If you’re already tracking Google AI Overviews, conversational search, and LLM-driven recommendation behavior, you’ve likely discovered the old playbook for discovery is not enough.Traditional search let a category present itself as a list; AI systems increasingly compress the list into a smaller recommendation set aka a synthesized “best answer” with a few links.

That compression is why brands are asking a very specific question now: how do we show up in AI Overviews and ChatGPT results? Consistently, defensibly, and in ways that actually drive revenue? Google’s own documentation frames AI features as an experience designed to surface relevant links and help users “get to the gist” quickly, with supporting sources.

Meanwhile, consumer behavior is shifting fast enough that it’s no longer safe to treat AI discovery as a future channel. Shifts to strategy should have been made yesterday. Adobe reported that traffic from generative AI sources to U.S. retail sites rose sharply (including +4,700% YoY in July 2025) and that 38% of surveyed U.S. consumers reported using generative AI for online shopping. Reuters also reported Adobe’s finding that retail sites saw a 693.4% jump in traffic tied to AI-powered shopping assistants and chatbots during the 2025 holiday season. The numbers will vary by category, but the direction is clear: AI is becoming a material mediator of consideration.

AI-driven discovery doesn’t degrade visibility gradually. It changes whether you’re even considered at all. Immediately.

From ranking to selection

Search optimized for ordering. AI optimizes for confidence. That matters because the AI experience is not trying to present the full market; it is trying to give the user the most defensible answer it can assemble quickly, based on the signals it can interpret.

Google is explicit that AI Overviews use a Gemini model “in tandem” with existing Search systems (including ranking systems and the Knowledge Graph), and that Overviews are designed to surface information backed by top web results and include supporting links. In parallel, Google’s guidance for site owners emphasizes that the same foundational SEO best practices apply, including ensuring important content is available in text form and that structured data matches visible text. 

The practical implication: AI visibility is not “a new hack.” It’s a higher standard of interpretability. But there’s more to be done.

What AI systems reward (and what they ignore)

When brands ask why a competitor is “showing up” more often in AI answers, the explanation is almost never one reason. It’s a stack of signals that reduce uncertainty.

Clarity beats cleverness. AI systems are far more likely to cite or recommend brands that are easy to summarize without ambiguity: clear positioning, explicit use cases, transparent pricing and policies, and content that reads like it was written to be understood.

Structure creates extractability. AI can only use what it can reliably parse. Google’s guidance is blunt: make sure important content is available in textual form; ensure structured data aligns with what users see; keep key profiles (like Business Profile and Merchant Center data) accurate. These inputs determine whether AI can confidently reuse your information.

Trust is an ecosystem signal. Reviews, authoritative mentions, and cross-site consistency reduce model risk. When attributes conflict across sources, AI systems tend to dilute or exclude.

Why “good SEO” still loses in AI discovery

One of the more common failure modes we’re seeing is a brand that ranks well but fails to surface in AI answers. That happens because ranking is about relevance and authority signals at page level; AI answers are about assembling a coherent recommendation with minimal uncertainty.

A site can be “SEO sound” and still be difficult for AI to use if critical facts are scattered, locked in PDFs/images, buried behind interactive components, or missing from clear FAQ-style language. And importantly, Google notes that there are no special optimizations required specifically for AI Overviews—but it reiterates core requirements like textual accessibility and structured data alignment. In other words: the fundamentals are the strategy baseline but only now they determine inclusion, not just rank.

The compounding cost of being excluded

The opportunity cost here isn’t abstract. AI-mediated users are increasingly using these tools as a pre-decision layer, sending traffic that’s better informed and often more engaged. Adobe found AI-referred retail visitors were more engaged (longer visits, more pages, lower bounce), even while conversion dynamics evolved over time. That’s consistent with what many brands are observing anecdotally: AI can concentrate intent.

The compounding effect is where this becomes strategic. Brands that are included more often benefit from additional downstream signals (reviews, coverage, brand recall), which reinforce the very inputs models and platforms use to make future selections. Over time, the gap between “frequently selected” and “rarely mentioned” becomes structural.

The industry has started using terms like Generative Engine Optimization (GEO) to describe this shift: optimizing for visibility in generative responses, not just rankings in classic search. Academic work has formalized this framework and the idea that visibility in generative engines is measurable and optimizable, albeit in a black-box environment.

That framing matters because it helps teams stop arguing about labels and start aligning on outcomes: Are we being selected, cited, and recommended when users ask category-level questions?

But it’s also important to remember that teams can only put their best foot forward. We can influence the signals AI systems rely on; we do not control closed models and it is quite literally impossible to guarantee a specific outcome.

How Allied Global Marketing helps brands become more discoverable in AI-driven discovery

AGM supports brands across entertainment, travel, culture, and commerce as discovery behavior shifts toward AI-generated answers and recommendation experiences. Our work focuses on identifying how brands surface in AI environments today, benchmarking against competitors, and strengthening the clarity, structure, credibility, and consistency that influence selection over time across web ecosystems and the platforms consumers actually use.

If you’d like to understand how your brand currently appears in AI Overviews and conversational search, or where you’re being out-selected by competitors, get in touch with our team to learn more about what we can do for you, and how to prioritize changes that materially improve AI visibility.