Brands built around a cause tend to have an incredibly valuable content asset right under their noses. Most of them just don’t utilize it.
I’ve been thinking a lot lately about what it means to do work that builds more than a bottom line.
With everything happening in the world right now: climate crises, the rollback of women and civil rights, scientific breakthroughs (and budget cuts), AI and the future of work being rewritten faster than most of us can keep up with…
It can all feel pretty heavy while you’re mapping content calendars and creating briefs.
And for me, it’s been pushing me toward defining my “why”.
I find myself increasingly drawn to purpose-driven brands. The ones at the cutting edge of impact. The change-makers built around a cause: where a scientist, researcher, or advocate spent years championing something and then built a company around it.
Where the people on staff have published studies, spoken at conferences and built followings not for personal-brand reasons, but because they genuinely care about a problem and have spent their careers trying to solve it.
These people and brands have always inspired me, but I’ve never really understood how I could contribute.
What I’m starting to see is that instinct maps closely to what’s winning in AI search right now.
The brands built on research have a moat — and most aren’t using it
Think about the companies you’d call purpose-driven or impact-focused. For example, the founding medical director who has a following because they’ve spent fifteen years researching and championing a specific cause.
There is so much genuine, credentialed, lived experience inside the walls of those organizations.
That expert knowledge is one of the most underutilized content assets in marketing. Not because brands don’t value it — they do — but because they haven’t connected it to a content strategy built to amplify their voice.
AI search is the inflection point these brands have been waiting for.
What the research is telling us
AI search tools aren’t just indexing keywords. They’re evaluating credibility signals. E-E-A-T (experience, expertise, authority, and trust) is present in 96% of AI Overview citations, and in 2025, it became an active filter vs. a quality guideline. Content that can’t demonstrate real authority gets screened out before it’s even considered.
Here’s why it works the way it does. When an AI system gets a question, it’s not just retrieving content, it’s doing something researchers call entity resolution. It’s trying to identify which real-world people, organizations, and concepts are credibly and independently associated with a topic.
The key word there is independently. AI cross-verifies expertise across sources that have no reason to coordinate. A named researcher quoted in a peer-reviewed journal, mentioned in a trade publication, and featured on an industry podcast creates three independent trust signals pointing at the same person.
That’s what AI is looking for, and it’s exactly what most brand content doesn’t provide. A ghostwritten blog post on your company site gives AI one signal, from a source with every reason to be biased.
Another cool finding? Expert quotes boost AI citation likelihood by 37%, and adding statistics by 22%. This confirms the hypothesis that SME input is hella valuable for LLM exposure.
And for brands that aren’t household names yet? For challenger brands, building authority through thought-leadership content is one of the few viable paths to AI visibility.
And why can’t search-focused content be thought leadership content?
I saw this play out firsthand with a client recently. When we built our content engine around their internal expert, a legitimate researcher with published work and a university affiliation, AI paid attention FAST.
Content tied to verifiable experts, people with clearly defined credentials and consistent publishing histories, tends to receive more weight in AI citation systems than anonymous or brand-attributed content.
Your expert doesn’t need to be famous. They do need to be a documented expert in their field.
Three ways internal expertise can influence AI search
Now, this is all well and good in theory, but no published expert has time to sit down and create content for your website (and beyond). So, it’s on marketers to bake it into our systems and to find creative ways to extract and promote it.
1. Create original data, or surface research you already have
If your brand (or SME) has funded/contributed to a study or holds proprietary data of any kind, it needs to be explicitly included in your content, with attribution.
Original research contains the three elements AI rewards most: novel statistics, citable methodology, and quotable expert findings.
If that data already exists somewhere within the organization, your job is to shout it from the rooftops so that AI can find and cite it.
2. Borrow the authority your SME already built, and amplify it
Has your expert published research? Led conference talks? Commanded a massive LinkedIn presence?
That external credibility is a signal AI can already read, and it carries over to your on-site content when woven in properly. A researcher who has been cited in peer-reviewed journals brings that expertise with them into your articles.
Their LinkedIn posts, podcast appearances, and external publications aren’t separate from your content strategy. They feed it. Named authors with clearly defined credentials and consistent publishing histories carry more weight with AI systems than content attributed to a brand or left anonymous.
If they don’t want to byline the article (fair), then a “reviewed by expert” attribution with a linked bio accomplishes a lot of the same thing. But really, an article bylined by the expert is the gold standard, even if it’s ghostwritten after a knowledge extraction.
3. Use your content to own the category association — not just the keyword
This is a big one. When your brand consistently appears near relevant topical keywords across the web, AI models begin to associate your content with that topic. And THAT should increase your chances of being referenced in AI-generated answers.
If a brand appears thousands of times alongside the same topic terms, in articles, papers, social posts and YouTube transcripts, those words become mathematically inseparable in the model’s training data.
For mission-driven brands, this is a real opportunity.
If your SME has spent a decade championing a specific problem, and your brand exists to solve it, then you have the raw material to build that association consistently and credibly over time. On your site, through owned channels, across the places where your audience is already paying attention. That’s how you teach AI that your brand owns a category.
Content is more powerful when it means something. If you’re doing meaningful work in the world right now, I’d love to partner. Feel free to reach out, and we’ll talk content strategy!
Karli is content marketing consultant behind Wild Idea, a content marketing and SEO collective focused on driving big results. With over 12 years in the marketing industry, she’s worked with brands large and small across many industries to grow organic traffic and reach new audiences. She writes on everything from marketing, social, and SEO to travel and real estate. On the weekends, she loves to explore new places, enjoy the outdoors and have a glass or two of vino!
