For the past two decades, owned media strategy has rested on two pillars: content and search. Create good content, make sure Google can find it. That formula built careers — mine included.
But something fundamental has changed. People are getting answers without clicking. LLMs are synthesising content from across the web and presenting it directly. ChatGPT, Perplexity, Copilot — they're not sending users to your site. They're reading your site and telling users what it says.
If your brand isn't visible to these systems, you're disappearing from a growing share of the information landscape. And most teams haven't even started to grapple with this.
The visibility problem nobody's measuring
Here's what keeps me up at night: we have twenty years of tooling for measuring search visibility. Rank trackers, SERP analysers, click-through-rate benchmarks. For AI visibility? Almost nothing.
Try asking Claude or ChatGPT about your brand's products. What comes back? Is it accurate? Is it current? Is it your competitor's messaging? Most companies have never checked. They're optimising for a channel they can measure while ignoring one they can't.
The brands that win in the next five years will be the ones that treat AI visibility with the same rigour they currently apply to SEO.
What makes content AI-visible
AI systems don't process content the same way search engines do. They don't care about keyword density or backlink profiles. They care about comprehensibility — can they extract clean, structured, unambiguous meaning from your content?
This means a few things become critically important:
Semantic HTML matters again. Proper heading hierarchies, meaningful section structure, descriptive alt text. The stuff your accessibility team has been asking for — turns out it also makes your content legible to LLMs.
Schema markup is no longer optional. JSON-LD structured data tells AI systems exactly what your content is about. Product schema, FAQ schema, article schema — each one gives machines a shortcut to understanding your pages without having to infer meaning from prose.
Content needs to be self-contained. AI systems often process pages in isolation. If your product page only makes sense in the context of a category page and a navigation menu, a machine reading just that page will get a fractured picture.
The practical first steps
I've been working on this with my team, and here's where we started:
- Audit your AI presence. Ask the major LLMs about your brand, your products, your category. Document what comes back. You'll be surprised — and probably not in a good way
- Fix your structured data. Run your key pages through schema validators. Fill the gaps. This is the lowest-effort, highest-impact change you can make
- Simplify your content architecture. Every page should answer a clear question, completely, without requiring the reader to navigate elsewhere for context
- Monitor over time. AI responses change as models are updated. What's accurate today might not be next month. Build a cadence for checking
This isn't about replacing SEO
I want to be clear: traditional search isn't going away. SEO still matters. But it's no longer sufficient. Owned media strategy now has three pillars: content, search, and AI visibility. Ignoring the third one is like optimising for desktop in 2015 and pretending mobile would sort itself out.
The window for getting ahead of this is narrow. Most competitors haven't started. The teams that build AI visibility into their content operations now will have a compounding advantage that's hard to reverse-engineer later.
And that's the kind of strategic asymmetry I live for.