Right now, most marketing teams don’t need convincing that AI visibility is important. The question has shifted from “should we think about this?” that “what exactly do we do about it?” And that’s where most brands get stuck, not because the opportunities are hard to come by, but because making them happen requires more collaboration across multiple teams than most people expect.
Games that move the needle on AI search aren’t all heavy lifting. Some of them are real quick wins, and may come right off your existing SEO wish list. But almost all of them sit at the crossroads of more than one group, and knowing which group needs to own what is just as important as knowing what to do in the first place. Your SEO team can’t achieve success on its own, so that’s where I’d start.
AI can only see what you show
The first thing I will make sure is that AI can read your site. It sounds basic, but it’s the kind of thing that gets missed precisely because everything seems fine on the outside.
AI search engines can only read what is visible to them through server-side rendering. If your site uses JavaScript to load client-side content, there’s a real chance that AI agents are working on an image that’s part of your product.
To understand why this is important, it helps to think about what actually happens when someone asks an AI engine a question. AI doesn’t browse your website like a human would. It reads the version of your site that is delivered directly from your server, before any JavaScript runs. If your navigation, product descriptions, or your most important content only appears after the page has loaded and the text has been rendered, the AI may never see it. It’s drawing conclusions about your story with an incomplete picture, and you’ll have no way of knowing.
Fixing this requires your engineering and development teams, not your SEO team. Your SEO team can inspect the site, identify which pages are affected, and explain what needs to be changed. But the real work is sitting in the race for development, which means that someone at a leadership level has to make their case and the product’s priorities. Paid media teams are useful here as well, as they often have a clear idea of which pages are driving the most traffic and should be prioritized first.
New signals and reliability AI engines really learn
Once the AI can read your site, the next question is whether it trusts what it reads. AI response engines factor in how recently your content has been reviewed when deciding what to cite, and one of the clearest ways to communicate is through schema tagging.
The schema tag sounds technical, and in terms of usage it is, but the concept is straightforward. A small addition to your site’s code, invisible to human visitors, that tells search engines and AI engines certain things about your content: when it was last updated, who wrote it, what format it is in. With video content directly, the schema can show the AI that the video is on the page, what it covers, and when it was produced. Without that signal, the video may not exist from an AI visibility perspective.
The reason most brands haven’t done this isn’t because it’s too difficult. It’s that schema sits in the gap between SEO and development, with no party officially in charge of the deployment process. The fix is a static workflow: The SEO team has to define which schema is needed on which pages, and the development team has to implement it as part of a regular release instead of treating each addition as a one-off project. Creative and social groups are part of this discussion as well because they are the ones who produce the video content that benefits the most from proper branding.
Stop preparing keywords. Start preparing articles.
This is the single biggest opening I see in AI search right now, and it’s also the one that requires the most fundamental change in how teams work together.
AI doesn’t rate pages for individual keywords the way traditional search does. It rewards thematically organized content and demonstrates comprehensive expertise across the subject area. So if your site has twenty pages each touching on its topic without any of them going into enough depth to fully own it, you may be passed over in favor of a competitor whose content is designed to show real depth.
A method that reveals which themes the AI really likes embedding analysis. Without getting too deep into the technical details: AI engines understand language by mapping words and ideas into clusters based on how related they are. Embedding analysis allows you to see those clusters, to understand which themes and content structures the AI treats as authoritative in your category, and where your content is similar or inconsistent.
What this produces is a data-driven topic framework: a map of the topics your content should be organized around if you want to appear in AI responses. But here is where the organization piece becomes critical. That framework is only important if it becomes a shared workflow across your content, social, and PR team, not just another SEO script that sits in a folder that no one else opens. If your PR team is pitching stories to publications, they should be covering similar themes. If social media forms the planning calendar, those important topics should be your backbone. SEO leads the analysis, but the output should be for everyone.
Your website is only part of the picture
This is a point that often changes the way people think about AI search entirely. AI responses are not only integrated into your website. They are generated from your site, Reddit threads, YouTube videos, forum posts, and publisher streams simultaneously. Your product’s AI story is shaped by what’s in all those areas, not just what your team directly controls.
The practical result is that a brand can have a very good website and well-made content, and still lose a place in the AI search because the wider conversation around it is small, fragmented, or pointing in different directions.
Getting this right means your SEO team needs a common line of communication with your social and community teams, sharing AI-revealed and leveraged topics so that platform-specific content is informed by common priorities. This is not about SEO calling other channels. It’s about making sure that signals across the web are compatible, which only happens when someone connects.
PR has a new job, and most snapshots don’t show it yet
The end game is one that requires a significant change in how success is measured. AI responds by combining sentiment from your PR coverage, what your community is saying, and your publisher’s quotes all at once. Slander or misrepresentation is not always contained in the publication in which it appears. It’s baked into what AI tells your customers about you.
So the priority is no longer just to earn links from high authority publications. There is increasing transparency and accuracy in the sources that AI actually cites, and corrects the record when incorrect information exists anywhere in the ecosystem. That’s a different brief than what most PR teams are currently working on. It also means that a paywalled publication, even if it is a respected publication, does not do a significant job of AI visualization work. If AI engines can’t access the content to read it, they won’t say so.
This requires your SEO team to provide PR with data on what sources LLMs are actually coming from and when product mentions are incorrect or non-existent. PR then takes action on it. Partnership teams are also part of this conversation, because co-created content and third-party endorsements are exactly the kind of open, readable signals that build AI credibility in a way that a paid feature can’t.
A common thread
None of these games are private. The techniques are there, the data is available, and the fix is well understood. What most organizations lack is a comprehensive process to implement them. Your SEO team can run an analysis and flag an opportunity, but they can’t deploy a supply fix without engineering, roll out a schema without a dev, manage a content calendar without a content team, or redirect a PR directory without a shared measurement framework.
Brands that are getting traction in AI search right now aren’t doing very complex work. They are the ones who have made AI visibility a shared priority across the organization, rather than a problem for one team to solve alone. That change starts with someone being willing to have a tough internal conversation, not about what the AI search opportunity is, but about who exactly owns the job to go after.
