Aisha Amaira is a MarTech expert who bridges the gap between complex data architectures and actionable marketing strategy. With an extensive background in CRM technology and customer data platforms, she specializes in decoding how emerging technologies like AI-driven search impact brand visibility and consumer behavior. This interview explores the fragmentation of the AI search landscape and how businesses must pivot from traditional SEO metrics to a focus on portability and utility-focused content.
Summarizing the current state of AI search, the discussion highlights that visibility is no longer a unified goal but a fragmented challenge across different platforms. The conversation covers the structural differences in how engines like ChatGPT, Perplexity, and Google AI Overviews retrieve information, the superior performance of educational content over brand assets, and the necessity of new metrics like portability and concentration to measure true digital resilience.
With only about 2% of URLs appearing across all major AI search engines simultaneously, what are the primary risks of using a single blended visibility score? How should teams adjust their reporting workflows to account for the 91% of citations that appear exclusively in one engine?
The biggest risk of a blended visibility score is that it creates a false sense of security; a brand might look like a market leader on paper while being completely invisible in two out of the three major engines. Our data shows that 91.07% of citations are exclusive to a single platform, meaning that an aggregate number effectively compresses three distinct ranking systems into a single, meaningless metric. To fix this, teams must first deconstruct their reporting to measure “Presence” by tracking the percentage of prompts where the domain appears in any engine, and then measure “Concentration” to see which specific engine is propping up their numbers. The next step is to audit specific URLs for “Portability,” identifying the tiny 2.37% sliver of content that actually bridges the gap between ChatGPT, Perplexity, and Google. Finally, workflows should shift toward platform-specific optimization, treating each engine as a unique distribution channel with its own logic rather than a singular “AI search” monolith.
Explanatory guides and tutorials often see double the cross-engine overlap compared to brand homepages or product pages. What specific content shifts are necessary to improve this portability, and how do you balance creating utility-focused content with the need to drive brand conversions?
To improve portability, brands must shift from being “the official source” to being “the useful source,” as guides and tutorials show a 2.3% universal overlap compared to a meager 1.1% for homepages. This shift requires moving away from self-centered brand copy toward structured, explanatory content that teaches or compares, as these formats align better with the retrieval logic of AI models. Balancing utility with conversion is a matter of strategic placement; you use high-portability assets like “how-to” guides to capture the citation, then weave in brand-specific conversion triggers naturally within that helpful framework. For example, a product page might only have a 1.2% overlap across engines, so the goal isn’t to force that page to rank, but to create a tutorial that references the product, ensuring the brand stays in the conversation even if the transaction page doesn’t.
Despite expectations, commercial prompts for specific products do not show significantly higher engine consensus than informational queries. Why does engine-specific retrieval logic remain so dominant in high-intent searches, and what practical steps can marketers take to audit their performance across these disjointed pools?
It is surprising to many that commercial prompts only show a 2.4% universal overlap, barely higher than the 2.0% seen in informational queries, despite the narrower pool of “best” products. This happens because each engine uses its own proprietary logic to determine trust, format preference, and source reliability, meaning Perplexity might prioritize a news site while ChatGPT favors a blog for the exact same “best CRM” prompt. Marketers need to stop assuming that high-intent queries naturally consolidate; instead, they should conduct side-by-side audits by running their top 100 commercial prompts through all three engines simultaneously. By using regex-based classification to see where they are winning and losing, teams can identify if their lack of presence is a site-wide issue or if they are simply failing to meet the specific “trust” criteria of a dominant engine like Google AI Overviews.
Large domains often show high visibility in one engine but lack portability across others, making them vulnerable to platform-specific shifts. How can a brand determine if its current presence is resilient or merely a platform habit, and what metrics best define true authority in this fragmented landscape?
Resilience is defined by portability, not just volume; look at Wikipedia, which has over 16,000 citations in our dataset but only a 1.3% universal overlap rate. A brand can determine if it is a “platform habit” by calculating the percentage of its citations that are exclusive to one engine; if that number is nearing 90% or 100%, your visibility is at the mercy of a single algorithm update. True authority in this landscape is measured by “Resilient Presence,” which we define as the percentage of your total cited URLs that appear in all three engines. If your portability is low, even with high citation counts, you aren’t an authority in the eyes of the AI ecosystem—you are just a favorite source for one specific crawler.
Moving away from aggregate scores involves measuring presence, portability, and concentration. How do you identify which specific AI engine to prioritize when data shows high concentration in only one, and what does a transition plan look like for a brand currently invisible in most engines?
Prioritization should be driven by where your specific audience is currently searching, but if you are concentrated in only one engine, you are effectively standing on a one-legged stool. A transition plan for an invisible brand begins with a “Utility Audit,” where you move away from brand-heavy landing pages and start publishing content that mimics the 2.3% overlap success of guides and tutorials. You then track “Presence” across all engines to see where the first signs of life appear, and once you establish a foothold in one, you analyze the citations of competitors who are portable to see what metadata or formatting they use. The final stage is a diversification of content formats, moving from 4.1 million potential URL types down to the specific structures—like articles and blogs which have a 1.8% overlap—that the engines are statistically more likely to agree upon.
What is your forecast for AI search engine visibility?
I expect the “Consensus Gap” to remain a structural reality rather than a temporary glitch, even as we saw a tiny increase in overlap from 2.2% in Q3 2025 to 2.7% in Q1 2026. My forecast is that we will move toward a “tri-polar” search strategy where the most successful brands stop trying to rank “everywhere” with one page and instead build specialized content clusters tailored to the distinct retrieval behaviors of each engine. We will see the death of the “Global AEO Score” as sophisticated marketing teams realize that being useful to an AI’s logic is more valuable than being the official brand of record. For our readers, my advice is to stop obsessing over your average rank and start measuring how many of your URLs actually travel across platforms—because if your content isn’t portable, it isn’t truly visible.