Are We Ready for AI? What the Global Data Barometer Reveals About Data Foundations for Artificial Intelligence
Artificial intelligence is reshaping the data landscape, yet most national data systems remain ill-equipped to meet its demands. In the second edition of the Global Data Barometer (GDB), Data Foundations for AI emerges as a cross-cutting theme, offering a timely lens to evaluate whether foundational elements of data governance are aligned with the realities—and risks—of AI deployment.
Unlike thematic clusters that focus on specific domains, cross-cutting themes draw from sub-questions, indicators, and supporting elements across the entire survey, reassembling these components to generate new, policy-relevant insights. The AI cross-cutting theme examines how well national data environments are preparing for, regulating, and leveraging artificial intelligence through the lens of training, governance, data protection, and data infrastructure.
The findings suggest that many countries are taking first steps, but most are not yet ready.
One area of visible activity is AI training and literacy. More than a third of assessed training programs now include AI-related topics, and these are often delivered in partnership with universities and expert organizations—underscoring the importance of academic and technical institutions in preparing the next generation of professionals. However, the Barometer reveals a worrying gap in training on ethics and responsible AI use: only a quarter of programs address ethical considerations, despite growing concerns over algorithmic bias, discrimination, and transparency.
From a policy perspective, government support for data reuse often overlooks AI entirely. Most national initiatives that promote data availability or reuse do not include explicit guidance on AI applications, revealing a disconnect between open data agendas and the emerging demands of AI governance. This omission risks leaving data ecosystems vulnerable to unintended consequences, as AI systems trained on poorly governed data can reinforce biases or erode public trust.
Legal frameworks also lag behind. The review of data protection legislation (DPL) shows that algorithmic decision-making is not yet a mainstream concern in most national laws. While regions vary, African countries appear to be ahead of Latin America in integrating provisions related to algorithmic processing into their DPL frameworks—an encouraging sign of proactive regulatory adaptation to emerging risks.
Finally, despite growing interest in AI for public-sector innovation, there is little documented evidence of actual AI deployment within government data systems. This may suggest that adoption is still in its early stages, or simply that usage is not being transparently reported—either scenario points to a lack of accountability and measurement around government use of advanced analytics and decision-support tools.
Taken together, these insights underscore a critical message: AI is not just a technical issue—it is a governance challenge. If countries are to harness the benefits of AI while avoiding its pitfalls, they must strengthen the data foundations upon which AI is built. That includes more inclusive and ethical training programs, updated legal protections, integrated policy frameworks, and transparent public-sector adoption.
The Global Data Barometer’s AI cross-cutting theme is a call to action for governments, civil society, and the private sector to move beyond pilot projects and policy rhetoric. A healthy data ecosystem is one that anticipates, regulates, and guides the use of AI in ways that serve the public good.