AI Answers: how to get citations and measure visibility

The problem no dashboard solves yet

Measuring AI visibility has become technically possible. Tools like Ahrefs, Semrush, and various specialized platforms already track how many times a domain is cited in AI-generated responses. However, as highlighted by Search Engine Journal in collaboration with Constance Tan from Ahrefs , most teams know how to measure AI visibility but very few can move it intentionally.

This is the distinction that matters for an Italian marketing manager or entrepreneur. Having a number on the dashboard isn't enough. You need to understand which variables determine whether content is cited or ignored by language models. Therefore, the value of this in-depth analysis lies precisely in translating these variables into concrete actions.

The theme connects directly to the evolution of Google's AI Overviews, which we at SHM Studio have already analyzed in detail in the context of the self-expansion of AI Overviews and its impact on organic CTR . Therefore, strategies for obtaining AI citations are not separate from traditional SEO: they are its natural evolution.

What is AI visibility in two sentences

AI visibility measures how often and in what position a brand or domain is cited in answers generated by systems like Google AI Overviews, ChatGPT, Perplexity, or Copilot. Unlike traditional ranking, this isn't about occupying a spot on a list; it's about being chosen as a trusted source by a model that synthesizes information for the user.

This distinction has profound operational implications. In fact, the criteria by which an LLM selects sources to cite do not exactly coincide with those of Google's ranking algorithm. Similarly, content that performs well in SERPs is not automatically what gets cited in AI Answers.

Three steps to increase citations in AI Answers

Based on Ahrefs' guidance and our operational analyses, we at SHM Studio identify three priority areas for Italian SMEs looking to increase their presence in AI responses.

1. Structure content as verifiable primary sources

Language models tend to cite content that presents verifiable claims, original data, or authoritative positions on a specific topic. Therefore, the first step is to review existing content with this lens: each key page on the site should directly answer a specific question, with cited sources and updated data.

Specifically, the formats that work best are clear definitions, structured lists, FAQs, and paragraphs that start with the answer before developing the reasoning. This approach is consistent with what emerged from the Pew study on AI content and its implications for Italian SMEs , which highlights the importance of editorial credibility as a selection factor.

In addition to this, it's useful to include structured markup (Schema.org) to help crawlers understand the context and type of content. This isn't a technical trick: it's a quality signal that AI systems interpret positively.

2. Build topical authority, not just backlinks

The authority that matters for AI Answers is thematic: a domain that covers a specific topic in depth and consistently is more likely to be cited than a generalist site with many backlinks. Therefore, the editorial strategy must focus on vertical depth on a few key topics.

This means producing content that links together logically, covers questions related to the same topic, and updates information over time. Consequently, an editorial plan structured by thematic clusters—like the one we propose in our SEO copywriting services — becomes the main operational tool to build this authority.

Among other things, thematic consistency also helps traditional ranking. Therefore, the investment has a double return: visibility in classic SERPs and mentions in AI Answers.

3. Monitor the third-party sources that models consult

Language models don't just index websites. They also consult Wikipedia, industry publications, specialized forums, platforms like Reddit, and authoritative journalistic sources. Therefore, AI visibility also depends on what is written about a brand or domain outside of the site itself.

In practice, this means taking care of your presence on third-party platforms: updating Wikipedia entries when relevant, contributing to industry publications, participating in relevant communities. Additionally, unlinked mentions—that is, references to the brand without a hyperlink—carry increasing weight in AI systems. Similar to what happens with offline reputation, message consistency across multiple channels strengthens the authority signal.

This aspect is directly tied to the topic of Google's new tool on editorial preferences , which allows publishers to signal their editorial priorities in response to the drop in traffic generated by AI search.

The metrics that really matter: what to track

Defining the right metrics is the most overlooked step. Many teams simply count total citations, but this number alone says nothing about the strategy's effectiveness. Therefore, it's useful to structure monitoring on three distinct levels.

• Citation frequency per target query: how many times the domain is cited in AI responses related to the company's strategic keywords. This is the main KPI.
• Position in the answer: being mentioned in the first sentence of an AI Answer has a different value than a mention at the bottom of a list. In fact, position influences the user's perception of authority.
• Referral traffic from AI sources: some platforms like Perplexity generate trackable traffic. However, Google AI Overviews doesn't always produce direct clicks, so this data should be integrated with brand awareness analysis.

In addition to this, it's important to monitor its evolution over time. AI visibility isn't static: models are updated, sources change, and content that gets citations today might lose relevance tomorrow. Therefore, monitoring must be continuous, not episodic.

To dive deeper into the broader context of this evolution, it is helpful to read our analysis on how a third of the web written by AI is changing content marketing : a phenomenon that reshapes the rules of online editorial competition.

The mistakes that wipe out the work done

Some common practices in traditional SEO strategies not only don't help AI visibility but can actually harm it. It's useful to know them to avoid them.

• Generic and undifferentiated content: if an article doesn't add anything new compared to what's already available online, an AI model has no reason to cite it. Therefore, every piece of content must have an original angle, proprietary data, or a specific perspective.
• Superficial updates: modifying an article's date without updating its content is a practice that AI systems recognize. Consequently, the update must be substantial and verifiable.
• Neglecting the natural language of questions: AI models are trained on natural language questions. Therefore, content that directly answers 'what's the best way to...' performs better than content optimized only for dry keywords.
• Ignoring cross-channel consistency: if the website says one thing and social media profiles or reviews say another, the signal of authority weakens. Similar to human reputation, consistency is a prerequisite for trust.

The starting point for Italian SMEs

For a medium-sized Italian company, tackling AI visibility might seem like a complex project. In reality, the operational priority is simpler: start with an audit of existing content, identify pages that cover strategic business themes, and check if they directly and structurally answer the questions potential customers ask AI systems.

This work naturally integrates with a strategy SEO already underway. In fact, the principles of editorial quality, content structure, and thematic authority are the same ones that guide traditional organic positioning. Therefore, it's not about building a parallel strategy, but evolving the existing one.

At SHM Studio we support Italian SMEs on this journey through our AI consulting services and the activities of Digital marketing integrated. In particular, the work on AI visibility combines with campaigns from Google Ads to ensure an omnichannel presence during the customer's decision-making moments.

To get a complete picture of the strategies and tools available, you can explore all our content dedicated to Intersection of SEO and AI visibility , where we gather analyses, updates, and operational guides on this fast-moving topic.

For those who want to dive deeper into the methodological foundations, the original Search Engine Journal analysis with Constance Tan of Ahrefs offers a solid starting point. For anyone managing a company's online presence, ignoring this evolution is no longer a viable option.