Survey of more than 150 quants, research analysts, and data scientists examines how AI is being used in investment research workflows
NEW YORK, Jan. 22, 2026 /PRNewswire/ — As firms explore embedding artificial intelligence (AI) in investment research workflows, quantitative teams are also placing greater emphasis on sector-specific data to capture alpha that is concentrated within distinct industry verticals. This is one of the top findings of a new Bloomberg survey of more than 150 quants, research analysts, and data scientists across EMEA and North America, conducted during a series of client workshops to understand key trends and challenges in investment research.
Building on last year’s Research Data survey, which highlighted the top challenges for teams utilizing research data, this year’s findings focus on the use of AI in investment research workflows and the datasets potentially needed to find an investment edge.
The survey highlights that firms are at an AI-adoption inflection point. While traditional machine learning techniques are now widely established in quantitative research workflows, more than half of respondents (54%) report that they have not yet begun their generative AI journey. This gap is likely driven less by a lack of appetite for generative AI and more by data readiness, as contextualized, well-structured data is a necessary prerequisite to power successful models and agentic workflows in today’s age of AI.
As firms look ahead to more AI-enabled deep research to support more specialized, domain-driven strategies, nearly three-quarters of respondents (72%) say they want to onboard sector and industry-specific data, such as company key performance indicators, pharmaceutical pipeline, and semiconductor segment revenue mix.
This push for deeper, sector-specific data aligns with how quantitative teams are currently applying AI in practice. According to the survey, the most common use case for AI is generating insights for stock selection (48%), followed by content summarization (21%) and thematic analysis (13%).
“The survey points to a pivotal shift in investment research: progress with AI is increasingly shaped by data readiness rather than experimentation alone. Firms are prioritizing sector-specific datasets for deeper company and industry context, alongside tick data and measures of investor expectations that capture how markets behave. Together, this reflects a move toward more context-rich inputs for alpha generation” said Angana Jacob, Global Head of Research Data, Bloomberg Enterprise Data. “Bloomberg supports this evolution by providing these deep, differentiated datasets as part of our end-to-end multi-asset Research Data solution, empowering more sophisticated quantitative workflows.”
Bloomberg’s Investment Research Data Solution provides a range of datasets designed to underpin advanced research workflows, including those enabled by AI and machine learning:
- Company Financials, Estimates, Pricing Point in Time: Historical point in time company actuals, consensus estimates, company guidance and pricing data for over 100,000 active and inactive public companies, supporting factor-based strategy construction, backtesting and training models.
- Industry Specific Company KPIs and Estimates: ~1200 unique point-in-time KPI (key performance indicators) across the 11 BICS Level 1 Sectors and 44 BICS Level 3 Industries, enabling deep sector and industry research.
- Operating Segment Fundamentals (BICS): Revenue, Total Balance Sheet Assets, Net Revenue and Operating Income for each reported segment of a company based on the standardized BICS segments for a company’s latest reported fiscal year to support in-depth industry research and cross-company comparability.
- Bloomberg Second Measure (BSM) Transaction Analytics: Subset of a U.S. consumer panel that includes 20+ million consumers, and covers 3,000+ public and private companies and 4000+ brands across industries.
- Geographic Segment Fundamentals (GEO): Regions and country-level actuals data as reported by companies or accurately estimated by a hybrid Trade Flow and GDP Model, enabling more detailed geographic exposure analysis and comparability.
Bloomberg also provides Tick History and Bar data, along with Estimates that reflect investor expectations and positioning — areas also highlighted by respondents as priorities. These datasets are designed to work together and can be connected with other sector-specific data, including alternative data, enabling clients to develop a more complete view of companies and industries. This approach is supported by a growing range of datasets in 2026, such as Company Segments & Deep Estimates, and Thematic Exposures.
Bloomberg’s Investment Research Data solutions are accessible via Data License Plus (DL+). DL+ is the next generation of Bloomberg’s Data License (DL). While DL provides access to billions of data points daily through Bloomberg’s ready-to-use data website, data.bloomberg.com, clients using DL+ unlock a unified data platform that increases operational efficiency and ensures seamless, scalable access to interconnected data across their enterprise. Delivery methods for Bloomberg data include SFTP, REST API, or into cloud environments.
To view the full results of the survey, please click here.
About Bloomberg
Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to markets. The company helps connect influential communities across the global financial ecosystem via reliable technology solutions that enable our customers to make more informed decisions and foster better collaboration. For more information, visit Bloomberg.com/company or request a demo.
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SOURCE Bloomberg
