
6 Jul. 2026 - Amy Firbank - Total Reads 364

Identifying topic authorities for AI search engines means understanding which sources an AI system consistently draws from, cites, or defers to when generating an answer within a specific category, and using that understanding to guide both your own content strategy and your assessment of who you’re actually competing against for citation. AndMine is a Melbourne based digital agency helping Australian businesses navigate this, and this piece sets out a practical method for doing it.
Traditional SEO competitor analysis focuses heavily on who ranks where in search results. Identifying topic authorities for AI search adds a related but distinct layer: understanding which sources an AI system treats as reliable enough to cite directly when answering a question in your category, since these are the sources you’re genuinely competing against for citation, not necessarily the same set of businesses you compete against for traditional rankings.
In my experience, these two lists overlap significantly but aren’t identical. A business can rank well traditionally while rarely appearing in AI generated answers, if a handful of other sources in the category are consistently treated as more authoritative or more clearly structured for citation.
A practical method starts with testing a set of relevant questions directly against major AI systems, ChatGPT, Perplexity, and Google’s AI Overview, the kind of questions your potential customers would genuinely ask. Note which sources get mentioned, cited, or linked repeatedly across multiple related queries, not just once. Sources that appear consistently across several different but related questions within your category are functioning as topic authorities for that space, whether or not they’d describe themselves that way.
Pay attention not just to who gets cited, but to what kind of content gets cited. Often it’s a specific type of page, a detailed comparison, a data-backed explainer, an authoritative definitional resource, rather than every page a given source publishes. Identifying the specific content type that earns citation within your category is often more useful than simply identifying the authoritative source in general terms.
Once you’ve identified the topic authorities and the specific content types earning citation in your category, AEO services, answer engine optimization specifically, become the practical mechanism for closing the gap. This typically means restructuring your own content to match the level of specificity, structure, and evidence that the identified authorities demonstrate, rather than attempting a generic content overhaul disconnected from what’s actually earning citation in your space.
This is a genuinely different starting point than typical content planning, since it’s grounded in observed AI behavior within your specific category rather than general best practice assumptions. A business that skips this step and applies generic AEO principles without checking what’s actually earning citation in its category risks optimizing for the wrong bar.
An AI search agency with genuine AEO and GEO experience can typically run this kind of topic authority analysis more efficiently than an in-house team doing it for the first time, since the process benefits from having tested many categories before and recognizing patterns in what earns citation across different industries. That said, a business with the time and internal capability can absolutely run a basic version of this analysis itself using the method described above, particularly as a starting point before deciding whether a more comprehensive, ongoing engagement with an agency makes sense.
Identifying topic authorities isn’t the end goal. It’s a diagnostic step that should inform three practical actions. Restructuring your own priority content to match the specificity and evidence standard the identified authorities demonstrate. Considering whether a genuine content or data gap exists that you’re positioned to fill better than the current authorities, since AI systems do cite new, more specific, or more current sources over time as they become available. Monitoring whether your own content’s citation frequency changes over subsequent months as you apply these changes, since this is the most direct way to tell whether the analysis is translating into real results.
Consider a business in a professional services category testing five or six realistic customer questions against ChatGPT and Google’s AI Overview. Across those questions, two consistent patterns tend to emerge: a small handful of sources get mentioned repeatedly, often three to five sites regardless of the specific question asked, and those sources tend to share common characteristics, detailed, specific answers backed by named credentials or data, rather than generic marketing copy.
From there, the useful next step is auditing your own equivalent content against that same standard. If the identified authorities consistently cite specific data, named case examples, or credentialed expertise, and your own content relies on general claims without that same specificity, you’ve found the actual gap to close, not just “write more content” but “write content that matches the specificity standard your category’s AI-trusted sources have already established.” This is a more actionable finding than a generic content audit typically produces, because it’s benchmarked against what’s actually earning citation right now, not a theoretical best practice.
Look for consistency across multiple related questions within your category, not a single query. A source appearing repeatedly across several different but related questions is functioning as a genuine topic authority, rather than being cited coincidentally on one specific query.
Yes, particularly in narrower or more specific sub-categories where existing content is thin or outdated. AI systems do cite new, sufficiently specific, and well-evidenced sources, especially in areas where established competitors haven’t provided a clear, direct answer.
Given how frequently AI systems and their underlying models change, revisiting this every few months for your most important categories is a reasonable cadence, rather than treating it as a one-time analysis. A source that’s a consistent topic authority today may be displaced within a year by a competitor who’s since published more specific, better-structured content, so treating this as a periodic check rather than a single audit protects against assuming the picture stays static.
It overlaps but isn’t identical. Traditional competitor analysis focuses on ranking position. Topic authority analysis for AI search focuses specifically on which sources get cited or referenced in AI generated answers, which can be a different, sometimes smaller, set of sources.
Identifying topic authorities AI search engines actually trust means testing real queries directly against major AI systems and noting which sources and content types earn consistent citation within your category. AndMine recommends treating this as a genuine diagnostic step that directly informs your AEO content priorities, rather than skipping straight to generic content restructuring.
If you’d like help identifying the topic authorities in your specific category and closing the gap, get in touch with AndMine for a straightforward conversation.
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