AI Productivity Gains in Banking

By December 2025, the integration of AI into Wall Street operations had progressed beyond mere experimentation within major US banks, becoming a vital part of daily functions. At a Goldman Sachs financial-services conference held on December 9, bank executives highlighted how AI, especially generative AI, serves as a significant operational enhancement that enhances productivity in various sectors, including engineering, operations, and customer service.

However, this advancement brings forth a challenging reality; improved productivity may result in a reduced workforce in the long term as banks optimize their operations and stabilize demand.

How Wall Street banks report AI productivity results

The productivity gains attributed to AI implementation are varied across banks, reflecting strategic choices rather than random experimentation. For example, JPMorgan’s CEO, Marianne Lake, noted a marked increase in productivity in AI-utilizing areas, rising from approximately 3% to around 6%. She projected that operational roles could ultimately experience productivity improvements between 40% and 50% as AI technology integrates more deeply into day-to-day tasks. This improvement stems from JPMorgan’s focus on secure access to large language models and intentional, workflow-driven changes.

Wells Fargo’s CEO, Charlie Scharf, observed that while headcount has not yet diminished due to AI, the bank has become significantly more efficient. He acknowledged that the internal budgeting forecasts suggest a smaller workforce may be necessary by 2026, indicating that structural workforce reductions are likely as productivity levels rise.

Similarly, PNC’s CEO, Bill Demchak, described AI’s role as an accelerator for existing automation trends rather than a revolutionary shift. With a stable headcount over a decade despite expansion, he predicted further momentum for efficiency driven by AI advances.

In terms of customer service, Citigroup’s incoming CFO Gonzalo Luchetti reported a solid 9% productivity surge in software development motivated by AI integration. He also highlighted enhancements in customer service through improved self-service options and real-time support for agents.

According to sources, Goldman Sachs has aligned its “OneGS 3.0” initiative with AI to enhance various operational functions such as sales and onboarding, which accompany staffing changes and efforts to streamline hiring processes.

Identifying early AI productivity gains on Wall Street

Generative AI is proving most beneficial in roles requiring extensive documentation, structured workflows, and defined regulations. It significantly reduces time spent searching for information, summarizing content, drafting responses, and streamlining approval processes. Areas receiving early benefits from AI include:

• Operations: rapid response drafting, case summarization, and quicker resolution of exceptions.
• Software Development: innovative coding, testing automation, refactoring, and documentation.
• Customer Service: reinforced self-service capabilities and enhanced support for agents.
• Sales and Onboarding: efficient data extraction and quicker client setup processes.
• Regulatory Reporting: faster narrative assembling and evidence compilation under strict supervision.

The influence of governance on AI adoption pace

While enthusiasm for AI among banks is palpable, regulatory control represents a potential constraint. US regulatory bodies mandate robust oversight for AI systems, tying their recommendations to established model risk management principles that encompass AI. Regulations such as the Federal Reserve and OCC’s SR 11-7 dictate model validation and ongoing performance monitoring, urging banks to adopt designs amenable to examination and traceability.

This regulatory environment influences how independently AI systems operate, enforcing strict oversight with responsibilities retained by humans for critical decisions, particularly in lending and dispute resolution.

Productivity improvements pose employment challenges

Bank leaders indicate a phased progression is in motion, commencing with increased outputs alongside a stable workforce as AI tools become entrenched. The subsequent phase will likely prompt staffing reevaluations through attrition, role restructuring, or targeted reductions as the productivity benefits of AI materialize.

Signals from Wells Fargo’s headcount projections and related severance expenses suggest that some banks are nearing this latter stage. Moreover, global insights from the International Monetary Fund highlight that AI may significantly influence job landscapes worldwide, with varying impacts across roles and regions. The World Economic Forum’s “Future of Jobs Report 2025” reflects similar sentiments concerning job transitions due to AI adoption.

Future implications of AI on Wall Street banking strategy

Looking ahead, banks that fully harness AI’s potential will likely prioritize comprehensive strategies encompassing workflow redesign, robust data foundations, and the establishment of governance protocols that maintain trust while promoting agility. Financial analyses, such as those from McKinsey, estimate that generative AI could provide banking enterprises with annual value between $200 billion and $340 billion, primarily driven by efficiency gains.

Ultimately, the pressing question is not whether AI can yield positive outcomes for banking institutions, but rather how swiftly these advantages can be normalized without compromising audit integrity, security measures, and consumer protection—while simultaneously managing the necessary workforce adjustments that accompany these transformations.

(Photo by Lo Lo) See also: BNP Paribas introduces AI tool for investment banking. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events; click here for more information.