Introduction to AI on Wall Street
By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service.
How Wall Street Banks Say AI is Delivering Results Today
The same discussion also surfaced a harder reality. If banks can produce more with the same teams, some roles may no longer be required at current levels once demand stabilises.
JPMorgan: Operational Gains Begin to Compound
Marianne Lake, chief executive of consumer and community banking at JPMorgan, said productivity in areas using AI has risen to around 6%, up from roughly 3% before deployment. She added that operations roles could eventually see productivity gains of 40% to 50% as AI becomes part of routine work.
Wells Fargo: Output Rising Ahead of Staffing Changes
Wells Fargo CEO Charlie Scharf said the bank has not reduced headcount because of AI so far, but noted that it is “getting a lot more done.” He said management expects to find areas where fewer people are needed as productivity improves.
PNC: AI Speeds Up a Long-Running Shift
PNC CEO Bill Demchak positioned AI as an accelerator rather than a new direction. He said the bank’s headcount has stayed largely flat for about a decade, even as the business expanded. That stability, he said, came from automation and branch optimisation, with AI likely to push the trend further.
Citigroup: Gains in Software and Customer Support
Citi’s incoming CFO Gonzalo Luchetti said the bank has recorded a 9% productivity improvement in software development. That mirrors a broader pattern across large firms adopting AI copilots to support coding work.
Goldman Sachs: Workflow Changes Paired with Hiring Restraint
According to Reuters, Goldman Sachs’ internal “OneGS 3.0” programme has focused on using AI to improve sales processes and client onboarding. It has also targeted process-heavy functions such as lending workflows, regulatory reporting, and vendor management.
Where Wall Street Banks See the Earliest AI Productivity Gains
Across banks, the clearest gains are showing up in work that relies heavily on documents, follows repeatable steps, and operates within defined rules. Generative AI can shorten the time needed to search for information, summarise material, draft content, and move work through approval chains—especially when paired with structured processes and human checks.
Common areas seeing early impact include:
- Operations: drafting responses, summarising cases, and resolving exceptions more quickly
- Software development: generating code, writing tests, refactoring, and producing documentation
- Customer service: stronger self-service combined with real-time support for agents
- Sales support and onboarding: pulling data from documents, filling forms, and speeding up client setup
- Regulatory reporting: assembling narratives and evidence faster, under strict review and controls
Why Governance Shapes the Pace of Adoption
For banks, enthusiasm is not the main constraint. Control is. US regulators have long required strong oversight of models, and those expectations extend to AI systems. Guidance such as the Federal Reserve and OCC’s SR 11-7 sets standards for model development, validation, and ongoing review.
Productivity Rises, but Employment Questions Remain
The comments from bank leaders point to a phased shift. The first phase looks like stable headcount paired with higher output as AI tools spread across teams. The second phase begins once those gains become consistent enough to influence staffing plans, through attrition, role changes, or targeted cuts.
What AI Means for Wall Street Bank Strategy Beyond 2025
Banks that gain the most from AI are likely to focus on three areas at once: redesigning workflows rather than layering on chat tools, building strong data foundations, and putting governance in place that supports speed without eroding trust.
Research firms argue the financial stakes are high. McKinsey estimates that generative AI could deliver between $200 billion and $340 billion in annual value for the banking sector, largely through productivity improvements.
Conclusion
The open question is no longer whether AI can deliver results in banking. It is how quickly banks can make those gains routine while preserving audit trails, security, and customer safeguards—and how they manage the workforce changes that follow.
FAQs
- Q: What is the current state of AI adoption on Wall Street?
A: AI adoption on Wall Street has moved past experiments and into everyday operations, with bank executives describing AI as an operational upgrade lifting productivity across engineering, operations, and customer service. - Q: Which areas are seeing the earliest AI productivity gains?
A: Areas seeing early impact include operations, software development, customer service, sales support and onboarding, and regulatory reporting. - Q: How are banks managing the workforce changes brought about by AI?
A: Banks are approaching the second phase of AI adoption, where gains become consistent enough to influence staffing plans, through attrition, role changes, or targeted cuts. - Q: What is the estimated annual value of generative AI for the banking sector?
A: McKinsey estimates that generative AI could deliver between $200 billion and $340 billion in annual value for the banking sector, largely through productivity improvements.
