I see plenty of overlap between SEO and AEO, but I do not treat them as the same discipline. The SEO playbook that worked reliably in traditional search will not take me as far when the goal is visibility inside AI-generated answers.
So I keep coming back to one practical question: what should I change first?
Instead of revisiting content structure for AI search, I focus on three priorities I believe deserve more attention now and three SEO habits I would intentionally emphasize less.
3 SEO priorities I would emphasize more
Establish brand authority and strong entities
Before an AI system is likely to cite my brand, it needs to understand that the brand exists, what it represents, and why it is credible. Entity recognition has become foundational to AI visibility in a way that traditional search did not always require, even though Google’s Knowledge Graph has been moving in this direction for years. Large language model training data tends to reward brands that show up consistently across trusted platforms.
When I work on this for clients, I pay closer attention to whether brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and any other source an LLM might use to understand an entity.
I also think PR and SEO or AEO teams need to work much more closely together. Earned media mentions are no longer just awareness plays; they are entity-building signals.
E-E-A-T was already pushing SEO in this direction, but author entities matter even more in AI search. When bylined experts have their own credible web presence, they strengthen the authority of the content they create.
When I can invest in entity building before scaling content, I usually see stronger AI citation potential because the credibility infrastructure is already in place.
Build topical depth with content clusters
AI systems tend to favor sources that show comprehensive authority on a subject, not just pages that happen to rank for isolated keywords. A thin content footprint is much more vulnerable in AI search than it was in traditional search.
That means I need to move beyond keyword-by-keyword planning and think more seriously about topic ownership. Instead of only asking, “What do we rank for?” I ask, “What topics do I want AI systems to associate this brand with?”
Internal linking becomes more valuable in this environment because it helps signal relationships between related pieces of content. I also treat content audits as a way to find gaps in topical coverage, not just as a way to identify pages with declining traffic.
When I can go deep in a specific niche, I often see content cited across multiple related queries. One well-built content cluster can create visibility far beyond a single keyword target.
Owning the topic cluster around the problem a client’s product solves can position that brand as a trusted resource before a sales conversation even begins. I also hear more often that buyers are finding those brands in LLMs during their research process.
Earn unlinked brand mentions and community presence
LLMs learn from the broader web, not only from pages with backlinks. A mention on Reddit, Quora, a niche forum, or an industry community can matter even when there is no link attached.
I think this is one of the bigger mindset shifts for SEO teams. AI systems look for patterns in what the web says about a brand across many sources, not only what ranks in Google. Owned content alone cannot manufacture that signal.
Trusted third-party communities such as Reddit can carry particular weight because LLMs have been heavily trained on them and often treat that content as a form of authentic user sentiment.
That makes community participation and digital PR increasingly important SEO-adjacent work. I care about whether a brand is being mentioned in the right places, even when the mention does not come with a backlink.
Monitoring unlinked brand mentions is becoming just as important to me as tracking backlinks. Tools such as Brandwatch and Mention, along with manual Reddit and Quora monitoring, can show where a brand is appearing organically and where it is absent.
I would rather talk with the team about where the brand is being discussed, whether those conversations are accurate, and whether the sentiment is positive than focus only on who is linking to the site.
Brands with an active presence in relevant communities are more likely to surface naturally in conversational, recommendation-style AI answers, including queries such as “What does Reddit think about X?” or “What’s the best Y according to users?”
For challenger brands trying to enter a category, earned community mentions can build AI-visible authority faster than traditional link building, which usually takes longer to accumulate.
B2C brands can benefit especially from genuine community presence because consumer AI queries often lean toward social proof and peer recommendations rather than formal editorial sources.
3 SEO priorities I would emphasize less
Chasing high-volume keywords with thin content
AI Overviews can absorb the click for broad informational queries. Ranking No. 1 for a head term increasingly means I may have invested a lot of effort into winning traffic that never actually reaches the site.
Search volume alone is no longer a reliable proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may send less traffic than a query with 2,000 searches that still requires a click.
I would rather create specific, authoritative content that answers a narrower question better than anything else available. I focus more on queries where the searcher needs to act, compare options, or access something only the site can provide. Those needs are harder for AI to fully resolve.
Keyword traffic potential is no longer the first metric I trust. I first ask whether someone will still need to click after AI answers the query. If the answer is no, the opportunity is not what it used to be.
Pursuing exact-match and manipulative link building
Low-quality link volume does not do much for AI citation likelihood. LLMs care more about the authority and relevance of the sources mentioning or citing a brand than raw link counts. The publications that matter for AI visibility usually have real editorial standards, and those are much harder to game.
I would focus on earning coverage and links from the kinds of sources AI systems are more likely to draw from, including trade publications, respected industry blogs, and academic-adjacent resources. The better long-term move is to build content worth referencing, not outreach that exists only to extract a link.
A hundred low-quality links will not necessarily get a brand cited in ChatGPT. Five links from publications the target audience actually reads might matter much more. Source authority is the metric I would watch more closely than link volume.
Optimizing for CTR on standard blue links
A growing share of informational queries are resolved without a click. That makes title tag and meta description optimization for CTR less valuable on queries dominated by AI Overviews. I would rather spend that time trying to become the cited source inside the AI answer.
For queries where clicks still happen, I put more weight on transactional and navigational intent because those searches are more resistant to full AI resolution.
CTR optimization assumes a searcher is choosing between blue links. For more queries now, that choice is shaped before the traditional results even become the focus. The opportunity has moved higher on the page.
The payoff is not always more traffic
There are more shifts I could make, but these are the first ones I would prioritize. I may lose some volume in traditional SEO metrics such as impressions and clicks, but that should matter less if the downstream business metrics remain strong. In AI search, I care more about conversions, pipeline, and revenue than vanity traffic. That is the tradeoff I believe this new search environment increasingly rewards.
Inspired by this post on Search Engine Land.
