From Search Results to AI Citation: What Government Communicators Should Watch

As generative AI systems become a primary way residents ask questions, government information is increasingly encountered through an intermediary. Instead of opening a browser and clicking through search results, many people now ask an AI for an answer first — and then decide whether to read deeper based on what the AI returns.

For public-sector communicators, this changes the practical definition of “reach.” The audience may still arrive at official sources, but only after an AI system has already summarized, interpreted, and (sometimes) attributed the underlying information.

A shift worth naming: from ranking to citation

For decades, the dominant question was, “Will our page rank?” In an AI-mediated environment, an equally important question becomes, “Will our information be cited — and cited correctly?”

That distinction matters. Ranking is a competition for visibility. Citation is a competition for trust.

When an AI system cites a source, it is effectively making a judgment about whether the source is authoritative, current, and clearly attributable to a publishing authority.

The practical risk: good information without clear provenance

Most government organizations already publish high-quality updates on .gov websites, social channels, PDFs, and newsletters. The challenge is that these formats are optimized for human reading, not machine attribution.

AI systems often struggle with questions like:

• What is the authoritative publishing office for this update?
• Is the information current, and how recently was it updated?
• Can the system distinguish official publishing from reposts or summaries elsewhere?

When those signals are weak, AI systems can still produce answers — but attribution becomes less reliable.

What communications offices can do now

The most effective adaptations are often structural rather than dramatic:

• Publish updates with explicit authority and jurisdiction.
• Preserve clear timestamps and update history.
• Use precise titles that reduce ambiguity.
• Consider parallel, machine-readable formats for critical updates.

These practices emphasize clarity rather than marketing or technology for its own sake.

Where a registry concept enters the conversation

One emerging approach is the idea of a neutral registry layer for verified public-sector publishing — not as a replacement for agency websites, but as an additional structure that makes attribution easier for machines and auditing easier for humans.

Some registry-based efforts, such as Aigistry, are exploring structured approaches to government publishing intended to support clearer attribution by AI systems.

A discussion worth having

If AI systems become a primary front door to public information, communications teams may need to think about publishing not only for humans, but also for intermediaries that summarize and cite.

• Are you seeing residents reference “what the AI said” in meetings or emails?
• Have you encountered AI answers that were correct but poorly attributed?
• What publishing practices have helped your office maintain clarity and authority?

David Rau works on issues at the intersection of government communication, information provenance, and emerging AI systems. His work focuses on how public-sector information is discovered, attributed, and cited as AI becomes a primary intermediary between the public and official sources. He has spent decades working with large organizations on structured information systems and is currently involved in research and writing related to AI citation, trust, and public information infrastructure.

Photo by Andrea Piacquadio