For more than a decade, the largest banks on Wall Street have poured resources into technology, steadily transforming trading floors, back offices, and client-facing operations. But the current wave of artificial intelligence adoption represents something fundamentally different — a force so powerful that it is prompting the industry’s most senior executives to publicly reckon with the possibility that tens of thousands of jobs could be reshaped or eliminated within the next few years. The question is no longer whether AI will change banking, but how fast, how deep, and who will be left standing when the dust settles.
According to a detailed report from Business Insider, the nation’s biggest banks — JPMorgan Chase, Citigroup, Goldman Sachs, Bank of America, and Wells Fargo — are each charting aggressive AI strategies, and their leaders are increasingly candid about the workforce implications. The article surveyed the public statements, earnings calls, and internal memos of these five banking giants to paint a picture of an industry at an inflection point.
Jamie Dimon Sounds the Alarm — and the Opportunity
JPMorgan Chase, the largest U.S. bank by assets, has arguably been the most vocal about AI’s transformative potential. CEO Jamie Dimon has repeatedly described artificial intelligence as a technology on par with the printing press, the steam engine, and the internet. In his 2024 annual letter to shareholders, Dimon wrote that AI could eventually touch “every single process” at the bank and that JPMorgan already had more than 2,000 AI and machine learning use cases in production across the firm, from fraud detection to trading strategies to customer service automation.
The bank has committed enormous resources: JPMorgan’s annual technology budget exceeds $17 billion, a substantial and growing portion of which is directed toward AI initiatives. The firm has hired thousands of AI specialists and data scientists, even as it acknowledges that AI could reduce headcount in certain business lines. Dimon himself has suggested that AI might allow the bank to move to a three-and-a-half-day workweek in the future — a statement that, while aspirational, signals just how much productivity gain leadership expects from these tools. As Business Insider noted, the bank’s internal deployment of a proprietary large language model, dubbed LLM Suite, is already being used by tens of thousands of employees for tasks ranging from drafting emails to summarizing research.
Goldman Sachs and Citigroup: Efficiency as a Strategic Imperative
Goldman Sachs, long regarded as Wall Street’s most technologically ambitious firm, has been integrating AI into its core operations with characteristic intensity. CEO David Solomon has spoken publicly about the potential for generative AI to automate significant portions of the bank’s coding, compliance, and research functions. Goldman has estimated that as much as 25% to 30% of its code could eventually be written by AI, a figure that carries profound implications for its engineering workforce. The bank has been piloting AI tools for investment banking pitch books, equity research summaries, and risk management — areas that have traditionally required armies of junior analysts and associates.
Citigroup, meanwhile, is pursuing AI as a central pillar of CEO Jane Fraser’s sweeping corporate overhaul. The bank announced in early 2024 that it would cut 20,000 jobs over two years as part of a broader restructuring, and Fraser has been explicit that technology — particularly AI — will play a major role in enabling the bank to do more with fewer people. Citi has been rolling out AI-powered tools for regulatory compliance, anti-money-laundering surveillance, and client onboarding, all of which are labor-intensive processes ripe for automation. As Business Insider reported, Citi’s leadership views AI not merely as a cost-cutting tool but as a way to fundamentally re-architect how the bank operates globally.
Bank of America and Wells Fargo: The Quiet Builders
Bank of America has taken a somewhat different approach, emphasizing its proprietary virtual assistant, Erica, which has handled more than 2 billion client interactions since its launch. CEO Brian Moynihan has described AI as a “game changer” for the bank’s consumer and wealth management businesses, and the firm has filed hundreds of AI-related patents. While Bank of America has been less publicly aggressive about headcount reductions tied to AI, internal planning documents and executive commentary suggest the bank expects meaningful efficiency gains in operations, compliance, and customer service over the next two to three years.
Wells Fargo, still navigating the aftermath of its fake-accounts scandal and a Federal Reserve-imposed asset cap, sees AI as a path to operational rehabilitation. CEO Charlie Scharf has emphasized the bank’s investment in AI-driven risk management and fraud detection, areas where the bank has faced intense regulatory scrutiny. Wells Fargo has been investing heavily in cloud infrastructure and AI talent, and its technology budget has grown substantially. The bank’s leadership has acknowledged that AI will change the composition of its workforce, though it has been careful to frame the transition as one of reskilling rather than outright displacement.
The Numbers Behind the Transformation
The scale of potential workforce disruption is staggering. A widely cited 2023 report from Goldman Sachs economists estimated that generative AI could expose the equivalent of 300 million full-time jobs globally to automation, with the financial services sector among the most affected industries. Within banking specifically, roles in operations, compliance, customer service, middle-office functions, and even portions of investment banking and asset management are considered highly susceptible to AI-driven automation.
Yet the picture is not one of pure displacement. Every major bank has emphasized that AI will also create new roles — in data science, AI engineering, prompt engineering, and AI governance. JPMorgan, for example, has been on a hiring spree for AI talent even as it automates other functions. The net effect on headcount remains uncertain and will likely vary significantly across business lines and geographies. What is clear, however, is that the composition of the banking workforce is shifting rapidly, with a premium on technical skills and adaptability.
Regulatory and Ethical Crosscurrents
The rush to deploy AI in banking is not without friction. Regulators in the United States and Europe have signaled increasing scrutiny of AI use in financial services, particularly around issues of algorithmic bias, model explainability, and data privacy. The Office of the Comptroller of the Currency, the Federal Reserve, and the Consumer Financial Protection Bureau have all issued guidance or initiated inquiries into how banks are using AI in lending decisions, customer interactions, and risk management. Banks that move too fast — or too opaquely — risk regulatory backlash that could slow adoption and erode public trust.
There are also thorny ethical questions about the pace of workforce transition. Labor advocates and some lawmakers have called on banks to invest more heavily in retraining and transition support for displaced workers, arguing that the industry’s record profits obligate it to manage the human costs of automation responsibly. The banks themselves have responded with a mix of reskilling programs, internal mobility initiatives, and public commitments to responsible AI deployment — though critics argue these measures are insufficient relative to the scale of the coming disruption.
A Competitive Arms Race With No Clear Finish Line
What is perhaps most striking about the current moment is the intensity of the competitive dynamic. No major bank can afford to fall behind on AI adoption, lest it cede ground to rivals — or, increasingly, to fintech challengers and big technology firms that are encroaching on traditional banking territory. The result is a self-reinforcing arms race in which each institution’s AI investments compel its competitors to accelerate their own, ratcheting up the pace of change across the entire sector.
The implications extend well beyond Wall Street. As the largest banks automate more of their operations, regional and community banks face pressure to follow suit or risk being outcompeted on cost, speed, and customer experience. Technology vendors, consulting firms, and AI startups are all jockeying for position as the banking industry’s AI spending surges. And for the hundreds of thousands of people who work in financial services, the message from the C-suite is unmistakable: the future belongs to those who can work alongside machines, not those whose work machines can do alone.
The next two to three years will be decisive. By 2026, the full impact of current AI investments will begin to materialize in earnings reports, headcount disclosures, and competitive positioning. The banks that execute well — deploying AI at scale while managing regulatory, ethical, and workforce risks — will likely emerge stronger and more profitable. Those that stumble may find themselves on the wrong side of the most consequential technological shift in the history of modern finance.
