Many marketing teams are applying GenAI to the same SEO playbook they’ve used for years, essentially using it as a shortcut for content volume. But AI-written assets often read the same from brand to brand, and that sameness is exactly what strips away brand visibility and citability. Humans should create original content and use AI as an analytical and scaling tool instead.
AI made content creation easier and faster, but it also changed how brand discovery works. Many buyers no longer start their journey with a Google search that leads them to a business’s website. According to SparkToro, 68% of searches end without a click, and a Skyword consumer survey found nearly half of full-time employed respondents have already made a major purchase or important decision based primarily on AI-generated information. If you’re not showing up in AI engine answers, your buyers may never find you.
Automating the production of generic content that feeds these algorithms feels productive, but it weakens signals that actually drive discovery. AI systems don’t reward volume the way search engines do. The LLMs recommend brands with content that demonstrates originality and authority. When companies prioritize volume over unique insights and ideas, they become indistinguishable from competitors.
I recently spoke with a marketing executive at a global consumer company that had just optimized its sites for search. The team used AI to scale asset creation. When we ran unbranded category prompts through LLMs, the brand did not appear, but its competitors did.
Even with its high content volume, the messaging did not create enough differentiated signals to earn LLM trust, and the brand lost control of its own narrative.
The harder — but better — way to use AI is for analyzing where the business can own the conversation and for scaling a human’s proprietary insight across formats and channels.
AI citations require signals AI can’t generate
Many marketing teams are applying a tactical “find and replace” strategy for AI discoverability, switching target keywords for AI prompts. Then they use GenAI to crank out massive amounts of content to answer these specific questions. This approach floods the internet with more of the same ideas.
When multiple brands publish similar guidance on broad topics, AI systems have little reason to associate any one brand with the answer. The content gets synthesized into the category average.
If your content doesn’t express a clear point of view, expertise or conviction, it gets flattened or explained by someone else.
Marketing teams must stop striving for visibility through volume and pursue category authority. It’s not the same playbook as SEO. AI discovery favors content containing what LLMs cannot fabricate:
- A unique category narrative: Identify a focused set of topics that the company has the expertise, evidence and credibility to own. Then create a distinctive point of view that no one else has. AI can’t produce these ideas because they originate inside the organization. Differentiated positioning gives AI systems a clearer reason to associate the brand with a specific category position.
- Original data and insights: AI answer engines cite new information that they can’t synthesize from their training data. Organizations need to publish proprietary data, research and human perspectives that add new knowledge to the conversation. The strongest original insights are connected to identifiable experts whose experience and credibility extend beyond the company website.
- Consistent presence across channels: Messaging must be consistent everywhere it appears. AI systems are more likely to surface brands that have repeated signals of expertise, perspective and trust across credible sources.
AI strengthens brand authority with analysis
A tactical marketing approach asks, “How do we produce more content for more queries?” A strategic authority approach asks, “What category do we have the right to win in, and what evidence do we need to become a trusted source in that category?”
AI gives humans data to answer those questions. AI supports:
- Finding opportunities through market analysis and gap identification.
Many marketers use AI for keyword research. This insight can be useful, but it’s too narrow. Search data is only one view into what buyers care about.
AI is more valuable when it helps marketers analyze patterns across a broader set of inputs: customer conversations, sales calls, SME interviews, prospect questions, category conversations and competitor messaging. AI reveals how the market actually talks about problems and where the existing conversation is thin or non-existent. Human marketers can devise messaging that fills these gaps.
We saw this with one of the nation’s leading specialty pet food brands. Their content already covered the right categories, but our AI-supported analysis, including industry, competitor and audience research, showed an opportunity to strengthen how content reached and resonated with its core audience. Pet parents don’t ask questions the way veterinarians do, and the brand’s more scientific framing was creating distance from the very audience it needed to reach. These insights helped drive content strategy updates for the brand, alongside deepening topical coverage, to match how pet parents actually think and talk about their pets’ nutrition and health.
- Strategies to bridge content and perception gaps
AI enables marketing teams to proactively audit how the brand is appearing across different AI systems. This analysis is only the first step. Combining this information with the market research shows teams the next steps for content strategy to improve where and how they’re found.
The new assets can’t be general how-to pages about the topics where the brand is absent. They need to bridge the areas where the company already has credibility with the gaps it needs to close. And the content must contain the company’s unique ideas and data.
IDEXX’s veterinary business shows this in action. After interviewing SMEs and using AI to analyze where the brand had credibility and where it was missing from the conversation, the brand found it already had strong content proving its authority in oncology, but much of it was locked inside courses in the IDEXX Learning Center rather than reaching the vets who needed it. Bridging the gap means extending the brand’s authority into the channels this audience uses by pulling expertise out from behind the gate and rebuilding it into content designed around how and where those practitioners search for information.
- Scaling human insight across channels
Humans create the original insight; AI adapts and scales it. With the new AI-influenced sales funnels, a single website asset likely won’t move the needle on visibility, no matter how well it ranks in search.
The brand’s expertise must reach audiences across the channels and sources they use to research problems, evaluate perspectives and make decisions.
Once the marketing team defines the brand narrative, collects the proprietary information and crafts the foundational story, AI creates derivatives from that core asset, such as emails, social posts and sales materials. The system works within parameters established by the marketing team, tailoring the core asset to different channels and for different audiences while preserving its central argument, evidence and point of view. Marketing teams can disseminate their narrative at scale without risking message drift that happens when they spend hours rewriting the same idea 20 different ways.
When a distinctive point of view appears consistently across accessible, credible sources, AI systems have stronger signals connecting the brand with that subject. Human buyers also use the assets to corroborate what they read elsewhere.
AI-generated volume is exactly what’s making brands indistinguishable from their competitors, lost in the noise. Don’t settle for AI-created commodity content based on a list of prompts. Companies earn citations by using AI to analyze their category and surface opportunities to lead, then building a connected content ecosystem around the brand’s proprietary ideas, evidence and human expertise.
