Key Takeaways
- AI summaries are where buyers find their information. Optimizing for AI search with mass-produced content won’t work.
- Marketers must build authority through a differentiated viewpoint, human expertise, and original insight.
Nine in 10 B2B marketing leaders report that AI visibility is an investment-level priority or higher. But many brands are running the classic SEO playbook to capture AI search demand: publishing more content, covering more topics, optimizing pages for AI crawlers. The moves that once built SEO visibility won’t build visibility in AI answer engines. Instead, they will accelerate invisibility. AI systems don’t reward content volume. They reward authority. And most brands are producing content that gets averaged into the internet’s background noise rather than cited as a trusted source.
Wide-ranging, mass-produced content gets averaged into general AI answers rather than specific citations. That’s a problem. Two-thirds of surveyed B2B buyers use GenAI as much or more than search when researching vendors. If your brand isn’t cited, you’re effectively invisible.
The biggest mistake marketers are making is treating AI visibility like SEO 2.0. AI engines don’t rank pages the way search engines did. They synthesize patterns, corroborate signals, and surface the sources the market appears to trust most.. Cited brands are distinctive enough that third-party sources — analysts, journalists and communities — talk about them by name, telling AI systems which companies the market actually trusts.
Showing up consistently in AI answers requires category authority. We define it as an accumulation of signals that tell LLMs (and your buyers) that your brand is the expert source on this topic. Brands with Category Authority shape the answers. Brands without it get summarized, generalized or ignored. To achieve this goal, unique insights beat a volume of generic output.
The Consequences of Low Category Authority
In this AI era, if your brand does not define its market position clearly and repeatedly, competitors and other sources will define it for you. And increasingly, AI systems become the mechanism through which that perception spreads.
I recently spoke with a marketing executive at a global consumer brand that had just revamped every site in its portfolio to optimize for search. At the same time, the team was incorporating AI to scale content production and grow search visibility faster. Was it working?
Three Signals that Drive Category Authority
We ran unbranded category-specific prompts through LLMs to reveal how the company showed up in the answers. It didn’t. Its competitors did. When we searched the brand name directly, the AI citations included outdated information about its own products.
The company was being overshadowed in one search, and wrong information cast them as the weaker option in the other. The marketing team executed the traditional SEO playbook almost perfectly, and they still lost control of the narrative. This demonstration reveals the importance of disseminating insight rather than flooding your site with generic “optimized” content.
The gap between being summarized and being cited boils down to three signals that most brands don’t produce (at least not consistently):
Specificity
AI answer engines reflect how people query them. When a dozen brands produce similar content on the same broad subject, AI has no reason to cite any of them. The algorithms reach for sources tied to a specific buyer, use case or pain point because that’s what matches the query.
That’s why relevance beats volume. AI engines cite sources that demonstrate sustained expertise in a particular niche, rather than cover every adjacent topic in a category.
Brands that get cited have built a clear solution-problem link. For example, Salesforce publishes content for specific roles in defined industries — a page on lead routing for financial services sales teams, a guide to patient engagement for healthcare admins, and implementation playbooks for retail marketers using their commerce cloud.
The content isn’t “How to use a CRM.” It’s “How a regional bank’s commercial banker should structure their opportunity stages.” Most competitors haven’t covered topics at that level of granularity.
The irony is that many brands are using AI to produce the exact type of interchangeable content that makes AI engines less likely to remember them.
To achieve specificity, you must define and prioritize targeted areas of expertise, identify related buyer contexts and pain points, and commit to regularly publishing on specific topics within the subject.
Original Insights
Buyers, media and AI reward new information over regurgitated insights. That’s why brands need proprietary data and research that adds new knowledge to the conversation. When your data is the only place a specific number, pattern or finding lives, citations follow the data. Proprietary data matters because it creates something increasingly rare in AI-mediated discovery: an original point of view grounded in evidence.
AI systems reward perspectives that introduce net-new understanding into the ecosystem.
When journalists, analysts and practitioners pick up your findings, those third-party references become additional authority signals to AI platforms.
Winning brands release data that no one else can, such as insights from their customer base, their product, their practice or their unique vantage point.
To properly execute original insights, you must identify the data assets you have or can build (customer data, survey programs, product telemetry, practice observations), invest in research that produces unique findings, and commit to releasing them consistently enough that AI and third parties learn to expect them.
Affirmed Credibility
According to Muck Rack, the majority of AI citations come from earned media, not paid placements or brand-owned content. Affirmed credibility is established when trustworthy voices outside a brand’s owned platform reinforce your brand’s point of view. Contributors include both internal and third-party industry experts.
LLMs weigh message continuity across independent sources. When the same perspective shows up multiple places, the brand most associated with that idea becomes more likely to appear in AI outputs.
Internal SMEs are the first layer of credibility. These experts bring real-world experience and humanity to messaging. When known subject matter experts echo a brand’s narrative, AI engines receive more authority signals.
IBM Security X-Force is a strong example of both affirmed credibility and original insights. The unit has a deep bench of named experts, each covering a specific lane and publishing under their own names. Their annual Threat Intelligence Index gets amplified by external security publications and practitioners. That cross-source corroboration is why X-Force is one of the most commonly cited cybersecurity sources in AI answers.
To gain affirmed credibility, you must support your internal experts in disseminating their perspectives consistently. Identify the third-party voices your buyers already trust (analysts, journalists, podcast hosts, influential practitioners, trade publications) and build relationships that bring those outside voices into alignment with your position.
While competitors optimize themselves into interchangeability, authoritative brands are becoming impossible for AI systems to ignore. Visible companies will produce the clearest signals of expertise, perspective and trust, not the most content. We’re in the AI era. Stop treating visibility like a distribution problem and approach it like an authority problem.
Andrew C. Wheeler is Chief Executive Officer of Skyword, where he leads the company’s strategy and operations. With nearly three decades of experience in high-growth marketing and technology organizations, he helps global brands rethink how they build authority and drive results. Since joining Skyword in 2013, Andrew has scaled the company’s services across Professional Services, Editorial, Creative Operations, and Customer Success. Prior to Skyword, he held senior leadership roles at BLiNQ Media, acquired by Gannett, and iProspect, part of Dentsu, where he led regional operations and enterprise client relationships. Known for combining clear strategy with disciplined execution, Andrew focuses on building growth models that translate vision into measurable performance.
