Bank of America and S&P Global Detailed AI Strategies
Financial leaders outlined plans to double AI investment and reorganize data divisions to scale model accuracy.
Updated on Oct. 2, 2026 in Artificial Intelligence
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Bank of America and S&P Global executives discussed AI implementation strategies at the Fortune AIQ Summit in New York. Bank of America announced plans to double its AI expense budget next year as it continues to expand its 140 current AI use cases.
Why it matters
Financial institutions are increasingly standardizing AI risk reviews and orchestrating multiple models to deliver measurable economic benefits. These strategies aim to balance innovation with auditability as firms shift from experimental projects to core operational workflows.
Bank of America currently maintains 140 AI use cases and processes projects through a proprietary review covering 16 pillars of risk. An orchestration layer routes tasks between open-weight and proprietary models, prioritizing deterministic models for specific financial applications.
The players
Bank of America
A multinational financial services institution leveraging a legacy of over a decade of AI usage to manage 140 internal use cases.
S&P Global
A 160-year-old financial data provider that recently reorganized its Market Intelligence division to better integrate AI platforms.
The details
Bank of America employs a risk-management framework to evaluate every AI initiative before deployment. To handle varied tasks, the bank uses an orchestration layer—a software system that coordinates workflows between different open-weight and proprietary models—to ensure the most efficient model is selected. S&P Global focuses on integrating its data with AI tools to improve accuracy, providing auditability and specific citations for its 60,000 clients.
Timeline
2018: S&P Global acquired Kensho.
March 2026: The Erica virtual assistant reached 3.2 billion client interactions.
July 6, 2026: S&P Global split its Market Intelligence unit into two separate divisions.
September 2026: Brian Moynihan reported internal AI cost and benefit figures.
Next year: Bank of America plans to double its AI expense budget.
The Tech Race
The transition to enterprise-wide AI reflects a broader industry shift from experimental chatbots like the Erica virtual assistant to multi-model orchestration. Financial firms are now competing to move AI projects from research labs into high-stakes, audited production environments.
Customers can expect continued growth in automated banking features as Bank of America increases its investment in internal AI infrastructure. These changes focus on enterprise efficiency and data accuracy, with specific consumer-facing rollouts dependent on future internal project approvals.
The takeaway
Financial firms are moving past basic AI adoption toward rigorous risk-management frameworks and tiered model orchestration. Investors and industry observers should watch for the actualized budget figures in the bank's next annual fiscal report to confirm the scale of this projected investment increase.
Further reading
For more on the development of intelligent financial tools, visit Artificial Intelligence.
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