The December 5, 2025 Data Standards Forum brought together standards bodies, regulators, data scientists, and technologists to discuss the future of structured data. This briefing synthesizes the discussions from the event, providing a concise overview of the critical insights shared. The analysis is organized into three areas: the evolution of the XBRL technical specification toward greater flexibility and AI-readiness; the symbiotic and increasingly critical relationship between artificial intelligence and data standards; and the strategic future of standards in global regulatory reporting.
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Executive Summary
An Advanced Data Model is Imminent: The Open Information Model (OIM) is being developed to move beyond the limitations of XBRL 2.1, prioritizing semantic meaning over syntax. This strategic shift is designed to enhance flexibility and, critically, make data natively consumable by artificial intelligence.
AI Validates, Not Replaces, Standards: Empirical research and expert consensus overwhelmingly conclude that AI's effectiveness is directly tied to the quality of input data. This makes standards like XBRL more critical for reducing errors and hallucinations, providing the essential guardrails probabilistic models require.
Current Specification Complexity Makes Mandates the Primary Driver of Adoption: Global case studies from the Netherlands, Australia, and examples implementing standards like the Legal Entity Identifier (LEI) prove that despite clear long-term benefits, widespread regulatory adoption of data standards is almost exclusively achieved through explicit mandates, not voluntary action.
The Future is Hybrid and Collaborative: AI will evolve into a component within larger, deterministic systems. The standards ecosystem, in turn, will depend on tighter collaboration between semantic experts (FASB, GASB), technical bodies (XBRL US), and regulators (SEC, FERC, FDIC, state and municipal governments) to drive progress, establish frameworks and implement initiatives like the Financial Data Transparency Act (FDTA).
