How AI Is Impacting Productivity at JPM, BAC, C & Others

Artificial intelligence (AI) is the biggest technological disruption since the Internet, reshaping how we work, create and make decisions. 

The biggest U.S. banks, JPMorgan JPM, Citigroup C, Bank of America BAC and Wells Fargo WFC, along with their several large regional counterparts like PNC Financial Services PNC, are spending billions of dollars on AI to enhance productivity and meet clients’ ever-changing financial requirements.

AI Moves From Pilot Projects to Core Banking Workflows

AI is transitioning from an innovation lab to a day-to-day operating system at U.S. banks, with executives increasingly framing it as a near-term productivity lever and, eventually, a headcount lever. 

JPMorgan, Citigroup, Bank of America, Wells Fargo and PNC Financial have described AI as a force multiplier that can compress cycle times in operations, accelerate software development and enhance client servicing. Thus, higher output per employee can support expense discipline over time, even if the savings show up gradually and unevenly across business lines. 

JPMorgan’s Measurable Productivity Lift and Big-Budget Execution: CFO Marianne Lake said AI has doubled the bank’s productivity impact from roughly 3% to 6% and highlighted especially significant gains for operations specialists, potentially 40% to 50% increases as tasks become more automated and AI-assisted. 

JPMorgan’s broader tech commitment remains substantial (approximately $18 billion annual technology budget). CEO Jamie Dimon has pointed to a $2 billion AI investment delivering similar magnitude savings, evidence of a focus on measurable ROI, not just experimentation. 

Citigroup Is Scaling Internal GenAI Tools to Boost Developer Output: The company is leaning on AI to modernize workflows at scale, with management emphasizing developer and knowledge-worker productivity. CEO Jane Fraser has said Citigroup’s internal AI tools are freeing up about 100,000 developer hours per week, and that close to 180,000 employees across 83 countries have access to the bank’s internal AI capabilities. 

Citigroup has also referenced an annual technology budget of around $12 billion, suggesting the bank has both the funding and organizational push to embed AI across functions. The near-term message is fewer hours spent on rote coding, document analysis and repetitive controls work, more capacity for higher-value engineering and client solutions. 

Bank of America’s AI Spend to Scale Service and Tech: The company has been among the most explicit on spending and adoption. Management has noted investing $4 billion of its roughly $13 billion technology budget in AI and related new tech initiatives, and has tied this to tangible productivity outcomes in both frontline and tech teams.

Bank of America has described bankers handling materially larger client coverage as AI automates briefing and prep work and cited major efficiency gains in software testing when developers use AI tooling. The company’s long-running virtual assistant, Erica, also illustrates how AI can absorb high-volume service interactions, freeing humans for complex requests, an operating model that can raise service levels without hiring at the same pace. 

Wells Fargo & PNC Financial’s Efficiency, Automation and the Headcount Question: Wells Fargo and PNC Financial are communicating a similar direction with a different tone. Wells Fargo CEO Charlie Scharf has stated that AI is enabling the bank to do more with the same staff while also signaling that the headcount is expected to decline next year as part of efficiency efforts, with AI likely to add momentum over time. 

PNC Financial CEO Bill Demchak has emphasized that AI will accelerate automation already underway, arguing the bank can potentially keep the headcount stable, even while scaling the business significantly over a decade, an investor-friendly framing that links AI to operating leverage.

Banks’ Efficiency Gains From AI Usage

The key question is how quickly AI-driven productivity converts into sustainable expense leverage. The early evidence points to real throughput gains in operations, software development and client support, but the path to better efficiency ratios is likely to be incremental as banks keep investing in data, controls and model governance.

The risk is that benefits may be back-end loaded. Near-term AI spend can pressure expense lines, job impacts may create restructuring costs, and model-risk governance can slow deployment. 

Over time, the winners will be those that industrialize AI across the franchise, embedding it into everyday decisions and workflows while staying on the right side of regulators, because that combination can translate into faster execution, better service and structurally lower unit costs.

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This article originally published on Zacks Investment Research (zacks.com).

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