I Used 20 Years of Brand Experience to Re-Understand AI Search
Last year, I seriously used an AI search engine for the first time to search for a chain brand, wanting to see how it would answer "How is this brand?"
The result surprised me.
AI did not give me a bunch of links to look through myself. It directly said: "The main characteristics of this brand are... The core user group is... Compared with similar brands..."
At that moment, I realized one thing: This is not a search engine; it is a consultant making judgments on behalf of the user.
I have been in branding for 22 years, and for the first time, I felt a bit uneasy.
1. Old Understanding: Search Is a Traffic Gateway
Having worked in branding for 20 years, my understanding of search was this:
- User has a need → Opens Baidu/Google → Types keywords → Sees a page of results → Clicks through → Becomes your traffic
So we did SEO, keywords, and content, all to get our links into search results so users would click through. Search engines were traffic distribution channels — your job was to secure a position in that channel.
This logic dominated brand digital operations for nearly 20 years. Then AI search arrived.
2. New Understanding: AI Search Is the Arbiter of Perception
The logic of AI search is completely different:
- User has a question → Asks AI → AI synthesizes all the information it has seen → Gives one answer, including brand recommendations
The user does not have to judge for themselves; AI does it for them.
💡 What Is the Essence of This Difference?
Traditional search: "Here are 10 brands, take a look yourself."
AI search: "Based on your needs, I recommend this brand, because..."
Traditional search gives choice. AI search gives recommendations. User trust in "recommendations" is far higher than in "link lists."
This means: if your brand appears in AI search recommendations, what you gain is not traffic, but cognitive-level endorsement. And if you do not appear, the user did not just fail to see you — AI told them "you are not among the recommendations."
After realizing this difference, I re-examined what I had been doing for 20 years and found a major blind spot.
3. The Blind Spot of Traditional Brand Experience in the AI Search Era
The core logic of traditional brand building is: make users remember you.
You run ads, campaigns, and content, all aimed at making target users think of your brand when they have a need. This is "user memory"-driven brand building.
In the AI search era, a new dimension has been added: make AI understand you.
When AI recommends brands to users, it recommends brands it "knows" — those with clear, consistent, and credible descriptions across multiple authoritative sources. If AI "does not know enough" about your brand, it would rather not mention you than make a blind recommendation.
📊 A Comparison That Left a Deep Impression on Me
Two chain restaurant brands, similar in scale (both 50-80 locations), with comparable market awareness.
Brand A: High awareness among users (good word-of-mouth among friends), but the official website information is rudimentary, no Baidu Baike entry, almost no coverage in industry media, and content is mainly promotional.
Brand B: Slightly lower user awareness, but the official website content is clear and complete, has a Baidu Baike entry, has been cited in multiple restaurant industry articles, and the WeChat Official Account content has clear brand positioning narratives.
When searching "chain restaurants suitable for family dinners in this city" via AI search — Brand B was recommended, while Brand A barely appeared.
AI was not recommending "the better brand"; it was recommending "the brand it knew better." Brand A had awareness in user minds, but not in AIs database.
This made me realize: the "make users remember you" approach I had practiced for 20 years needs an additional layer in the AI search era — "make AI able to describe you."
4. Reassessing 20 Years of Brand Experience in the AI Search Era
After realizing this, I started re-examining which of my experiences still hold value and which need updating.
| Traditional Brand Experience | Value in AI Search Era | Direction of Change |
|---|---|---|
| Clear brand positioning | ✅ Value amplified | Clear brand positioning is most needed when AI recommends; brands with vague positioning cannot be articulated by AI |
| Differentiated brand story | ✅ Value amplified | AI citations need content with specific facts and unique angles, which is exactly the core of brand narrative |
| User insights | ✅ Value amplified | Only by knowing what questions users are asking can you create content AI is willing to cite |
| Keyword density optimization | ❌ Essentially ineffective | AI does not look at keyword density; it looks at overall content credibility and authoritativeness |
| Mass low-quality content production | ❌ Ineffective or even counterproductive | Low-quality content lowers the brand overall credibility in AIs database |
| Brand media exposure | ✅ Value enhanced | Media coverage is an authoritative source recognized by AI, carrying more weight than self-published content |
| Promotion-focused content | ⚠️ Needs adjustment | AI will not cite promotional content; the proportion of "brand positioning" content needs to increase |
This table made me realize: The core value of 20 years of brand experience has not become obsolete, but the application scenarios have changed. Brand positioning, brand stories, user insights — these fundamental brand capabilities are even more valuable in the AI search era — but they need to be expressed in new ways that AI can "understand."
5. New Dimensions of Brand Building in the AI Search Era
Based on these insights, I summarized three new dimensions that brand building needs in the AI search era:
It is not just about letting users see your content, but giving AI a clear "structured understanding of you" in its database. Official website, encyclopedias, industry media coverage, professional platform profiles — these together form the foundation for AI to know you.
Dimension 2: Question-Driven Content Creation
It is not "what content I want to publish," but "what questions users will ask in AI search, and which ones I can answer." The starting point of every piece of content is a real user question scenario.
Dimension 3: Continuous AI Search Monitoring
Traditional brands can sense brand effectiveness through sales data, but the AI search era requires directly monitoring "brand appearance rate in AI search" — because this is a leading indicator that affects user decisions, reflecting problems 1-3 months earlier than sales data.
22 years has let me witness the rise and fall of many brands. Those that can survive cycles all share one trait: they know who they are, can articulate it clearly, and maintain consistency at every touchpoint.
In the AI search era, the difficulty of this has not decreased — but its value has been amplified.
If you want to know what your brand looks like in AI search, run a diagnostic with Chengying Ruanbo — it is the fastest way. The moment you see the results, you will feel what I felt then — a bit shaken, but finally knowing where to start.
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