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Banks seek to rein in soaring AI agent costs, McKinsey says - Finance news and analysis from Global Banking & Finance Review
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Banks seek to rein in soaring AI agent costs, McKinsey says

Published by Global Banking & Finance Review

Posted on November 10, 2026

2 min read

· Last updated: November 10, 2026

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McKinsey: Banks Move to Curb Surging AI Agent Costs Amid Growing Adoption

Global Banking Faces Rising AI Agent Costs and Strategic Shifts

By Tatiana Bautzer

AI Agent Spending and Cost Management

NEW YORK, Nov 10 (Reuters) - Banks worldwide are trying to curb the soaring cost of AI agents as spending on the tokens that power them could easily reach 20% of total labor costs, according to McKinsey's Global Banking Annual Review.

Emergence of AI FinOps Teams

Responsibility for controlling AI-related spending has shifted to new teams that some banks call AI FinOps. Initially, banks sought to rein in costs with systems that directed employees to different AI models based on the complexity of their queries.

Challenges with AI Agent Communication

"But those models are ineffective when AI agents are working, because they communicate with each other and tokens quickly add up," said Vik Sohoni, a senior partner at McKinsey and one of the report's authors, in an interview with Reuters.

Strategic Deployment and ROI Focus

As a result, banks are becoming more selective about where they deploy agentic AI, focusing on areas with the strongest expected returns. Productivity-related savings are relatively easy to measure, but banks are increasingly looking at AI-driven revenue growth to determine where higher spending is justified, Sohoni said.

AI Adoption Trends Among Customers

Customers are adopting AI much faster than previous technologies, according to the report. McKinsey expects AI agents to become widely used within three years, compared with the 5 to 10 years it took mobile internet to gain broad adoption.

Survey Insights on AI Usage in Retail Banking

A McKinsey survey of 46,000 retail banking customers worldwide found that most already use AI to compare banking services, find higher-yield savings accounts and refinance or consolidate debt. Among AI users, 72% said they would allow a banking agent to carry out one financial task, although they would cap autonomous approvals at $500. Meta's Muse agent was downloaded 5 million times in the US during its first 22 days after launch.

Potential Impacts on Bank Revenue and Profit

While AI agents could help banks boost revenue and cut costs, they could also pressure profits by shifting deposits from non-interest-bearing accounts into higher-yielding accounts. So far, that impact has been limited because most banks block external AI agents from authorizing transactions within their systems. 

(Reporting by Tatiana Bautzer. Editing by Mark Potter)

Key Takeaways

  • Token costs account for only 20–25% of variable run costs for AI agents in banking, with human oversight accounting for 70–75%, emphasizing the need to optimize end-to-end workflows rather than just tokens. (mckinsey.com)
  • Single-agent workflows can cost $20K–$30K, and multi-agent teams $100K–$200K per run, underscoring the urgency for cost control via routing models, limiting quality-control frequency, and refining data flows. (mckinsey.com)
  • Banks are forming AI FinOps teams to govern AI spending, focusing deployments on high-return use cases, measuring both productivity savings and AI-driven revenue, and learning from McKinsey’s benchmarks to sustain ROI. (mckinsey.com)

References

Frequently Asked Questions

Why are banks concerned about rising AI agent costs?
Banks are concerned because spending on AI tokens could reach 20% of total labor costs, prompting the need for cost controls.
How are banks managing AI-related spending?
Banks are forming dedicated AI FinOps teams and deploying agentic AI selectively, focusing on areas with the highest expected returns.
How quickly are banking customers adopting AI?
AI adoption among banking customers is rapid, with McKinsey expecting AI agents to become widely used within three years.
What impact could AI agents have on bank profits?
AI agents might pressure profits by facilitating shifts from non-interest to high-yield accounts, although this impact is currently limited.
What are some common uses of AI among banking customers?
Most customers use AI for comparing banking services, finding higher-yield savings accounts, and refinancing or consolidating debt.

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