Milena Traikovich is a powerhouse in the world of demand generation, specializing in the intricate mechanics of how B2B brands capture and convert high-quality leads. With a deep background in performance optimization and data analytics, she has spent years helping companies navigate the shift from traditional search engine dominance to the emerging era of AI-driven discovery. In this discussion, she explores the evolving landscape of Generative Engine Optimization (GEO), breaking down why current brand visibility is lower than many realize and how technical B2B firms can recalibrate their content strategies to stay relevant in an AI-first world.
Many B2B brands currently appear in fewer than 5% of the AI-generated responses relevant to their niche. How does this low visibility impact a company’s long-term sales funnel, and what are the first steps a marketing team should take to audit their current AI presence?
When a brand appears in fewer than 5% of relevant AI responses, they are effectively becoming invisible at the very moment their customers are making critical research decisions. Our research into over 1,000 prompts showed that only 21% of brands manage to appear in more than 25% of the answers that actually matter to them, which means the vast majority of the sales funnel is leaking before it even begins. To fix this, the first step is a rigorous audit using GEO-specific tools to analyze brand mentions across engines like ChatGPT, Perplexity, Grok, and Gemini. You need to identify the “visibility gap” by testing the specific technical topics you expect to rank for and seeing which competitors or third-party sites are taking those citations. Without this data-driven baseline, you are essentially flying blind while your prospects transition away from traditional search bars toward interactive AI chatbots.
Owned media websites are cited twice as often as earned media sites in generative AI answers. Why do these models seem to prioritize brand-controlled content over traditional PR, and how can companies better leverage their partner ecosystems to broaden their footprint in these summaries?
The data clearly shows that owned media is a powerhouse, with more than twice as many owned websites being cited compared to earned media sites. This happens because AI models crave the deep, structured, and authoritative technical data typically found on a company’s own product pages or documentation hubs. However, relying solely on your own domain has diminishing returns; even the most cited brands often top out at about 31% visibility. To break through that ceiling, you must extend your content footprint into your broader ecosystem, ensuring that your channel partners and integrated technology collaborators are also hosting detailed information about your solutions. By treating your partner’s web real estate as an extension of your own, you create a “surround sound” effect that makes it much more likely for an AI to pull your brand from multiple high-authority sources.
While some marketers focus heavily on written text, YouTube appears in the top citation lists for the vast majority of B2B brands. What specific types of video content are most effective for AI indexing, and how should brands integrate video into their broader technical SEO strategy?
Video is no longer optional for GEO, especially considering that about 75% of the brands we analyzed had YouTube among their top 25 cited domains. AI models, particularly Google’s Gemini, are increasingly sophisticated at parsing video content to provide direct answers to complex B2B queries. For maximum effectiveness, brands should focus on high-utility video content like technical “how-to” guides, product demonstrations, and engineering deep dives that answer specific industry questions. This video strategy must be tightly integrated with your technical SEO by ensuring transcripts, metadata, and on-page context are all aligned so the AI can easily connect the visual data to the written word. If you are only publishing white papers and blogs, you are ignoring a massive channel that the AI engines are clearly prioritizing.
Long-form LinkedIn articles continue to surface in AI responses even years after they were originally posted. Given this lasting influence, how should a brand’s social media team adjust their content calendar, and what specific article structures seem to attract the most AI citations?
It was a revelation to see that LinkedIn appeared in the top 25 domain citations for 37% of the brands analyzed, proving it has a long-tail influence that far outlasts the typical social media post. Because these articles remain relevant to AI models years after publication, social media teams need to shift their focus from “fleeting engagement” to “evergreen authority” by scheduling regular, high-quality long-form articles. The structures that attract the most citations are those that mimic a definitive guide: clear headings, data-backed insights, and comprehensive answers to niche industry problems. Instead of just posting news updates, your content calendar should prioritize these “pillar” articles that establish your experts as the primary source of truth for the AI to reference.
Conventional wisdom suggests that Wikipedia and Reddit are primary drivers of AI visibility, yet they often fail to appear in the top citations for technical B2B brands. Which niche trade publications should marketers prioritize instead, and how do you determine which titles are actually influencing AI models?
Our research debunked the “Wikipedia and Reddit” myth for B2B, as Wikipedia didn’t even make the top 25 list for 60% of brands, and Reddit was irrelevant for 83% of the brands we tracked. In the technical B2B world, AI engines seem to ignore the general noise of Reddit in favor of specialized trade publications that PR specialists might sometimes overlook. To determine which titles actually matter, you have to move beyond “perceived influence” and look at which domains are actually being cited in AI-generated answers for your specific sector. Often, a handful of niche titles drive the majority of citations, so you must audit the AI responses to see which publications are being treated as authoritative sources for your specific product categories, such as semiconductors or industrial automation.
High-performing domains often see a significant jump in visibility compared to those outside the top ten citations. What are the common characteristics of these high-visibility websites, and how can a mid-sized company compete with established enterprise giants for those limited citation spots?
The disparity is stark: brands in the top 10 cited domains average 25% visibility, while those outside that group drop to a mere 7.6%. High-visibility sites generally share a commitment to deep, structured content and a high volume of relevant technical documentation that AI models can easily ingest. For a mid-sized company to compete with an enterprise giant, the strategy shouldn’t be to outspend them, but to “out-niche” them by becoming the absolute most authoritative source on a specific, narrow topic. By focusing your GEO efforts on a specialized cluster of prompts where the giant is weak, and by ensuring your content is more detailed and better structured for AI parsing, you can secure one of those coveted top-ten citation spots even with a smaller overall footprint.
What is your forecast for B2B generative engine optimization?
I expect the landscape to shift rapidly as AI models begin to place a much higher premium on the “authority” of earned media, moving away from the current heavy reliance on owned content. While owned media currently dominates, the leaders of major AI companies are already signaling a shift toward prioritizing verified third-party journalism and expert reviews to improve the accuracy of their answers. For B2B marketers, this means the future of GEO won’t just be about what you say on your own site, but about how effectively you can secure placements in the specific, high-authority trade publications that these models are learning to trust. If you want to stay ahead, you must start building a data-driven strategy today that balances deep technical web content with a very targeted earned media approach.
