Signal GovLab foresight study identifies seven convergent signals reshaping data governance in the AI era

Summary

The GovLab convened two expert forecasting studios between 2025 and 2026, bringing together nineteen senior practitioners spanning official statistics, digital and trade policy, open science, AI governance, geospatial systems and public-sector innovation across multiple jurisdictions. Using a qualitative signal-scanning methodology grounded in horizon-scanning and anticipatory-governance traditions, the researchers elicited, clustered and thematically synthesized emerging developments in data access, governance and reuse, then stress-tested them against practitioner experience. They identified seven convergent signals: the open-data paradigm is under strain; data ecosystems are becoming machine-centric and AI-mediated; inference is reshaping the foundations of data governance; data infrastructure is becoming harder to sustain; governance is fragmenting across institutions and jurisdictions; sovereignty and security concerns are driving a turn toward strategic control; and data-sharing models require stronger incentives and benefit-sharing mechanisms. The paper argues data governance is becoming inseparable from AI governance, digital public infrastructure, economic strategy, democratic resilience and geopolitical competition, framing its contribution as diagnostic — an evidence-informed framework for reasoning about structural shifts already underway, rather than a specific technological forecast.