Capgemini report warns of major data maturity gap as public sector ramps up AI ambitions
A new global report by the Capgemini Research Institute has revealed a significant disconnect between the public sector’s ambition to deploy agentic AI and its actual readiness to do so. While 90% of public sector organizations say they plan to explore, pilot, or implement agentic AI in the next two to three years, only 21% believe they currently have the data maturity required to train and fine-tune such advanced AI systems.
The study, titled “Data Foundations for Government – From AI Ambition to Execution”, paints a revealing picture of the challenges that lie ahead as governments rush to modernize services with AI. Although 64% of public sector bodies are already exploring or deploying generative AI (Gen AI), the gap between AI enthusiasm and execution readiness remains substantial.
“Governments can be more agile and effective as AI augments the work of public servants — but only if the data foundations are strong.”
— Marc Reinhardt, Public Sector Global Industry Leader, Capgemini
A Mismatch Between Ambition and Readiness
The public sector sees AI—especially agentic AI, which operates autonomously within defined goals—as a potential game-changer for improving efficiency, delivering better citizen services, and doing more with fewer resources. But without strong data foundations, these ambitions risk stalling at the pilot stage.
“Governments can be more agile and effective as AI augments the work of public servants — but only if the data foundations are strong,” said Marc Reinhardt, Public Sector Global Industry Leader at Capgemini. He noted that citizen expectations are rising, and government budgets and resources are increasingly stretched, pushing public agencies to seek out more innovative and cost-effective solutions.
However, the ability to deploy advanced AI models depends largely on having reliable, secure, and accessible data ecosystems. This is where most organizations struggle.
Core Challenges Hindering Progress
According to the report, 79% of public sector organizations cite data security as a major barrier to AI implementation. Another 74% expressed concerns about trust in AI outputs, and many also grapple with compliance challenges, especially in light of rapidly evolving regulatory frameworks such as the EU AI Act and other national AI ethics policies.
Data sharing also remains a critical bottleneck. Although 65% of governments have data-sharing initiatives underway, most are still in planning or pilot stages, held back by data sovereignty and governance concerns. These limitations restrict the availability and quality of the data required for training effective and responsible AI systems.
Rise of AI Leadership in Government
Despite these challenges, the public sector is beginning to build out AI leadership. The report finds that 64% of organizations now have Chief Data Officers (CDOs) in place, and 27% have appointed Chief AI Officers (CAIOs)—a sign that many governments are starting to treat data and AI as strategic priorities rather than just IT concerns.
This growing leadership focus signals a shift toward creating dedicated capabilities for data stewardship, AI policy, and cross-departmental coordination. However, progress remains uneven, and institutional silos continue to hamper cross-agency data sharing and collaboration.
A Call to Action: Build the Foundations First
Capgemini’s report sends a clear message: to harness the full potential of agentic and generative AI, governments must first address foundational issues. These include improving data accessibility, standardization, interoperability, and trust, as well as investing in the skills and governance structures required to manage AI responsibly.
Without these essential components, even the most ambitious AI projects risk underdelivering—or worse, eroding public trust in government innovation.
“AI has the potential to revolutionize public services—but only if it’s built on trusted, shared, and well-governed data ecosystems,” Reinhardt emphasized.
As digital transformation accelerates across the globe, the ability of public institutions to turn AI ambition into sustainable execution will depend not just on technology, but on data maturity, leadership vision, and regulatory clarity.
