What is GenAI?

AISEO

Dec 7

Understanding How AI Is Rewriting Brand Discovery

In 2023, the launch of ChatGPT marked a turning point in how people find information.

Traditional search engines such as Google once shaped nearly every step of the discovery journey. Today, we are entering a new era, one defined by Generative AI search—or GenAI search—where people increasingly turn to AI assistants rather than traditional search bars.

This shift is not theoretical. It is unfolding in real time.

According to McKinsey, nearly half of consumers already use generative AI for product research or recommendations. By 2028, McKinsey predicts that $750 billion will “funnel through AI-powered search.” Changes in consumer behavior are already visible. Gartner projects that traditional search engine volume will drop 25% by 2026 as users adopt AI assistants as primary research tools.

GenAI search is transforming not only where consumers look, but how decisions are formed. For brands, this requires a new understanding of how large language models (LLMs) retrieve, rank, and narrate information across the web.

Search is becoming conversational, contextual, and generative. As Deloitte recently wrote in the WSJ’s CMO Journal: “Over time, brand preference could move from the consumer to the machine, established before consumers even enter the funnel.”

What Exactly Is GenAI Search?

GenAI search refers to the use of generative AI systems—such as ChatGPT, Gemini, Claude, and Perplexity—to help users discover, compare, and understand information. Unlike traditional search engines that provide lists of links, GenAI tools deliver:

Conversational answers
Editorial-style comparisons
Personalized recommendations
Synthesized narratives

For example, when a user asks:

“What are the best clean perfumes?”

Instead of returning a list of websites, a GenAI assistant might offer:

Curated product recommendations
Key differentiators across brands
Insights extracted from reviews and editorial content
Context such as price, sourcing, or formulation philosophy

This is search as interpretation—not retrieval.

A Mastercard survey on holiday shopping found that half of shoppers trusted intelligent assistants to “deliver unique and thoughtful recommendations,” particularly for discovering new products, confirming best deals, and summarizing thousands of reviews.

This marks a profound evolution: GenAI search is an interpretive system, not a retrieval system.

Why Consumers Are Switching to AI Assistants

Consumers are moving toward AI-driven search for several reasons.

Convenience: GenAI reduces cognitive load. Rather than skimming dozens of reviews or links, users receive immediate, synthesized answers.
Personalization: LLMs tailor recommendations to preferences. Users can ask for options “under $100,” “similar to X,” or “with clean ingredients.”
Simplification: LLMs increasingly serve as the first point of contact between users and the internet, filtering information into clarity.
Trust by fluency: Consumers often treat AI-generated responses as authoritative because the answers feel cohesive, confident, and easy to interpret.

As these behaviors scale, AI-powered search becomes a new center of gravity in digital discovery.

How GenAI Search Works (AIO Explanation)

Component	How It Works
Retrieval Systems	LLMs pull from two sources: their trained knowledge base and real-time web retrieval for updated facts, reviews, and press coverage. If a brand’s information is sparse, outdated, or inconsistent, the model cannot assemble a reliable picture.
Ranking Mechanisms	When generating recommendations, LLMs evaluate clear category definitions, consistent language across surfaces, repeated authority signals, and consensus across multiple sources. This resembles SEO but operates at a narrative level rather than keyword density.
Narrative Synthesis	Unlike search engines that retrieve pages, LLMs synthesize patterns into coherent narratives. [A study of 18,000 chat queries ][1]found that GPT responses rely on synthesized understanding rather than direct web retrieval. Clear, consistent brand stories are surfaced; fragmented stories are often omitted.

Structured vs. Unstructured Data in AI Search

GenAI search relies on two broad categories of information. Clear, consistent messaging becomes the scaffolding AI uses to understand and represent your brand.

Types of Data in AI Search	What It Does	Examples
Structured Data	Structured data helps AI identify factual details about a brand and retrieve key attributes consistently.	Metadata Product details Pricing Ingredients Location Technical specs
Unstructured Data	Unstructured data shapes the narrative—who you are, what you stand for, and how AI interprets your brand.	Articles Interviews Reviews About pages Blogs Press coverage

The Rise of AI Recommendation Engines

GenAI search does more than answer questions; it influences preferences and curates choices before users even visit a website.

Consumers now ask GenAI:

“What's the best skincare routine for oily skin?”
“Which dental studio should I choose in New York?”
“What whiskey under $100 tastes like a premium bottle?”

These queries turn AI systems into tastemakers, not simply search utilities. Increasingly, shoppers rely on chatbots for holiday shopping, trend discovery, and comparing specific products like winter boots.

This shifts the competitive landscape:
Brands are no longer competing for clicks. They are competing for inclusion in AI-generated answers.

What Determines Whether a Brand Shows Up in GenAI Search?

Factors in AI Search	Why It Matters
Narrative Clarity	LLMs surface brands they can summarize clearly in one or two sentences. Ambiguous or overly broad positioning makes retrieval harder and reduces visibility.
Consistency Across Surfaces	Conflicting details across websites, product pages, press releases, and social media reduce trust signals. LLMs prioritize brands with stable, reliable information.
Authority Signals	Press coverage, awards, expert mentions, interviews, and third-party validation strengthen a brand’s credibility. LLMs use these signals as proxies for trustworthiness and expertise.
Structured Data Hygiene	Clean, complete metadata (pricing, ingredients, services, location, specs) helps LLMs retrieve factual information quickly and reduces hallucinations or omissions.
Category Fit	Brands that map cleanly to recognized categories and subcategories are more likely to appear in recommendation queries such as “best X” or “top Y for Z.”
Social Proof	Customer reviews, sentiment patterns, and recurring themes in feedback help LLMs infer perceived quality, reliability, and user experience.
Differentiation	Brands with indistinguishable messaging blend into the noise. Clear, differentiated positioning makes it easier for AI systems to understand and repeat what makes the brand distinct.

Why GenAI Search Matters for Brands

GenAI search is not a passing trend; it is a structural shift in discovery.

For brands, this means:

AI recommendations are the new SEO page one
AI summaries act as mini editorials about your brand
Each AI output becomes a micro reputation moment

In other words, GenAI represents a new organic channel, and it’s already leading to real conversionsAn analysis of more than 140,000 ChatGPT conversations found that “over one in five (21.6%) of ChatGPT interactions now demonstrate some degree of commercial intent.”

If a brand fails to appear in AI-generated answers, it risks losing entire customer journeys.

How Brands Can Prepare for GenAI Search

Action	Why It Matters
Invest in a Clear Brand Narrative	LLMs repeat what they can understand and summarize easily. A clear narrative becomes the foundation of how AI articulates your brand in recommendations and category explainers.
Practice Narrative Governance	Consistency across websites, press coverage, product descriptions, metadata, and interviews strengthens trust signals and reduces contradictory or outdated information that can confuse AI systems.
Build Authority Signals	Press coverage, expert commentary, awards, and thought leadership help AI models assess credibility. Authority signals act as reinforcement that a brand is trusted and recognized within its category.
Use AI-Ready Content Strategy	Writing with clarity, specificity, and structure improves AI comprehension. Content that is easy for humans to skim is also easier for AI to interpret and summarize without distortion.
Conduct AI Visibility Audits	Regularly testing how major models (ChatGPT, Gemini, Claude, Perplexity) describe your brand reveals inconsistencies, gaps, or unwanted narratives before they affect recommendations.
Map Category Positioning	Understanding how AI interprets your competitive landscape helps identify where your brand fits—and where it may need stronger differentiation or clearer category cues to appear in “best of” or “recommended for” queries.

GenAI search represents a fundamental shift in how people discover brands. Instead of scanning multiple websites, users receive curated, narrative-driven answers from AI systems that interpret brand identity, authority, and value.

For brands, this requires more than traditional SEO or content marketing. It requires structured storytelling, authority-building, and strategic AI visibility.

Search is no longer about questions; it is about answers.

The question is no longer whether brands should adapt, but how quickly they can move.