S&P Global is extending its AI-enabled data delivery capabilities with the addition of Visible Alpha consensus estimates and credit ratings to its AI Data Portal, alongside expanded conversational AI functionality within Capital IQ Pro. The announcements reflect a broader strategy to make its financial datasets accessible through external AI applications and agents while embedding AI more deeply into its own research and analytical platforms.
The latest development, announced on 15 October, brings Visible Alpha’s granular sell-side estimates, financial line items and segment data into the AI Data Portal, following the addition of RatingsXpress credit data in late September. Both datasets are accessible through the portal’s Model Context Protocol (MCP) integration, allowing customers to retrieve S&P Global information through compatible AI applications, including ChatGPT, Claude, Gemini and Copilot.Meanwhile, an October update to Capital IQ Pro introduces enhanced ChatIQ functionality, extending natural-language interaction across financial research, screening, modelling and portfolio analysis.
Together, the developments illustrate how S&P Global is pursuing two complementary approaches to AI adoption: providing the data infrastructure for clients building their own AI workflows while expanding the capabilities of its established applications.
Expanding the AI Data Portal
The addition of Visible Alpha introduces a substantial new category of information to the AI Data Portal. Drawing on the working models of more than 250 contributing sell-side brokers, Visible Alpha provides standardised estimates covering more than 8,000 companies across over 170 industries.
Beyond headline consensus figures, the available content includes driver-level estimates, contributor counts, estimate dispersion, historical financials and business-segment forecasts. These allow analysts to examine the assumptions underlying market expectations and identify differences between individual contributors.
For equity research teams, potential applications include earnings preparation, company valuation, peer comparisons and portfolio monitoring. Access to underlying forecast drivers could also support more detailed analysis of how changing assumptions about revenues, margins or business segments affect consensus expectations.
“Investors and analysts want to see more than the headline number. They need the assumptions and the level of analyst agreement behind it,” says Peter Licursi, Head of AI and Growth, Kensho Data & Platforms at S&P Global Market Intelligence.
“With Visible Alpha’s rich sell-side data now available through the S&P Global AI Data Portal, customers can move from a headline consensus figure to the drivers underneath it in a single query, with answers grounded in verifiable S&P Global data. Because it sits alongside our company intelligence and credit ratings data, customers can assess the market’s expectations, fundamentals and credit ratings information in the same workflow.”
The Visible Alpha launch follows the September addition of RatingsXpress, which provides nearly one million issuer- and security-level credit ratings, together with outlooks, CreditWatch status and ratings actions from S&P Global Ratings.
This extends the portal’s coverage into credit risk assessment, ratings surveillance and counterparty monitoring, potentially allowing institutional users to combine different categories of financial intelligence within AI-supported research processes.
Deterministic and Adaptive Retrieval
Central to S&P Global’s approach is the distinction between two methods of accessing its data.
Deterministic Retrieval, currently available for both Visible Alpha and RatingsXpress, uses structured, API-driven queries to retrieve specific information. It is designed for workflows where the required data is already known and predictable retrieval is important.Adaptive Retrieval, introduced earlier this year, takes a different approach. It allows AI agents and large language models to interpret more open-ended questions and identify relevant information across multiple S&P Global datasets.
Support for Visible Alpha through Adaptive Retrieval is expected later this year, while S&P Global indicated in September that RatingsXpress support would follow in the coming weeks.
As previously reported by Market & Alt Data Insight, Adaptive Retrieval forms part of S&P Global’s efforts to make its data accessible within agentic workflows, where AI systems can independently identify and assemble the information required to complete a task.
Maintaining deterministic access alongside adaptive retrieval is particularly relevant for institutional users, where accuracy, provenance and reproducibility remain important considerations. A structured request for a particular credit rating or financial metric presents different requirements from an exploratory query asking an AI system to identify potential risks across a portfolio.
The availability of both approaches gives firms flexibility in designing AI workflows, although the extent to which agents can reliably combine information across datasets will become clearer as the additional Adaptive Retrieval integrations become operational.
Bringing AI Deeper into Capital IQ Pro
While the AI Data Portal focuses on making S&P Global’s information available to external applications, the latest Capital IQ Pro release extends AI capabilities within the company’s established research environment.
The enhanced ChatIQ interface allows users to interrogate company, market, credit and economic information alongside financial documents, news and research through natural-language questions. It also brings screening and charting capabilities into the conversational interface.
New functionality includes ChatIQ Spreadsheets, an AI-powered Excel assistant designed to support financial model creation, review and analysis using refreshable Capital IQ Pro formulas.
ChatIQ Presentations, currently available in Labs, extends the approach into PowerPoint, allowing users to generate data-driven presentations using S&P Global information.
The release also introduces AI-powered Portfolio Insights, currently in beta, which is designed to identify drivers of portfolio performance and relate returns to market developments, corporate events, earnings and news.
These capabilities extend conversational AI beyond information retrieval into the analytical and reporting activities that follow it, potentially reducing the need for users to move between separate research tools.
The wider Capital IQ Pro update also includes new discounted cash flow analysis, customisable enterprise valuation calculations, point-in-time trading comparables and expanded fixed-income analytics. In private markets, a new benchmarking tool incorporating With Intelligence data allows users to construct tailored comparisons of fund performance.
Building on a Broader AI Reorganisation
The recent announcements follow a series of organisational and technology changes at S&P Global Market Intelligence.
In July, the company restructured the division, bringing its data delivery and platform capabilities into a more integrated structure and giving Kensho a more prominent role in developing AI-enabled products and services.
The reorganisation established Kensho Data & Platforms as one of two principal business verticals, alongside Enterprise Solutions, with the former bringing together established products including Capital IQ, RatingsDirect, Visible Alpha and With Intelligence.
Earlier in the year, Warren Breakstone, Head of Data & Research at S&P Global Market Intelligence, outlined the company’s ambitions to make Capital IQ Pro accessible to third-party AI agents through MCP servers.
“A key component of our strategy is making the platform agent-friendly by unlocking it through MCP servers, so clients can access the same data and capabilities through various GenAI front-ends,” Breakstone told Market & Alt Data Insight in March. “Those MCP servers enable us to build agentic solutions ourselves and also enable our clients to build their own agentic solutions on top of Capital IQ Pro.”
The strategy also involves consolidating acquired datasets within S&P Global’s broader information infrastructure. The integration of With Intelligence private markets content into Capital IQ Pro, followed by the expansion of Visible Alpha through the AI Data Portal, illustrates how the company is seeking to make its different information assets available across multiple applications and delivery channels.
The Changing Role of Financial Data Platforms
S&P Global’s approach reflects a developing shift in the relationship between financial data providers and their institutional customers.
Traditionally, vendors have distributed information through proprietary applications, data feeds and APIs, with users or internal systems responsible for finding, combining and interpreting the information.
AI-enabled retrieval introduces another route, allowing applications and agents to interact directly with vendor datasets through natural-language interfaces and standardised protocols such as MCP.
For data providers, this creates opportunities to make proprietary content available across a wider range of customer workflows. It also raises questions about licensing, entitlement management, provenance and the commercial relationship between the underlying data and the applications through which it is consumed.
S&P Global’s investment in both the AI Data Portal and ChatIQ suggests that it sees continuing demand for its own analytical environment alongside growing demand for direct, machine-readable access to its information.
The next stage will depend partly on how effectively these capabilities work together. As Adaptive Retrieval expands to incorporate Visible Alpha and credit ratings, customers should gain greater scope to combine consensus expectations, company fundamentals and credit intelligence within individual AI workflows.
For institutional data teams, the significance lies in the potential to make a broader range of licensed financial information accessible to AI applications without requiring each dataset to be integrated separately into every research environment.
The remaining challenge is to demonstrate that these increasingly complex workflows can preserve the accuracy, transparency and control that institutional users require, while delivering measurable improvements in research efficiency and analytical capability.
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