

Viergutz notes, “Those clients that have already moved or started selecting those packages really stand to benefit the most from this new technology.” The connection is profound: modern core systems and AI both demand well-understood and organized data sets. Banks that structured their data for modern core implementation simultaneously laid the groundwork for effective AI deployment.

Institutions relying on legacy systems face a compounding challenge: they must modernize their infrastructure concurrently with developing AI capabilities, creating a widening gap between early movers and those on traditional paths.

Beyond Back-Office: The Future of Personalization

Current AI implementations largely focus on back-office functions like document processing, compliance, risk assessment, and coding. These applications deliver efficiency gains while minimizing customer-facing risks. However, this is just the beginning.

While Viergutz acknowledges that AI will start “in the back office, in the middle office, and non-customer-facing applications” for safety, he predicts a rapid shift within the next year or two toward direct customer interactions. This coming phase will finally deliver genuine personalization in banking.

Banking will evolve beyond offering mere product variations to enabling the kind of deep customization seen 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 can combine comprehensive data understanding with AI-enabled orchestration across banking and related services will create a level of differentiation that product features alone cannot replicate.

Source: thefinancialbrand.com