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S&P Global adds ChatIQ AI tools to Capital IQ Pro

S&P Global adds ChatIQ AI tools to Capital IQ Pro

S&P Global introduced AI features to Capital IQ Pro, including ChatIQ research tools, AI-driven modeling capabilities and presentation workflows, plus launched Vault Risk Assessment for digital assets risk analysis.

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Context & Analysis

S&P Global runs data, benchmark, and analytics services across capital markets and other sectors, and expanding AI into Capital IQ Pro and digital asset risk tools fits directly into that role as an information and workflow provider for financial institutions worldwide.

The Capital IQ Pro upgrades deepen the existing narrative rather than rewrite it. The move leans into AI-driven productivity and enterprise data themes already highlighted by analysts, especially a focus on higher-margin workflow tools and rising API and LLM usage. ChatIQ across research, spreadsheets, and presentations, plus Vault Risk Assessment in digital assets, sit in the bucket of new products that feed the Vitality Index and the push for broader fee-based revenue across Market Intelligence and Ratings-related data.

The real proof point to watch now is how far these AI tools penetrate client workflows. That will be visible in future disclosures on Vitality Index contribution, Capital IQ Pro user engagement, and AI usage metrics such as MCP connectors or API and LLM call volumes, which will show whether this release materially supports the revenue and margin assumptions in the current narrative.

FAQ
What new AI features did S&P Global add to Capital IQ Pro?
S&P Global introduced ChatIQ research tools and AI-driven modeling capabilities and presentation workflows for financial professionals using Capital IQ Pro.
What is Vault Risk Assessment?
Vault Risk Assessment is a new S&P Global tool that provides risk analysis for clients exposed to the digital assets sector.
What should investors watch to see if these AI tools matter?
Investors can track future disclosures on Vitality Index contribution, Capital IQ Pro user engagement, and AI usage metrics such as MCP connectors or API and LLM call volumes.
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