AI Literacy: The Missing Piece in Government Innovation

AI Literacy as a Leadership Competency

AI literacy is the first real step toward incorporating artificial intelligence into government organizations because leaders cannot responsibly manage a workforce that uses AI if they do not understand how it works, what it can and cannot do, and where it can fail. Without this foundation, decisions about tools, policies, and workflows are driven more by vendors and early adopters than by public purpose and accountable leadership. When leaders and staff share a basic fluency in AI, the government can harness the technology to support the mission, protect citizens, and reinforce democratic values.​

Artificial intelligence has moved quickly from abstract concept to everyday reality across the public sector, with agencies piloting tools that summarize reports, draft correspondence, and analyze large datasets. That pace of change often tempts organizations to implement technologies first and figure out governance later, but this approach is backwards. For government, AI must start with literacy: a shared understanding of what AI is, how it operates at a high level, and what it means for mission, accountability, and public trust. Literacy does not require every public employee to write code, but it does require sufficient knowledge to ask good questions, recognize risks, and make informed decisions about when and how AI should be used.​

Ethics and Public Trust

Ethical awareness is inseparable from AI literacy in government because public institutions carry a special obligation to act fairly, transparently, and in ways that respect rights. When officials understand how data choices, model design, and training processes can introduce bias, they are better equipped to prevent harms and ensure that automated systems do not disproportionately disadvantage certain communities. Literacy helps staff translate broad principles such as fairness and accountability into concrete practices, such as documenting data sources, setting up human review, and defining clear appeal mechanisms for AI-informed decisions.​

Without that understanding, ethical oversight can easily become a superficial checkbox exercise or be outsourced entirely to vendors who do not bear the same public accountability. AI-literate leaders, by contrast, know to ask whose interests a model serves, which groups might be underrepresented in the data, and how to monitor systems for drift or unintended consequences over time. This capacity to interrogate AI use builds public trust, because citizens can see that technology is being deployed with deliberate care rather than blind enthusiasm.​

Cybersecurity and Shared Vigilance

Cybersecurity risks expand as agencies adopt AI systems that rely on large datasets, cloud infrastructure, and integrations with existing platforms. Threat actors can target not only traditional networks but also the AI models themselves, attempting to poison training data, manipulate inputs, or exploit weaknesses in how AI tools connect to other systems. A workforce with at least basic AI literacy is better positioned to notice anomalies, follow secure practices, and support specialized security teams in identifying and responding to threats.​

When employees understand the difference between training data and live data, or between a predictive model and a generative system, they can more quickly recognize when some