Organizations Have Overhauled Data Systems for AI Integration
Global entities are modifying their database architectures to allow artificial intelligence systems to better access statistics.
Updated on Oct. 6, 2026 in Artificial Intelligence
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Bloomberg has launched a new data provision method enabling AI to search for stocks using everyday language. This shift is part of a broader trend as global institutions restructure databases to ensure AI can accurately retrieve and interpret statistical information.
Why it matters
Traditional data storage required human intervention to verify identifiers and prices, often leading to potential misinterpretations by automated systems. By embedding definitions and criteria directly into data architectures, organizations aim to improve the reliability and accessibility of their internal insights for AI tools.
Siemens currently utilizes AI to process 2,800 customer inquiries each week, while Snowflake's AI service successfully handled over 330,000 requests by the end of 2025.
The players
Bloomberg
This global financial services and software company provides market data and analytics to institutions worldwide.
United Nations
This international organization facilitates cooperation on global issues, including the management of large-scale statistical datasets.
Siemens
Based in Germany, this multinational conglomerate focuses on industrial automation, energy, and infrastructure technology.
Snowflake
This United States-based cloud computing company provides specialized data warehousing and AI-integrated analytics services.
The details
Organizations are modifying data architectures to include statistical definitions and survey criteria alongside numerical values. This technical integration allows AI systems to bypass traditional manual verification, facilitating direct retrieval of data such as employment rates or financial stock identifiers.
Timeline
By the end of 2025, Snowflake's AI service processed 330,000 inquiries.
The UN launched the UN System Data Commons platform on September 17, 2026.
Bloomberg announced its new data provision method on September 29, 2026.
The UN aims to link 80% of its statistical data by 2027.
The Tech Race
The push to optimize data for AI mirrors the broader industry shift away from manual database management toward machine-readable architectures. This evolution positions firms like Bloomberg and Snowflake to compete more effectively by automating the retrieval of complex, institutional-grade datasets.
Users will experience faster and more accurate results when using AI tools to search for complex statistics or financial information. These improvements reduce the need for manual verification and technical knowledge, making institutional data more approachable for professional and general tasks.
The takeaway
The transition to machine-ready data is becoming a requirement for any organization hoping to leverage AI effectively. Companies should prioritize embedding metadata and definitions directly into their storage systems to reduce errors and improve automated analytical performance.
Further reading
Learn more about the latest innovations in Artificial Intelligence.
Source note: This article includes information reported by 조선일보.
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