The Terrain Has Shifted: Shaping Influence in the Age of AI

You probably don’t control how your policies, arguments, or ideas are being introduced to your audience. And increasingly, neither do they.

It used to be search, click, read, interpret. But AI has changed the game. 

A memo isn’t just read anymore; it’s processed. A bill isn’t just summarized; it’s explained. Your audience often encounters your argument first through an LLM’s synthesis of it, not your original words. At the same time, more and more first drafts of press releases, policy memos, and even legal briefs are created, or re-worked and edited, by AI. Between generation and consumption, ideas now pass through multiple layers of machine interpretation.

We’re at an inflection point, on par with the rise of social media, where understanding how AI interprets, prioritizes, and cites information is mission-critical for anyone operating in public affairs, policy, or communications.

At Precision, we’ve been tracking this shift for years, building strategies to influence how LLMs influence perceptions of their reputations, policies, and crises. We call it TerrAIn Mapping.

At its core, Terrain Mapping is about both understanding and shaping how influence works in an AI-mediated landscape, and where strategies fall apart. It provides a framework to shift how ideas are surfaced, validated, and learned by the system, influencing both discovery and the first draft of thinking; from staff-level analysis all the way up to executive decision-making and policy formation.   

Most organizations are still operating as if visibility is about search result ranking or virality. Those elements are still important, but in LLM-driven systems, visibility is about inclusion, validation, and prioritization; whether your ideas are present in the sources models draw from, synthesize, and recombine when answering a question.

The real risk, and opportunity, lies beyond brand visibility; it’s in how AI systems are molding understanding of the issues that define your operating environment. A tech company is not just represented by how models describe its products, but by how they explain its key issue areas like privacy, competition, and data governance. A healthcare company is not just reflected in its treatments, but in how systems interpret science, trust, and risk. Influence now operates at the level of the issue ecosystem, not just the entity.

That shifts the objective. The whole game now is shaping the answers across issues now.We bring clarity and control to this emerging system in four ways.

1. We map demand: what questions are actually being asked, and how they’re phrased, so communications meet the moment of inquiry, not just broadcast a message.
2. We map the source ecosystem: not just where your organization appears, but whether your ideas are validated across owned content, high-trust third-party sources, and the broader conversation spaces LLMs rely on.
3. We map outputs: how different models, across audiences and personas, are interpreting your category—because the same system produces different answers depending on who is asking.
4. We intervene and reshape: identifying where your perspective is missing, misrepresented, or underweighted, and actively reweighting the information environment so your ideas are surfaced, cited, and repeated.

Across clients, sectors, and issue areas, the pattern is consistent: influence is no longer built in one place. It is constructed across layers; what you define, what others validate, and what gets repeated often enough to become default understanding within this automated ecosystem.

This is why owned content alone is insufficient, and why traditional SEO frameworks fall short. The goal is not to rank; it is to be cited, reinforced, and ultimately embedded in how the system explains your space.

Even before LLMs, research from Massachusetts Institute of Technology found that Wikipedia coverage shapes judicial behavior, influencing how judges cite and frame precedent. What gets surfaced literally becomes the law. 

And LLMs are accelerating that dynamic further, going beyond retrieving information into structuring the first draft of policy, narrative, and decision-making.

Terrain Mapping is not just about optimizing content. It’s about understanding and directing the information environment that AI systems draw from, before your message is ever read.

For public affairs and policy leaders, that creates a new mandate: your perspective must be discoverable, interpretable, and prioritized by the systems increasingly shaping how people think, draft, and decide.

We’re already doing this work with partners who recognize the profound nature of this shift. If you’re asking how your issue, your organization, or your priorities show up in AI-generated outputs today, it’s time to see it and shape it.

Because the question is no longer whether AI will influence your work. It’s whether your work is influencing AI.

Elaine Ogden | EVP, Data & Analytics

Elaine Ogden is a data and communications strategist helping organizations understand how people think, feel, and act—and turning those insights into measurable impact. At Precision, Elaine is an executive vice president leading the Data and Analytics practice. She works with companies, campaigns, and nonprofits to decode audience behavior, develop data-driven strategies, and build frameworks to measure success. Her clients span industries—from healthcare and tech to consumer brands and advocacy organizations. Elaine previously served as Deputy Assistant Secretary of State for Research and Analytics in the Bureau of Global Public Affairs, where she managed a global team of more than 75 and oversaw an $11 million budget. She led the State Department’s opinion research, media and social media analysis for communications, and spearheaded the launch of an AI-powered insights platform projected to save more than 180,000 staff hours in its first year. Elaine previously worked as the Director of Analytics at W2O Group (now Real Chemistry), helping Fortune 500 companies and mission-driven organizations craft communications rooted in data. She has also led insights and analysis work within gaming, production, and startup companies.