Anticipatory Data Governance in the Age of AI: Emerging Signals in Data Access, Reuse, and Sovereignty

Abstract

This paper reports findings from a structured participatory foresight study comprising two expert forecasting studios convened by The GovLab between 2025 and 2026. The studios brought together nineteen senior practitioners spanning official statistics, digital and trade policy, open science, AI governance, geospatial systems, and public-sector innovation across multiple jurisdictions. Applying a qualitative signal-scanning methodology grounded in the horizon-scanning and anticipatory-governance traditions, we elicited, clustered, and thematically synthesized emerging developments in data access, governance,and reuse, and stress-tested them against practitioner experience. We identify seven convergent signals: (1) the open-data paradigm is under strain; (2) data ecosystems are becoming machine-centric and AI-mediated; (3) inference is reshaping the foundations of data governance;(4) data infrastructure is becoming harder to sustain; (5) governance is fragmenting across institutions and jurisdictions; (6) sovereignty and security are driving a turn toward strategic control; and (7) data-sharing models require stronger incentives and benefit-sharing mechanisms. We further map the reinforcing feedback loops that couple these signals, showing how interventions in one domain propagate risks and opportunities across the wider ecosystem. We argue that data governance is becoming inseparable from AI governance, digital public infrastructure, economic strategy, democratic resilience, and geopolitical competition, and we outline an agenda for anticipatory data governance capable of adapting before dependencies, risks, and missed opportunities become locked in. The contribution is diagnostic rather than predictive: the signals offer an evidence-informed framework for reasoning about structural shifts already underway, not a forecast of specific technological outcomes.