注：本文为 “零点击时代 | AI 驱动生态” 相关合辑。
英文引文，机翻未校。
如有内容异常，请看原文。

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From Click to Citation: Part 1 - Practical Research-informed Technical Strategies for AI Visibility

从点击到引用：第一部分——基于研究的 AI 曝光度实操技术策略

Dr. Robert Li
28 Sep 2025

TL;DR

核心要点速览

• Users click AI citations at 1% vs 15% for traditional search
  用户点击 AI 引用链接的比例仅为 1%，而传统搜索引擎的该比例为 15%
• Only 7% of sources appear across all AI platforms, while 71% appear on just one. Platform-specific strategies now essential
  仅有 7%的信息源出现在所有 AI 平台，71%的信息源仅在单一平台展示。制定平台专属策略已成必要举措
• Two-stage user journey: Stage 1 prioritizes UGC for discovery; Stage 2 uses official sources for validation
  两阶段用户行为路径：第一阶段通过用户生成内容（UGC）完成信息发现，第二阶段借助官方信息源进行信息验证
• Practical Guidance (29%) and Writing (24%) dominate AI usage, with most interactions never leaving AI platforms
  实用指导类（29%）和写作类（24%）需求占据 AI 使用场景主流，且大部分交互全程在 AI 平台内完成
• Adobe shows AI-referred visitors browse 12% more pages but convert 9% less—requiring new conversion strategies
  奥多比（Adobe）数据显示，AI 引流的访客页面浏览量高出 12%，但转化量低 9%，这要求企业制定全新的转化策略

Let’s acknowledge the elephant in the room. My previous article that discussed the research on this was a monstrous academic meta-analysis that, while comprehensive, proved impenetrable for most digital professionals who need actionable insights today.
我们不妨直面一个显而易见的问题：我此前就该主题撰写的研究文章是一篇篇幅庞大的学术元分析，尽管内容全面，却让当下需要可落地见解的大多数数字行业从业者难以理解。

Let’s strips away the theoretical complexity to focus on what actually matters: how the shift from search to AI affects your website traffic, what you can do about it, and why traditional metrics might be lying to you.
本文将摒弃复杂的理论，聚焦核心问题：从搜索引擎到 AI 的转变如何影响你的网站流量，你该采取何种应对措施，以及为何传统数据指标可能存在误导性。

This will be Part 1 of a 2 part series with this first article focusing on technical AISEO/GEO (whatever you wish to call it) strategies, and part 2 focusing on organizational adaptation strategies.
本文为系列文章的第一部分，将聚焦 AISEO/GEO（可按需命名）的技术策略，第二部分则会探讨企业组织的适配策略。

So, we already know the implications of the research into this area are profound. Your response needs to start now.
相关领域的研究已揭示出这一转变的深远影响，因此，你需要立即采取应对行动。

The New Zero-Click Internet

全新的零点击互联网时代

Here’s what’s happening right now: more than 10% of the global population is using one or more AI platforms weekly (Chatterji et al., 2025), and the greatest concentration of this use is in first world countries, so that concentration is higher where you and I live. When they ask questions, the AI provides answers with citations to sources. Users read the AI’s response and move on. They don’t click through. This is the fundamental atomic moment that is driving the new zero-click Internet (Pew Research Center, 2025).
当前的现状是：全球超 10%的人口每周使用至少一个 AI 平台（查特吉等人，2025），且该行为主要集中在发达国家，因此在我们所处的地区，AI 平台的使用率会更高。用户在 AI 平台提出问题后，AI 会给出附带信息源引用的答案，用户阅读答案后便会离开，不会点击跳转至原信息源。这一核心行为，正推动着全新的零点击互联网时代的到来（皮尤研究中心，2025）。

The numbers tell the story:
数据直观地印证了这一趋势：

• Traditional search averages a 15% click on any organic result (Pew Research Center, 2025)
  传统搜索引擎中，自然搜索结果的平均点击比例为 15%（皮尤研究中心，2025）
• Google’s Position 1 gets the majority of this, at a 39.8% click-through rate (First Page Sage, 2025)
  谷歌搜索结果首位的链接占据了大部分点击量，点击率达 39.8%（First Page Sage，2025）
• AI platform citations? A 1% click-through rate (Pew Research Center, 2025)
  而 AI 平台引用链接的点击率，仅为 1%（皮尤研究中心，2025）

Mail Online discovered this the hard way. Despite maintaining top search rankings and regularly appearing in AI citations, their daily traffic dropped from 6,000 to 100 clicks—a 98% reduction (Mediaweek, 2025).
《每日邮报》网络版便为此付出了惨痛代价。尽管其在搜索引擎中始终保持高位排名，且频繁出现在 AI 平台的引用中，但其日点击量从 6000 次锐减至 100 次，降幅达 98%（《媒体周刊》，2025）。

This pattern repeats across industries. Publishers report less than 1% of total website traffic comes from AI platform referrals (TollBit, 2024). The “authority-traffic paradox” means your content gains credibility and influence without generating website visits (SEMRush, 2025).
这一趋势在各行业均有体现。出版商表示，来自 AI 平台的引流占网站总流量的比例不足 1%（TollBit，2024）。这种“权威-流量悖论”意味着，你的内容虽能获得公信力和影响力，却无法为网站带来访问量（SEMRush，2025）。

AI Visibility Depends on Which Platform Users Choose

AI 曝光度取决于用户选择的平台

Each AI platform plays favorites differently, and the overlap is minimal:
各 AI 平台的信息源偏好存在显著差异，且平台间的信息源重合度极低：

ChatGPT loves Reddit (appearing in 141% of prompts—yes, more than once per query) and Wikipedia (152%) according to SEMRush (2025). The platform prioritizes community discussions and user-generated content over corporate websites.
据 SEMRush（2025）数据显示，ChatGPT 偏爱引用红迪网（Reddit）和维基百科（Wikipedia）的内容，前者在 141%的查询请求中被提及（即单次查询中可能被多次引用），后者的提及比例为 152%。该平台相较于企业官网，更重视社区讨论内容和用户生成内容。

Gemini sticks closest to traditional Google rankings with the lowest source diversity. If you rank well in Google Search, you have better odds here—but the citation click-through remains minimal.
Gemini 与谷歌传统搜索排名的契合度最高，信息源的多样性最低。若你的内容在谷歌搜索中排名靠前，那么在该平台获得引用的概率也会更高，但引用链接的点击率依然极低。

Perplexity emphasizes research-backed content and academic sources. The platform shows citations more prominently, achieving 3-5% click-through rates—still low, but that is still 3-5x better than ChatGPT.
Perplexity 侧重引用有研究支撑的内容和学术信息源，其引用展示形式更为醒目，点击率达 3%-5%。尽管该比例依然偏低，但已是 ChatGPT 的 3-5 倍。

Claude focuses on authoritative, comprehensive content with strong documentation. Professional users dominate here, creating different citation patterns than consumer-focused platforms.
Claude 专注于引用权威、全面且配套文档完善的内容，其用户以专业人士为主，因此形成了与面向普通消费者的 AI 平台截然不同的引用模式。

Only 46 sources (7% of the 467 studied) appear across all four platforms (SEMRush, 2025). These universal sources—Google, Wikipedia, YouTube—already dominate traditional search. For every other website, 71% of sources appear on only ONE platform.
仅有 46 个信息源（占研究样本 467 个的 7%）出现在上述四个平台中（SEMRush，2025）。这些通用信息源包括谷歌、维基百科、油管（YouTube），它们在传统搜索引擎中本就占据主导地位。而其余 71%的信息源，仅出现在单一 AI 平台。

What this means: Your AI optimization strategy can’t be platform-agnostic. You need to know your brand, your audience and their behaviors and, most importantly, you need to know where you can win, and where you won’t win.
这意味着：你的 AI 优化策略不能脱离具体平台。你需要了解自身品牌、目标受众及其行为特征，更重要的是，要明确自身在哪些平台能够获得优势，在哪些平台难以突围。

The Two-Stage AI-assisted Decision Architecture

人工智能辅助的两阶段决策架构

Forget everything you know about the traditional search-based customer journey. In an AI mediated environment, users don’t follow typical browse and compare behaviors. AI users follow a different path, a different discovery architecture, and understanding it determines whether your content gets cited at all.
请抛开你对传统搜索引擎下用户消费路径的所有认知。在 AI 介导的环境中，用户不再遵循典型的浏览、对比行为。AI 用户有着截然不同的行为路径和信息发现架构，能否理解这一架构，决定了你的内容能否被 AI 平台引用。

Stage 1: Discovery Through Community Sentiment

第一阶段：通过社区舆论完成信息发现

When users ask “What’s the best project management software for small teams?”, AI platforms don’t immediately cite official websites. Instead, they synthesize discussions from Reddit threads, discussions on X, Quora answers, replies on specialized forums.
当用户提出“小型团队适用的最佳项目管理软件有哪些？”这类问题时，AI 平台不会直接引用企业官网内容，而是会整合红迪网、X 平台、奎拉（Quora）及各专业论坛中的相关讨论和回答。

User-generated content (UGC) dominates this stage because AI platforms have been engineered to treat community consensus as a proxy for real-world validation. A product mentioned positively across multiple UGC forums carries more weight than any direct brand or corporate PR or marketing claim.
用户生成内容（UGC）在这一阶段占据主导地位，因为 AI 平台的算法设计将社区共识视为现实认可度的参考标准。一款产品若在多个用户生成内容平台获得正面评价，其影响力远大于品牌方的直接宣传或企业公关、营销话术。

This explains why officially published content is about half as effective at getting cited as UGC. Polished product pages and carefully crafted blog posts lose to authentic user discussions.
这也解释了为何官方发布的内容被引用的概率仅为用户生成内容的一半。制作精良的产品页面和精心撰写的博客文章，在真实的用户讨论面前往往缺乏竞争力。

Stage 2: Validation Through Official Sources

第二阶段：通过官方信息源完成信息验证

Only after users narrow their choices through back and forth conversational interaction do they seek specific information: “Does Notion have Gantt charts?” or “What does Asana cost for 10 users?”
只有当用户通过与 AI 的反复对话缩小选择范围后，才会进一步查询具体信息，例如“Notion 是否支持甘特图功能？”或“Asana 针对 10 人团队的定价是多少？”。

It is now, when highly specific information is required, that AI platforms cite official websites, documentation, and pricing pages. But, still, users might never click through. The AI agent extracts the information it needs and presents the information directly.
只有在用户需要这类高度具体的信息时，AI 平台才会引用企业官网、官方文档和定价页面的内容。但即便如此，用户仍可能不会点击跳转——AI 智能体会直接提取所需信息并呈现给用户。

This two-stage architecture creates the “mention-source divide.” Brands frequently discussed in communities (high mentions) might never appear as authoritative sources (low citations). Conversely, brands with comprehensive documentation might be cited without being recommended. And this varies wildly depending on the industry vertical.
这一两阶段架构催生了“提及-来源鸿沟”：在社区中被频繁讨论的品牌（高提及度），可能从未作为权威信息源被引用（低引用度）；反之，拥有完善官方文档的品牌，可能被 AI 平台引用，却未获得推荐。且这一现象在不同行业中的表现存在显著差异。

SEMRush’s (2025) analysis reveals:
SEMRush（2025）的分析结果显示：

• Finance: 22-27 brands achieve both high mentions and citations (institutional authority matters)
  金融行业：有 22-27 个品牌同时拥有高提及度和高引用度（机构权威性至关重要）
• Fashion: Only 3 brands achieve both (community opinion dominates)
  时尚行业：仅有 3 个品牌同时拥有高提及度和高引用度（社区舆论占据主导）
• Business Services: 12-22 brands achieve both (mixed signals)
  商业服务行业：有 12-22 个品牌同时拥有高提及度和高引用度（影响因素兼具多元性）

Why Users Don’t Click (And When They Do)

用户为何不点击引用链接（以及点击的场景）

The data from 700 million ChatGPT users (Chatterji et al., 2025) reveals something crucial: only 24% of interactions involve “seeking information”—the behavior most like traditional search. The rest breaks down as:
来自 7 亿 ChatGPT 用户的数据（查特吉等人，2025）揭示了一个关键事实：仅有 24%的用户交互属于“信息检索”行为（与传统搜索引擎的使用行为最相似），其余交互场景的分布如下：

• Practical Guidance (29%): “How do I…” questions where users want step-by-step instructions
  实用指导类（29%）：用户提出“如何做……”类问题，需要分步操作指导
• Writing (24%): Creating and editing content directly in the AI
  写作类（24%）：用户直接在 AI 平台创作、编辑内容
• Asking (49% of all interactions): Seeking advice and recommendations
  咨询类（占总交互的 49%）：用户寻求建议和推荐
• Doing (40% of all interactions): Task completion within the platform
  实操类（占总交互的 40%）：用户在 AI 平台内完成具体任务

Users rate “Asking” interactions highest for satisfaction. They actually prefer the AI’s synthesized advice over clicking through to multiple sources. This behavior intensifies for non-critical information where a user feels that clicking through citations to validate the information is seen as unnecessary.
用户对“咨询类”交互的满意度最高，相较于点击多个信息源自行查找，他们更倾向于接受 AI 整合后的建议。对于非关键信息，用户的这一行为表现得更为明显——他们认为无需点击引用链接验证信息。

The interface matters enormously. Perplexity’s prominent citation numbering achieves 3-5% click-through. ChatGPT’s subtle citation formatting stays around 1%. This 3-5x difference suggests optimization opportunities (Arc Intermedia, 2025).
平台界面设计对点击率的影响极大。Perplexity 平台醒目的引用编号设计使其点击率达到 3%-5%，而 ChatGPT 低调的引用格式使其点击率仅维持在 1%左右。这 3-5 倍的差距，意味着界面优化存在较大空间（Arc Intermedia，2025）。

All that being said, though, this isn’t a static number. Adobe’s (2025) research does offer future hope: citation click-through improved from October 2024 to February 2025 as platforms matured and users grew familiar with interfaces.
但需要说明的是，点击率并非固定不变的数值。奥多比（2025）的研究为未来带来了希望：2024 年 10 月至 2025 年 2 月，随着 AI 平台的不断成熟和用户对界面的逐渐熟悉，引用链接的点击率出现了提升。

We’re still talking about improvements from 1% to perhaps 2-3%—not a return to traditional search patterns-but this may continue to improve as more users master the interface and, who knows, maybe these platforms will increase the prominence of citations.
目前来看，点击率的提升仅从 1%增至 2%-3%，并未恢复到传统搜索引擎的水平，但随着更多用户熟悉平台界面，这一比例可能会持续上升。此外，各 AI 平台也可能会进一步突出引用链接的展示位置。

What This Means for You

这对你的启示

The Authority-Traffic Paradox in Practice

现实中的权威-流量悖论

What this means for you and your content is that it can now gain massive authority without generating proportional traffic. This hits content-focused websites hardest—news publishers, blogs, educational resources—where the entire business model assumes traffic leads to revenue.
这对你和你的内容而言，意味着内容可获得极高的权威性，却无法带来相应的流量。这对以内容为核心的网站（如新闻出版商、博客、教育资源平台）冲击最大，因为这类网站的商业模式完全建立在“流量转化为收益”的基础上。

The paradox further intensifies for non-critical content. Users won’t validate restaurant recommendations, product reviews, or how-to guides. They will, however, check official sources for pricing, specifications, health, financial or regulatory information.
对于非关键内容，这一悖论表现得更为突出。用户不会去验证餐厅推荐、产品评测或操作指南类内容的真实性，但会主动查阅官方信息源，获取定价、产品规格、健康、金融或监管相关信息。

The Quality Visitor Contradiction

高质量访客的矛盾性

When users do click through from AI citations, they also behave differently (Adobe Digital Insights, 2025):
当用户确实从 AI 引用链接跳转至你的网站时，其行为特征也与传统引流的访客存在差异（奥多比数字洞察，2025）：

• Browse 12% more pages (6.2 vs 5.5 for search)
  页面浏览量高出 12%（AI 引流访客平均浏览 6.2 个页面，搜索引擎引流访客为 5.5 个）
• Spend 41% longer on site
  网站停留时间延长 41%
• Show 23% lower bounce rates
  跳出率降低 23%
• Paradoxically, they convert 9% less on average
  但矛盾的是，平均转化量低 9%

These visitors arrive pre-qualified, having already consumed comprehensive information. They need validation, not education and this might be why conversion rates appear so paradoxical, as the long held assumption is that a visitor is higher in the awareness funnel than they already are. So traditional conversion optimization—simplified forms, CTAs, squeeze pages—might actually work against you.
这些 AI 引流的访客是经过前期筛选的，他们在跳转前已获取了全面的相关信息，其需求是“信息验证”，而非“信息了解”。这或许是转化量看似矛盾的原因——企业一直默认访客处于认知漏斗的较高阶段，而实际情况并非如此。因此，传统的转化优化手段（如简化表单、行动号召按钮、挤压式落地页），反而可能产生反效果。

However, again, this varies wildly depending on the industry vertical.
当然，这一现象在不同行业中仍存在显著差异。

Fashion and retail see particularly lower conversion from AI platform derived visitors despite higher engagement.
时尚和零售行业中，AI 引流访客的互动度虽高，但转化量尤为偏低。

But travel sites see AI-referred visitors generate 80% more revenue per visit. Financial services also show 18% higher conversion rates (Adobe Digital Insights, 2025). This is likely due to the traditional design cues that travel and finance websites exhibit such as up front travel searches, and financial calculators (and financial services also have a longer conversion funnel, and more critical information requirements).
而旅游类网站中，AI 引流访客的单客收益高出 80%，金融服务行业的 AI 引流访客转化量也高出 18%（奥多比数字洞察，2025）。这可能是因为旅游和金融类网站保留了传统的设计特征，例如首页的旅游搜索框、金融计算器等；此外，金融服务行业的转化漏斗更长，且用户对信息的严谨性要求更高。

The conclusion then, is optimization for AI visibility is highly dependant upon industry dynamics, and, therefore, expertise.
由此可得出结论：AI 曝光度优化高度依赖行业发展态势，也因此需要专业的行业知识支撑。

Platform Concentration Creates New Gatekeepers

平台集中度催生新的流量守门人

With 71% of sources appearing on only one platform, your visibility depends entirely on which platforms your audience uses. A dominant position on ChatGPT means nothing if your customers prefer Claude.
由于 71%的信息源仅出现在单一平台，你的内容曝光度完全取决于目标受众使用的平台。若你的客户更倾向于使用 Claude，那么你在 ChatGPT 上的领先地位毫无意义。

This concentration varies by platform:
不同平台的信息源集中度存在差异：

• ChatGPT: Most democratic distribution but lowest click-through
  ChatGPT：信息源分布最具普惠性，但点击率最低
• Gemini: Highest concentration, favors existing Google winners
  Gemini：信息源集中度最高，偏向谷歌搜索中已占据优势的信息源
• Perplexity: Moderate concentration with better click-through
  Perplexity：信息源集中度适中，点击率相对较高
• Claude: Professional focus with different citation patterns
  Claude：聚焦专业领域，拥有独特的引用模式

You can’t optimize for all platforms equally as the strategies often conflict.
你无法对所有平台进行同等力度的优化，因为各平台的优化策略往往存在冲突。

Practical Technical Strategies That Work

可落地的实操技术策略

1. Master the Two-Stage Decision Architecture

1. 掌握两阶段决策架构

For Stage 1 Discovery:
针对第一阶段（信息发现）的策略：

• Participate authentically in appropriate UGC discussion forums about your industry
  以真实身份参与所属行业相关的用户生成内容讨论论坛
• Answer questions without promotional content
  解答问题时避免植入推广内容
• Engage in specialized forums where your customers gather
  积极参与目标客户聚集的专业论坛
• Create content that users want to naturally discuss and share
  创作用户愿意自发讨论和分享的内容

Patagonia achieved a 21.96% Share of Voice in fashion not through SEO but through consistent presence in sustainability discussions (SEMRush, 2025). Their community engagement happened organically because their staff participated as community members, not marketers.
巴塔哥尼亚（Patagonia）在时尚行业的声量占比达到 21.96%，这并非依靠搜索引擎优化，而是通过持续参与可持续发展相关讨论实现的（SEMRush，2025）。其社区运营之所以能自然推进，是因为员工以社区成员的身份参与互动，而非以营销人员的身份进行推广。

For Stage 2 Validation:
针对第二阶段（信息验证）的策略：

• Maintain comprehensive, current documentation
  维护内容全面、实时更新的官方文档
• Structure data with clear schema markup
  通过清晰的模式标记实现数据结构化
• Provide transparent pricing and specifications
  公示透明的定价和产品规格信息
• Ensure technical details are easily extractable
  确保技术细节易于被 AI 智能体提取

The brands that win both stages develop dual content strategies.
能在两个阶段均占据优势的品牌，都制定了双轨制内容策略。

In this stage, Notion succeeded by maintaining vibrant community discussions while providing detailed technical documentation on how to use their service that was easily accessible and consumable by autonomous/bot/AI agent traffic.
Notion 便是典型案例，其在维持活跃的社区讨论的同时，还提供了详尽的产品使用技术文档，且该文档易于被自动程序、机器人和 AI 智能体抓取和解析。

2. Optimize for Citations, Not Clicks

2. 为获得引用而优化，而非为获得点击

Traditional SEO optimized for search rankings that generated clicks. AI optimization requires a different approach:
传统的搜索引擎优化以提升排名、获取点击为目标，而 AI 优化则需要不同的思路：

Technical Implementation:
技术落地手段：

• Add credible citations to your content (improves AI visibility by up to 40% according to Princeton research, 2024)
  在内容中添加可信的引用来源（普林斯顿大学 2024 年的研究显示，此举可使 AI 曝光度提升至多 40%）
• Structure content with clear question-answer formatting
  采用清晰的问答格式构建内容结构
• Use deep schema markup extensively (30% higher chance of AI inclusion per SearchEngineLand, 2025)
  广泛使用深度模式标记（《搜索引擎天地》2025 年数据显示，此举可使内容被 AI 平台收录的概率提升 30%）
• Create FAQ sections that directly answer common queries
  设立常见问题板块，直接解答用户高频问题

Content Architecture:
内容架构设计：

• Place answers in the first 75-150 words of sections
  在每个内容板块的前 75-150 个单词中给出核心答案
• Break content into digestible 200-300 word segments
  将内容拆解为 200-300 个单词的易读片段
• Use descriptive headings that mirror user questions
  使用贴合用户提问方式的描述性标题
• Include statistical evidence and specific data points
  融入统计依据和具体数据

3. Choose Your Platform

3. 选择适配的平台

You can’t win everywhere. Pick based on your audience:
你无法在所有平台都取得成功，需根据目标受众选择适配的平台：

ChatGPT Strategy:
ChatGPT 平台优化策略：

• Focus on community presence and discussions
  聚焦社区运营和讨论互动
• Optimize for conversational queries
  针对对话式查询进行优化
• Accept minimal click-through as the cost of authority
  接受低点击率为获得平台权威性的必要代价

Perplexity Strategy:
Perplexity 平台优化策略：

• Emphasize research-backed content
  重点打造有研究支撑的内容
• Include academic-style citations
  添加学术风格的引用标注
• Benefit from 3-5x better click-through rates
  借助该平台 3-5 倍于其他平台的点击率实现流量转化

Gemini Strategy:
Gemini 平台优化策略：

• Maintain traditional SEO excellence
  保持传统搜索引擎优化的优势
• Focus on featured snippet optimization
  重点优化谷歌精选摘要
• Leverage existing Google authority
  借助已有的谷歌平台权威性

Claude Strategy:
Claude 平台优化策略：

• Create comprehensive professional resources
  打造内容全面的专业资源
• Focus on B2B and technical content
  聚焦企业对企业（B2B）和技术类内容
• Target professional user base
  以专业用户群体为目标受众

4. Rebuild Conversion Paths for Pre-Qualified Visitors

4. 为预筛选访客重构转化路径

AI visitors arrive differently prepared. Adjust accordingly:
AI 引流的访客在进入网站前已掌握大量信息，企业需据此调整转化路径：

Remove Education, Add Validation:
弱化信息科普，强化信息验证：

• Skip basic product explanations
  跳过基础的产品介绍
• Provide detailed specifications immediately
  直接展示详尽的产品规格
• Show social proof and testimonials prominently
  醒目展示社交证明和用户评价
• Focus on differentiators, not features
  聚焦产品差异化优势，而非基础功能

Trust Signals Over Marketing and Sales speak:
用信任标识替代营销话术：

• Display certifications and awards
  展示产品认证和所获荣誉
• Include detailed case studies
  附上详尽的案例分析
• Show actual customer results
  展示真实的客户使用效果
• Provide transparent pricing
  公示透明的定价信息

Buffer increased AI-referred visitor conversions by 34% by restructuring their site around “proof layers”—case studies, testimonials, and third-party validations—rather than traditional landing pages.
Buffer 公司通过围绕“信任层”（案例分析、用户评价、第三方验证）重构网站，而非使用传统落地页，使 AI 引流访客的转化量提升了 34%。

5. Develop New Metrics for Success

5. 制定全新的成功衡量指标

Traditional metrics mislead in an AI-dominated landscape:
在 AI 主导的互联网环境中，传统数据指标存在误导性：

Stop Obsessing Over:
无需再过度关注：

• Raw traffic numbers
  原始流量数据
• Click-through rates
  点击率
• Traditional ranking positions
  传统搜索引擎排名
• Page views
  页面浏览量

Start Measuring:
需要开始关注：

• Share of Voice in AI responses
  内容在 AI 回答中的声量占比
• Citation frequency across platforms
  内容在各平台的被引用频次
• Mention-to-citation ratios
  内容的提及-引用比
• Quality of citation context
  内容被引用的语境质量
• Conversion rate of AI-referred traffic
  AI 引流的转化效率

This can be done using a combination of manual AI visibility audits on your targeted platforms as well as specialized tooling:
可通过结合目标平台的人工 AI 曝光度审计和专业工具实现上述指标的监测：

Established Platforms:
成熟的监测平台：

SEMRush AI Visibility Index - Tracks brand mentions and citations across ChatGPT and Google AI Mode
SEMRush AI 曝光度指数 —— 监测品牌在 ChatGPT 和谷歌 AI 模式中的提及度和引用度

Authoritas SERP & AI Tracking - Monitors both traditional search and AI overview appearances
Authoritas 搜索引擎结果页及 AI 监测工具 —— 同时监测内容在传统搜索引擎和 AI 概览中的展示情况

HubSpot Share of Voice - Accessible solution that can track AI-informing awareness in wider spaces.
HubSpot 声量监测工具 —— 操作便捷，可监测内容在更广泛场景中对 AI 用户认知的影响

Perplexity Pages Analytics - Native analytics for Perplexity citations (limited but free)
Perplexity 页面分析工具 —— 该平台原生的引用监测工具（功能有限但免费）

Emerging Specialized Tools:
新兴的专业工具：
Otterly.ai - Dedicated AI search optimization platform with citation tracking
Otterly.ai —— 专注于 AI 搜索优化的平台，附带引用监测功能

AIVisibility.io - Tracks performance across ChatGPT, Claude, and Perplexity
AIVisibility.io —— 监测内容在 ChatGPT、Claude 和 Perplexity 平台的表现

How to Measure What You Can’t See

如何监测无形的 AI 曝光度

AI Visibility Audits

AI 曝光度审计

Here are some example queries you can test at regular intervals such as weekly, fortnightly, monthly across all target AI platforms:
你可按周、双周或月为周期，在所有目标 AI 平台中测试以下示例查询，开展 AI 曝光度审计：

1. “What are the best [your product category] for [target audience]?”
   “[目标受众 ] 适用的最佳[你的产品品类 ] 有哪些？”
2. “How do I solve [problem your product addresses]?”
   “如何解决[你的产品能解决的问题 ]？”
3. “What’s the difference between [your company] and [competitor]?”
   “[你的企业 ] 与[竞争对手 ] 的区别是什么？”
4. “[Your company name]” (brand query)
   “[你的企业名称 ]”（品牌直搜）
5. “Pros and cons of [your product/service]”
   “[你的产品/服务 ] 的优缺点是什么？”

Document:
需记录以下内容：

• Which platforms cite you
  哪些平台引用了你的内容
• Context of citations (positive, neutral, negative)
  引用语境（正面、中立、负面）
• Whether you appear in Stage 1 (discovery) or Stage 2 (validation)
  你的内容出现在第一阶段（信息发现）还是第二阶段（信息验证）
• Competitor citations for the same queries
  竞争对手的内容在相同查询中的被引用情况

Use the technique of query fan-out to map out the conversational journey your target user demographic might follow on a target AI platform. Use this to inform your content strategy and next campaign to get cited.
运用查询辐射法，梳理目标用户群体在目标 AI 平台中的对话式行为路径，并据此制定内容策略和下一次的获引用推广活动。

Platform-Specific Tracking

平台专属监测策略

ChatGPT: Monitor Reddit and forum mentions. Track discussion sentiment.
ChatGPT：监测品牌在红迪网和各论坛的提及度，追踪讨论舆论倾向。

Perplexity: Analyze citation frequency in research-style queries.
Perplexity：分析品牌内容在研究类查询中的被引用频次。

Gemini: Maintain traditional SEO tracking alongside AI citations.
Gemini：在监测 AI 引用的同时，保持传统的搜索引擎优化监测。

Claude: Focus on professional and technical query performance. Is your technical documentation being cited correctly?
Claude：聚焦品牌内容在专业和技术类查询中的表现，确认技术文档是否被正确引用。

Treat audits on these platforms as if they are a 24/7/365 on-demand focus group.
将各平台的 AI 曝光度审计，视为全年无休、可按需调用的焦点小组调研。

The Uncomfortable Truths:

不得不面对的现实：

You will lose traffic. The 95-96% reduction some publishers experience (TollBit, 2024) might be extreme, but significant drops appear to be inevitable and permanent.
你的网站流量会下降。 尽管部分出版商遭遇的 95%-96%的流量降幅（TollBit，2024）属于极端情况，但流量大幅下降已是不可避免且持续存在的趋势。

Platform control is minimal. Unlike SEO, you can’t reverse-engineer your way to visibility. AI models at the core of these platforms are probabilistic, not deterministic.
对平台的掌控力极低。 与搜索引擎优化不同，你无法通过逆向工程提升 AI 平台曝光度，因为这些平台的核心 AI 模型基于概率算法，而非确定性算法。

User generated content beats PR and corporate marketing.
用户生成内容的影响力远超公关和企业营销。

Citation without traffic is the new normal. Your business model will either need to account, or be re-invented, for this.
“有引用无流量”成为新常态。 你的商业模式需要适应这一变化，否则就必须重构。

You won’t be a universal source. The 7% of universal sources are globally recognizable, ubiquitous platforms. You’re likely not one of them. However, you could be an authoritative source on a couple of them. But it requires expert audience targeting.
你无法成为通用信息源。 那 7%的通用信息源均为全球知名、无处不在的平台，你大概率无法跻身其中。但你可以成为部分平台的权威信息源，而这需要精准的受众定位能力。

The Path Forward: Accepting the New Reality

未来之路：接受新的现实

What You Need to Do This Week

需采取的行动

1. Run the visibility audit. Test those five queries across all four platforms. Document your baseline.
   开展曝光度审计。 在上述四个平台中测试那五个查询，记录基准数据。
2. Pick your primary platform(s). You can’t win everywhere. Choose based on where your customers are.
   选定核心运营平台。 你无法在所有平台取胜，需根据目标客户的平台使用习惯选择。
3. Start community engagement. Search for your relevant communities or forums where your customers are talking to each other. Begin participating authentically—no promotion.
   启动社区运营。 寻找目标客户聚集的相关社区或论坛，以真实身份参与互动，不进行推广。
4. Restructure one key page. Pick your most important landing page. Restructure it for pre-qualified AI derived visitors.
   重构核心页面。 选择你最重要的落地页，为 AI 引流的预筛选访客进行重构。
5. Set up new tracking. Stop watching traffic. Start measuring citation counts, citation quality and Share of Voice.
   搭建全新的监测体系。 不再过度关注流量，转而监测引用频次、引用质量和声量占比。

What Success Looks Like Now

当下的成功标准

Success in the AI citation economy doesn’t look like traditional SEO wins. Instead:
在 AI 引用经济中，成功的标准与传统搜索引擎优化截然不同，具体表现为：

• Your brand appears consistently in AI responses for relevant queries
  你的品牌在 AI 平台相关查询的回答中持续出现
• Community discussions mention you positively without prompting
  你的品牌在社区讨论中获得自发的正面提及
• You get less visitors,but the visitors you do get convert at higher rates
  网站访客数量减少，但转化效率显著提升
• Your authority grows even as traffic continues to diminish
  尽管流量持续下降，品牌的权威性却不断提升
• You maintain visibility on at least one major platform
  至少在一个主流 AI 平台保持稳定的曝光度

Wistia exemplifies this new success. Their traffic from search dropped 40%, but leads from AI-referred visitors increased 60%. They achieved this through consistent participation in marketing communities, comprehensive documentation, and conversion paths optimized for pre-qualified visitors.
威斯蒂亚（Wistia）是这一新型成功的典型案例。其搜索引擎引流下降 40%，但 AI 引流带来的潜在客户量提升 60%。这一成果的取得，源于其持续参与营销社区、打造全面的官方文档，以及为预筛选访客优化转化路径。

Letting Go of Control

放下掌控欲

Traditional SEO gave us the illusion of control, and too many practitioners ended up falling into repeatable blueprints (admittedly, doing it right resulted in remarkably consistent performance regardless of industry or search query). Change these factors, improve these metrics, climb these rankings.
传统的搜索引擎优化让我们产生了“可掌控流量”的错觉，太多从业者陷入了可复制的优化模板（诚然，若操作得当，无论所属行业或搜索查询类型如何，都能取得稳定的效果）：调整相关因素、优化数据指标、提升搜索排名。

The comfort of this consistency goes away with AI citations.
而 AI 引用的出现，让这种“稳定可控”的舒适感不复存在。

You cannot control:
你无法掌控的因素包括：

• Which sources AI platforms choose
  AI 平台选择哪些信息源
• How they present your information
  AI 平台如何呈现你的内容
• Whether users click through
  用户是否会点击引用链接
• Which platforms gain market share
  哪些平台会占据市场份额
• When algorithms change
  算法何时进行更新

However, you can influence through:
但你可以通过以下方式施加影响：

• Your community presence and authenticity
  你的社区参与度和互动的真实性
• Content quality and structure
  内容质量和架构设计
• Documentation comprehensiveness
  官方文档的全面性
• Conversion optimization for AI visitors
  针对 AI 引流访客的转化优化
• Platform-specific strategies
  平台专属的优化策略

Industries at a Crossroads

处于十字路口的各行业

Some sectors face existential challenges:
部分行业正面临生存挑战：

Publishers and Media: With 95-96% traffic reduction potential, ad-based models collapse. Subscription or licensing become essential.
出版商和媒体行业：流量或面临 95%-96%的降幅，基于广告的商业模式将崩塌，订阅制或内容授权模式成为必然选择。

E-commerce: Product discovery shifts entirely to AI platforms. Direct relationships with customers become critical.
电子商务行业：产品发现环节完全转移至 AI 平台，与客户建立直接联系变得至关重要。

B2B Services: Community reputation matters more than corporate messaging. Thought leadership happens in forums, not blogs.
企业对企业服务行业：社区口碑比企业官方宣传更重要，行业思想引领出现在论坛，而非博客。

Local Businesses: AI platforms aggregate reviews and recommendations. Individual website traffic becomes almost irrelevant.
本地商户：AI 平台整合了各类评价和推荐，独立网站的流量几乎变得无关紧要。

Looking forward, in the next 24 Months

展望未来 24 个月

Based on current trends, we might expect:
基于当前趋势，未来 24 个月可能出现以下变化：
Citation click-through rates will improve as interfaces mature. They will improve much more if the interfaces of these AI platforms give more prominence to AI citations. The growing concern about content provenance and authenticity leads me to believe that the pressure for this increased visibility will increase over time.
引用链接的点击率将提升，这得益于 AI 平台界面的不断成熟。若各平台进一步突出引用链接的展示，点击率的提升幅度会更大。随着人们对内容来源和真实性的关注度不断提高，平台将面临越来越大的压力，不得不提升引用链接的曝光度。

Platform consolidation. We are in an AI induced bubble. There’s too much money sloshing around in the AI industry that is subsidising our usage. This will inevitably come to an end, and likely sooner than we expect, given how quickly we are barrelling through the hype cycle. It may not happen in the near term, but I do expect one of these four major AI platforms may be absorbed by another.
平台整合。 我们正处于 AI 催生的泡沫中，大量资金涌入 AI 行业，补贴着用户的使用成本。这一局面终将结束，且考虑到 AI 行业的炒作周期推进极快，结束的时间可能比我们预期的更早。尽管短期内可能不会出现，但上述四大 AI 平台中，大概率会有一家被另一家收购。

New monetization models will necessarily emerge for content—likely involving direct platform enablement (such as intra-platform markets, premium content consumption pricing for AI agents/specialized publisher AI agents) or partnerships. Right now the most prominent model is data/content licensing.
内容领域将诞生全新的盈利模式，这类模式可能涉及平台直接赋能（如平台内交易市场、向 AI 智能体/专业出版商 AI 智能体收取优质内容使用费）或行业合作。目前，最主流的模式是数据/内容授权。

Community-generated content will continue gaining importance over official sources. An optimist might foresee a renaissance in forums and bulletin boards. A pessimist might foresee enshittification as these third places become infested with marketers performing GEO/AISEO (whatever you want to call it).
社区生成内容的重要性将继续超越官方信息源。 乐观者可能会预见论坛和公告板的复兴，而悲观者则认为，随着大量营销人员涌入这些“第三空间”开展 GEO/AISEO（可按需命名）优化，这些平台将逐渐走向衰落。

Me? I think there will be a “barbelling” or stratification. Some platforms (that can afford to invest in the technologies to enable this) will retain and even increase in importance due to restrictive access, strong moderation and protection, and selective monetization of access (for both GEO/AISEO marketers and autonomous/agentic traffic) and the content. Some forums (most likely smaller, more niche ones that don’t have the capital to invest in this type of development) will fail to do this quickly, and will be “enshittified”.
而我的观点是：行业将出现“杠铃式”分层。部分有能力投入技术研发的平台，将通过限制访问、强化内容审核和保护、对 GEO/AISEO 营销人员及自动程序/智能体流量实行选择性的访问和内容盈利模式，保持甚至提升其重要性。而部分论坛（大概率是规模较小、更垂直的论坛，因缺乏资金无法开展此类研发），将因无法快速适应变化而走向衰落。

Specialized AI agents will further reduce the need for users to visit websites. These will become the new mobile apps, and we will likely see an entire ecosystem of AI agents that will interact with users and each other-an agentic fabric, if you will (no, seriously, research is already emerging that supports this).
专业 AI 智能体将进一步降低用户访问网站的需求。这类智能体将成为新一代的移动应用，而我们可能会看到一个完整的 AI 智能体生态系统——各类智能体之间、智能体与用户之间可实现互动，形成一张“智能体网络”（这并非空想，已有相关研究证实了这一趋势的可能性）。

Final Thoughts

最终思考

The shift from search to AI citations reshaping the way that we, as digital professionals share stories, market products and build brands. Those that built sustainable businesses on genuine value will most easily adapt. But those that relied on gaming algorithms and maximizing vanity metrics will struggle.
从搜索引擎到 AI 引用的转变，正重塑着数字行业从业者讲述品牌故事、推广产品和打造品牌的方式。那些基于真实价值打造可持续商业模式的企业，将最易适应这一变化；而那些依靠钻算法漏洞、追求虚荣数据指标的企业，将举步维艰。

We’re going to need to go back to the basics:
我们需要回归商业本质：

• Create genuine value for users
  为用户创造真实的价值
• Build authentic community relationships
  建立真实的社区关系
• Provide clear, accessible information
  提供清晰、易获取的信息
• Solve real problems effectively
  高效解决用户的实际问题

What is changing is the mechanisms. Instead of optimizing for a single algorithm that sends us traffic, we’re optimizing for models that synthesize information. Instead of measuring success through visits, we measure it through influence first and authority second.
发生变化的，只是实现商业目标的方式。我们不再为单一的、能为我们带来流量的算法进行优化，而是为整合信息的 AI 模型进行优化；我们不再以访问量衡量成功，而是将影响力放在首位，权威性放在第二位。

This transition period offers opportunities for those willing to abandon old assumptions. Small brands with authentic community presence can outrank giants. Niche expertise becomes more valuable than broad coverage.
这一转型期为那些愿意摒弃旧有认知的企业带来了机遇：拥有真实社区影响力的小众品牌，可超越行业巨头；垂直领域的专业能力，比泛领域的覆盖能力更具价值。

The winners will be those who accept this new reality fastest and adapt their strategies accordingly. The authority-traffic paradox isn’t a problem to solve—it’s the new normal to embrace.
最终的赢家，将是那些最快接受新现实并据此调整策略的企业。权威-流量悖论并非一个需要解决的问题，而是一个需要接受的新常态。

Your website will get fewer visitors. But those who arrive should be better qualified, more engaged, and have definitive expectations. Your content will not be for generating clicks, it will be for building authority and influencing decisions.
你的网站访客数量会减少，但到访的访客将是更优质的、互动性更强的，且有着明确的需求。你的内容不再是为了获得点击，而是为了打造品牌权威性，影响用户的决策。

Welcome to the age of the zero-click.
欢迎来到零点击时代。

References

参考文献

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Chatterji, A., Cunningham, T., Deming, D. J., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025). How people use ChatGPT. NBER Working Paper No. 34255. National Bureau of Economic Research.
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First Page Sage. (2025, May 27). Google click-through rates (CTRs) by ranking position. First Page Sage.
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Li, R. (2025). Adapting Your WordPress Site to AI Sense-Making Compression. Retrieved from https://drli.blog/posts/wordpress-ai/
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Mediaweek. (2025, April 22). Google’s AI overviews linked to sharp CTR declines. Mediaweek.
《媒体周刊》. (2025 年 4 月 22 日). 谷歌 AI 概览导致点击率大幅下降. 《媒体周刊》.

Pew Research Center. (2025, July 22). Google users are less likely to click on links when an AI summary appears in the results. Pew Research Center.
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SearchEngineLand. (2025). Schema AI overviews: structured data visibility. Search Engine Land.
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SEMRush. (2025). AI visibility index study: Market transformation analysis. SEMRush Enterprise.
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TollBit. (2024). State of the Bots Q4 2024 Report. TollBit Analytics.
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From Click to Citation: Part 2 - Building Teams for an Agentic Web

从点击到引用：第二部分——为智能体网络构建适配团队
Dr. Robert Li
26 Oct 2025

TL;DR

核心要点速览

• LLM algorithmic limitations—”agreeable average” problem, catastrophic forgetting, “lost in the middle” phenomenon, lateral thinking constraints—are fundamental mathematical constraints in neural networks, not temporary bugs. Organizations exploiting these through specialized expertise in the long tail of LLM training distributions create defensible competitive moats.
  大语言模型的算法局限性——“合意均值”问题、灾难性遗忘、“中间信息失效”现象、横向思维受限——均为神经网络中固有的数学约束，并非暂时性程序漏洞。企业若能凭借大语言模型训练数据长尾领域的专业能力利用这些局限性，便能构建起难以被复制的竞争壁垒。

• Three-tier architecture with fluid team assignments: Democratic Foundation (Tier 3 execution), Core Combinatorial Teams (Tier 2 defensible offerings), Frontier Innovation (Tier 1 R&D). Same teams and individuals operate fluidly across tiers based on combinatorial expertise and project context.
  搭建团队分配灵活的三层架构：普惠基础层（第三层，执行环节）、核心组合团队层（第二层，打造具备竞争壁垒的业务体系）、前沿创新层（第一层，研发环节）。同一团队与个人可依据自身的组合专业能力及项目场景，在各层级间灵活开展工作。

• Combinatorial specialization multiplies defensibility: Single-vector (ChatGPT specialist OR healthcare expert) = valuable but replicable. Dual-vector (platform × vertical) = rare combination. Multi-vector (Perplexity × financial services × MCP architect) = uniquely defensible and personalizable, exploiting underrepresented combinatorial spaces in LLM training data.
  组合专业化能大幅提升竞争壁垒的构建能力：单维度专业化（仅为 ChatGPT 平台专家或医疗行业专家）具备价值但易被模仿；双维度专业化（AI 平台×垂直行业）属于稀缺能力组合；多维度专业化（Perplexity 平台×金融服务行业×MCP 架构师）可挖掘大语言模型训练数据中代表性不足的组合领域价值，打造独一无二且可定制的竞争优势，具备极强的不可替代性。

• Democratization of AI fluency “10x’s every employee” and prevents specialist→generalist commoditization. Baseline AI capabilities across all teams enable individuals to be Tier 1 frontier specialists in one area while operating as Tier 2 or Tier 3 in others. AI amplifies execution velocity at Tier 3, accelerates innovation at Tier 2, and expands exploration capacity at Tier 1—creating culture where specialists emerge naturally rather than through centralized bottlenecks.
  人工智能应用能力的普惠化能“让每位员工的工作效能提升 10 倍”，并避免专业人才向通用型人才转化而产生的能力商品化问题。所有团队均掌握基础人工智能能力，可支持员工在某一领域成为第一层的前沿专家，同时在其他领域承担第二层或第三层的工作任务。人工智能技术能提升第三层的执行效率，加快第二层的创新节奏，拓展第一层的探索边界，进而打造专业人才自然涌现的团队文化，而非因集中化管理形成人才发展瓶颈。

• Human expertise provides trust advantages LLMs cannot replicate: 52% prefer human doctors, 70% prefer human financial advisors, perceived human authorship increases credibility (d=0.67, p<0.001). Combinatorial teams signal both technical optimization and domain authority users demand.
  人类专业能力具备大语言模型无法复刻的信任优势：52%的用户更倾向于选择人类医生提供服务，70%的用户更认可人类金融顾问的专业建议，内容由人类创作的认知会显著提升信息可信度（效应量 d=0.67，显著性 p<0.001）。组合型团队所具备的能力，恰好能同时满足用户对技术优化能力与行业权威度的双重需求。

• Platform fragmentation requires specialists: 71% of sources appear on only one platform; 7% achieve universal presence across ChatGPT, Gemini, Perplexity, Claude. Historical precedent shows specialized structures delivered 40-60% performance advantages during technological transitions.
  AI 平台的碎片化格局要求企业配备专业细分人才：71%的信息源仅出现在单一 AI 平台，仅有 7%的信息源能同时在 ChatGPT、Gemini、Perplexity、Claude 四大平台实现曝光。历史经验表明，在技术转型阶段，专业化的组织架构能为企业带来 40%-60%的效能优势。

• Organizations have 12-18 months to establish defensible positions before competitive advantages solidify (ChatGPT launched November 2022, currently ~26 months into transition).
  企业仅有 12-18 个月的窗口期构建具备竞争壁垒的市场地位，此后行业竞争优势将趋于固化（ChatGPT 于 2022 年 11 月上线，目前行业已进入技术转型期约 26 个月）。

In Part 1, we examined how AI platforms reshape digital visibility through citation patterns, the two-stage decision architecture, and the authority-traffic paradox. We outlined technical strategies for AI visibility optimization.
在第一部分中，我们分析了 AI 平台如何通过引用模式、两阶段决策架构及权威-流量悖论重构数字曝光逻辑，并梳理了优化 AI 曝光度的技术策略。

This raises the human question: Who executes these strategies? What organizational structures support AI visibility optimization across four major platforms with minimal overlap? How do teams develop expertise in both platform-specific algorithms and industry-specific user behavior?
这便引出了关于人才与组织的核心问题：由谁来落地这些策略？何种组织架构能支撑企业在四大低重合度的主流 AI 平台同步开展曝光度优化工作？团队应如何培养兼具 AI 平台算法特性与行业用户行为的双重专业能力？

The answers lie in organizational restructuring.
问题的答案，在于组织架构的重构。

What We Know: Foundation for Team Building

现有认知：团队搭建的底层基础

Before discussing organizational adaptation, let’s recap the essential context:
在探讨组织架构适配策略前，我们先梳理核心背景信息：

Citation and Platform Dynamics: Users click AI citations at approximately 1% compared to 15% for traditional search. Only 7% of sources appear across all four major platforms, while 71% appear on just one. Each platform exhibits distinct citation characteristics requiring platform-specific expertise (Li, 2025; SEMRush, 2025).
引用特征与平台格局：用户点击 AI 平台引用链接的比例约为 1%，而传统搜索引擎的该比例为 15%。仅有 7%的信息源能同时出现在四大主流 AI 平台，71%的信息源仅在单一平台展示。各 AI 平台具备独特的引用特征，要求企业配备专属的平台运营专业人才（李，2025；SEMRush，2025）。

User Behavior: Users follow a two-stage decision architecture. Stage 1 prioritizes user-generated content for discovery. Stage 2 uses official sources for validation. This creates the “mention-source divide” where community content appears overrepresented while officially published content receives approximately half the citation rate (SEMRush, 2025).
用户行为特征：用户在 AI 环境下遵循两阶段决策架构，第一阶段通过用户生成内容完成信息发现，第二阶段借助官方信息源进行信息验证。这一特征催生了“提及-来源鸿沟”——社区内容在 AI 引用中占比过高，而企业官方发布内容的被引用率仅为用户生成内容的约一半（SEMRush，2025）。

Rapid Evolution: As platforms mature and functionality evolves, optimization approaches require continuous adaptation in much shorter cycles than traditional SEO. This platform diversity and rapid evolution suggest organizations need specialist teams concentrating on specific AI platforms as well as specialist teams concentrating on specific industry verticals, working together with combined expertise.
行业发展特征：随着 AI 平台的成熟与功能迭代，其曝光度优化策略的调整周期远短于传统搜索引擎优化。基于 AI 平台的多元化格局与行业的快速发展特征，企业需要组建专属的 AI 平台细分团队与垂直行业细分团队，通过能力融合开展协同工作。

Platform and Vertical Expertise Requirements

平台与垂直行业的专业能力要求

Given these distinct platform characteristics and the minimal citation overlap between them, organizations face a fundamental question: what specialist capabilities do teams actually need? The answer splits into two complementary dimensions.
基于各 AI 平台的独特特征及极低的引用重合度，企业面临一个核心问题：团队究竟需要具备哪些专业细分能力？答案可分为两个互补的维度。

Platform-Specific Teams

平台专属细分团队

Each major AI platform requires distinct optimization approaches:
各大主流 AI 平台均需配套差异化的优化策略，对应的团队能力要求如下：

ChatGPT prioritizes community discussions with Reddit appearing in 141% of prompts and Wikipedia in 152%. Teams need conversational AI specialists, brand voice experts, and community engagement managers.
ChatGPT 平台更倾向于引用社区讨论内容，红迪网在 141%的查询请求中被提及，维基百科的提及比例达 152%。服务该平台的团队需配备对话式人工智能专家、品牌语料专家及社区运营经理。

Perplexity emphasizes research-backed content with 3-5% click-through rates—3-5x higher than ChatGPT. Teams require research specialists, data analysts, and citation experts ensuring academic-style documentation.
Perplexity 平台侧重引用有研究支撑的内容，其引用链接点击率达 3%-5%，是 ChatGPT 平台的 3-5 倍。服务该平台的团队需配备研究专员、数据分析师及引用规范专家，确保内容的学术化标注与文档化呈现。

Gemini shows lowest source diversity and adheres closest to traditional Google rankings. Teams need technical SEO specialists, Google quality framework experts, and YMYL domain specialists.
Gemini 平台的信息源多样性最低，且与谷歌传统搜索排名的契合度最高。服务该平台的团队需配备技术搜索引擎优化专家、谷歌质量框架研究专家及高价值信息领域专业人才。

Claude focuses on authoritative comprehensive content attracting professional users. Teams include authority-building specialists, long-form content experts, and industry thought leaders.
Claude 平台专注于引用权威、全面的内容，用户群体以专业人士为主。服务该平台的团队需配备品牌权威打造专员、长篇内容创作专家及行业思想领袖。

Vertical Industry Specialization

垂直行业专业细分

Rather than prescribing specific verticals, organizations should continuously assess where they possess differentiated domain expertise, which verticals exhibit AI citation patterns matching their content strengths, where they can create defensible moats, and what economic opportunity justifies investment.
企业无需拘泥于特定垂直行业，而应持续评估自身具备差异化优势的领域、与自身内容能力匹配的 AI 引用特征所属行业、能够构建竞争壁垒的赛道，以及具备投资价值的商业机会。

Misalignment between your content capabilities and vertical requirements creates uphill battles. The goal is identifying where your organization’s unique combination of technical and commercial expertise creates advantages competitors cannot easily replicate.
若企业的内容能力与所属行业的 AI 曝光需求不匹配，后续工作将事倍功半。核心目标是找到企业技术能力与商业能力的独特结合点，打造竞争对手难以复制的优势。

Why AI’s Mathematical Limitations Create Opportunity

人工智能的数学局限性为何能创造机会

This is the most critical insight underpinning the entire framework. Large language models train on massive datasets representing the center of the distribution—common knowledge, mainstream perspectives. This creates inherent, exploitable limitations.
这是支撑整个架构的核心认知。 大语言模型的训练数据基于海量的主流分布信息，即通用知识与主流观点，这一特征使其存在固有的、可被利用的局限性。

Understanding these limitations is pivotal because they are not temporary software bugs but fundamental mathematical and algorithmic constraints arising from how LLMs are architected.
理解这些局限性至关重要，因为它们并非暂时性的软件漏洞，而是由大语言模型的架构设计所决定的、固有的数学与算法约束。

The Four Mathematical Certainties

四大固有数学特征

1. The “Agreeable Average” Problem: LLM outputs sample from probability distributions shaped by training data. While hyperparameters like temperature can widen or narrow output aperture, output always centers on statistically probable middle ground. You cannot simultaneously maximize both determinism and diversity from the same model.
. 合意均值问题：大语言模型的输出结果基于训练数据形成的概率分布抽样生成。尽管温度系数等超参数可调整输出结果的范围，但输出内容始终围绕统计概率上的主流观点。无法在同一模型中同时实现结果确定性与多样性的最大化。

2. The Tuning Paradox: Extending sampling aperture too widely produces nonsensical gibberish. Very low values produce deterministic but overly narrow outputs lacking diversity. Organizations cannot simply “tune their way out” without introducing other failure modes.
调优悖论：若过度扩大抽样范围，模型输出内容将毫无意义；若抽样范围过小，输出结果虽具备确定性，但内容维度单一、缺乏多样性。企业无法仅通过参数调优解决这一问题，否则将引发其他类型的模型失效。

3. The Specialization Trap: Fine-tuning for specialization encounters overfitting (model memorizes specific examples rather than learning transferable patterns) and catastrophic forgetting (specialized data causes loss of general capabilities). Research shows as model scale increases, forgetting severity intensifies. The “lost in the middle” phenomenon means models exhibit U-shaped attention bias, favoring start and end of sequences while neglecting middle content—even with 100K+ token contexts.
专业化陷阱：为实现领域专业化而进行的模型微调，会面临过拟合与灾难性遗忘两大问题——过拟合指模型仅记忆具体案例，未习得可迁移的规律；灾难性遗忘指领域专属的训练数据会导致模型丧失通用能力。研究表明，模型规模越大，灾难性遗忘的程度越严重。而中间信息失效现象则指模型存在 U 型注意力偏差，即便支持 10 万以上的词元上下文，仍会更关注内容的开头与结尾，忽略中间部分信息。

4. The Pattern Transfer Problem: LLMs cannot genuinely “think laterally” or apply learned patterns to novel contexts underrepresented in training data. The famous “strawberry test” exposed this: models couldn’t count Rs because tokenization constraints prevented character-level reasoning despite having similar pattern knowledge.
模式迁移难题：大语言模型无法真正实现“横向思维”，也无法将习得的规律应用于训练数据中代表性不足的全新场景。著名的“草莓测试”便印证了这一点：尽管模型具备相关模式识别能力，但因分词规则的限制，无法实现字符级别的推理，进而无法完成单词中字母 R 的计数任务。

These Are Hard Problems Creating Strategic Windows

难解的技术问题造就战略窗口期

None of these represent insurmountable theoretical barriers. Researchers actively work on solutions. However, these remain difficult problems in deep learning as of 2025, requiring significant computational resources, novel architectures, and fundamental advances in how models encode and retrieve knowledge.
这些问题均非无法突破的理论壁垒，科研人员正积极研究解决方案。但截至 2025 年，这些仍是深度学习领域的难题，解决过程需要大量的计算资源、创新的模型架构，以及模型编码与知识检索方式的根本性突破。

This difficulty creates strategic windows: organizations structuring teams to exploit current limitations gain competitive advantages persisting until widespread solutions emerge—likely years away given complexity involved.
这些技术难题造就了行业战略窗口期：企业若能搭建适配的团队，利用大语言模型当前的局限性开展工作，便能构建持续的竞争优势，且该优势将一直保持至通用解决方案出现——鉴于问题的复杂性，这一过程可能需要数年时间。

These algorithmic constraints create the foundation for our organizational approach: combining platform and vertical expertise positions teams in the long tail of LLM training distributions where models struggle most, while democratizing baseline capabilities prevents specialist bottlenecks.
这些算法约束构成了本文组织架构设计的底层基础：将 AI 平台专业能力与垂直行业专业能力融合，能让团队聚焦于大语言模型训练数据的长尾领域——这正是模型表现最弱的领域；而基础能力的普惠化，能有效避免专业人才瓶颈的形成。

Why Human Expertise Remains Essential

人类专业能力为何仍不可或缺

Mathematical limitations create technical opportunities, but empirical research reveals another justification: users consistently prefer human involvement even when AI alternatives exist.
大语言模型的数学局限性创造了技术机会，而实证研究则揭示了另一核心事实：即便存在人工智能替代方案，用户仍始终倾向于有人类参与的服务与内容。

Domain-Specific Trust Patterns: Healthcare shows 52% preferring human doctors versus 47% AI. Financial services shows 70% preferring human advisors versus 6% robo-advisors. Customer service shows 81% willing to wait for human agents for complex problems (University of Arizona, 2024; CFA Institute, 2021; Callvu, 2024).
各领域的信任特征：医疗领域中，52%的用户更倾向于选择人类医生，47%的用户接受人工智能医疗服务；金融服务领域中，70%的用户认可人类理财顾问，仅 6%的用户选择智能投顾；客户服务领域中，81%的用户愿意为解决复杂问题等待人工客服（亚利桑那大学，2024；特许金融分析师协会，2021；Callvu 公司，2024）。

The Mechanism: Research identifies trust in automation through three dimensions: performance, process, and purpose. Human-in-the-loop systems optimize all three. Social presence theory research found higher social presence reduces three psychological tensions: feeling misunderstood → understood, replaced → empowered, alienated → connected (Oh et al., 2018).
底层逻辑：研究表明，用户对自动化系统的信任度由表现、流程、目的三个维度决定，而人类参与的系统能在这三个维度实现全面优化。社会存在感理论的研究显示，更高的社会存在感能缓解用户的三大心理焦虑：从被误解到被理解、从被替代到被赋能、从被疏离到被联结（吴等人，2018）。

High-Stakes Contexts: Identical text labeled AI-authored versus human-authored showed significant credibility differences: human-authored perceived as more credible (d = 0.67, p < 0.001) and more intelligent (d = 0.41). Perceived AI contribution predicted credibility decline independent of content quality (University of Kansas, 2024).
高风险场景特征：针对同一文本，标注为人工智能创作与人类创作会带来显著的可信度差异——人类创作的文本被认为更可信（效应量 d=0.67，显著性 p<0.001）、更具专业性（效应量 d=0.41）。研究还发现，无论内容质量如何，用户感知到的人工智能参与度越高，内容的可信度越低（堪萨斯大学，2024）。

Implications for AI Visibility Teams

对 AI 曝光度优化团队的启示

First, human expertise signals trust and authority. Content benefits from visible human involvement—author bios, professional credentials, domain expertise indicators—as trust mechanisms affecting user behavior when content appears in AI citations.
第一，人类专业能力是信任与权威的信号。内容中加入可见的人类参与标识——如作者简介、专业资质、领域专长证明——能作为信任机制，在内容出现在 AI 引用中时，对用户行为产生积极影响。

Second, task characteristics determine when human visibility matters most. High-stakes domains (healthcare, finance, professional services) require visible human expertise for user acceptance. Routine informational content shows more user flexibility.
第二，任务特征决定人类参与标识的重要程度。在医疗、金融、专业服务等高风险领域，需突出人类专业能力，才能获得用户认可；而对于常规的信息类内容，用户的接受度更高，对人类参与标识的要求较低。

Third, combinatorial specialization gains value from human collaboration dynamics. ChatGPT specialists contribute platform knowledge while healthcare experts contribute domain knowledge. This human-to-human synthesis creates content signaling both technical optimization and domain authority, addressing user preferences for human expertise in specialized contexts.
第三，组合专业化的价值源于人类的协同创新。ChatGPT 平台专家提供平台运营知识，医疗行业专家提供领域专业知识，人类之间的能力融合所创作的内容，能同时传递技术优化与行业权威的双重信号，契合用户在专业场景下对人类专业能力的偏好。

This insight—that users value human expertise particularly in specialized domains—leads directly to the organizational framework.
正是这一认知——用户在专业领域尤为重视人类专业能力——直接推动了本文组织架构框架的设计。

The Combinatorial Framework: Multiplying Defensibility

组合框架：实现竞争壁垒的倍数级构建

Mathematical limitations create technical opportunities. Human trust preferences create market opportunities. The combinatorial framework exploits both simultaneously by combining technical specialization (platform expertise) with commercial specialization (vertical expertise) to create exponentially defensible service offerings.
大语言模型的数学局限性创造了技术机会，用户对人类的信任偏好创造了市场机会。组合框架将技术专业化（平台专业能力）与商业专业化（垂直行业专业能力）融合，能同时利用这两大机会，打造具备指数级竞争壁垒的服务体系。

Single-vector vs. Multi-vector Specialization:
单维度与多维度专业化对比：

• Single-vector: ChatGPT specialist OR healthcare expert = valuable but replicable
  单维度：仅为 ChatGPT 平台专家或医疗行业专家——具备价值但易被复制
• Dual-vector: ChatGPT specialist × Healthcare expert = rare combination leveraging YOUR organization’s specific strengths
  双维度：ChatGPT 平台专家与医疗行业专家的能力融合——稀缺的能力组合，能充分发挥企业的独特优势
• Multi-vector: Perplexity specialist × Financial services expert × MCP architect = uniquely defensible based on what YOU possess and personalizable to YOUR client’s specific needs
  多维度：Perplexity 平台专家、金融服务行业专家与 MCP 架构师的能力融合——基于企业自身资源打造的、独一无二的竞争壁垒，且可根据客户的具体需求定制化调整

The framework recognizes competitive advantage emerges not from finding globally underrepresented niches, but from combining your organization’s specific technical and commercial capabilities in ways LLMs and competitors cannot easily represent.
该框架的核心逻辑是，企业的竞争优势并非源于寻找全球范围内的小众赛道，而是源于将自身独特的技术能力与商业能力进行融合，形成大语言模型与竞争对手难以复刻的能力组合。

Three-Tier Architecture for Sustained Innovation

持续创新的三层架构

Combinatorial specialization answers what creates defensibility. The organizational architecture answers how to build and sustain it. Organizations pursuing AI visibility optimization should implement a three-tier architecture where democratization provides foundation, combinatorial specialization creates core offerings, and frontier innovation continuously extends competitive moats.
组合专业化解答了什么能构建竞争壁垒的问题，而组织架构则解答了如何构建并维持竞争壁垒的问题。开展 AI 曝光度优化工作的企业，应搭建三层架构：以能力普惠化为基础，以组合专业化打造核心业务体系，以前沿创新持续拓展竞争壁垒。

Tier 3 - Democratic Foundation (Execution Layer): Base layer requires baseline AI visibility competency through systematic training rather than specialist expertise. Teams execute proven playbooks developed by Tier 2, handling operational AI visibility work at scale. This prevents bottlenecks, reduces costs, and enables rapid execution once approaches prove effective.
第三层——普惠基础层（执行层）：这一基础层级要求所有团队通过系统化培训掌握 AI 曝光度优化的基础能力，而非配备专业细分人才。该层级团队负责落地第二层开发的成熟方法论，规模化处理 AI 曝光度优化的日常运营工作。这一设计能有效避免人才瓶颈、降低运营成本，并确保有效策略的快速落地。

Tier 2 - Core Combinatorial Teams (Defensible Offering Layer): Middle tier operations combine technical specialists × commercial specialists based on existing organizational strengths. These teams create exponentially defensible service offerings by leveraging YOUR organization’s unique platform-vertical expertise combination to solve novel client use cases.
第二层——核心组合团队层（竞争壁垒层）：这一中间层级基于企业现有优势，实现技术专业人才与商业专业人才的能力融合。该层级团队利用企业独有的 AI 平台-垂直行业专业能力组合，解决客户的全新需求，打造具备指数级竞争壁垒的服务体系。

Core teams receive innovations from Tier 1 frontier specialists, consolidate them into deliverable services, then democratize proven components to Tier 3 foundational operations, freeing core specialist capacity for next innovation wave.
核心组合团队接收第一层前沿创新团队的创新成果，将其转化为可落地的服务方案，随后将经验证的成熟模块推广至第三层普惠基础层，释放核心专业人才的精力，投入到下一轮创新工作中。

Tier 1 - Frontier Innovation Teams (R&D Layer): Focus purely on exploration—emerging AI platforms before mainstream adoption, novel optimization techniques not yet proven, breakthrough methodologies, custom tool development. Frontier specialists test new platforms, develop proprietary algorithms, explore new community forums, create custom tooling, and research untested frameworks without pressure for immediate ROI.
第一层——前沿创新团队层（研发层）：这一层级专注于探索工作，包括研究尚未主流化的新兴 AI 平台、未经验证的新型优化技术、突破性方法论，以及定制化工具的开发。前沿创新专家开展新平台测试、自研算法开发、新社区论坛探索、定制化工具打造与未验证框架研究等工作，且无需承担短期投资回报的压力。

The Continuous Innovation Flow

持续的创新闭环

The architecture creates systematic innovation flow continuously extending competitive advantages:
这一三层架构构建了系统化的创新闭环，能持续拓展企业的竞争优势：

Tier 1 → Tier 2 Flow (Productization): Frontier teams discover optimization approaches through pure exploration. When approaches show promise, Tier 2 core teams integrate discoveries into client-facing services, refining for reliability and scalability.
第一层→第二层（产品化）：前沿创新团队通过纯探索性工作发现优化策略，当策略展现出应用潜力时，核心组合团队将其整合至面向客户的服务方案中，优化方案的可靠性与可规模化性。

Tier 2 → Tier 3 Flow (Democratization): As core teams prove approaches work with key clients, they document methodologies and train Tier 3 teams in execution. Proven techniques democratize, freeing Tier 2 capacity for next innovation wave.
第二层→第三层（普惠化）：核心组合团队在核心客户处验证策略有效性后，将方法论形成文档，并培训第三层团队落地执行。成熟技术的普惠化，能释放核心组合团队的精力，投入到下一轮创新中。

Tier 3 → Tier 1 Flow (Insight): Foundation teams executing at scale surface unexpected patterns, platform behavior changes, and edge cases. These insights feed back to Tier 1, informing frontier research priorities.
第三层→第一层（认知反哺）：普惠基础层团队在规模化执行过程中，能发现非预期的规律、平台行为变化与极端案例，这些认知将反哺至第一层，为前沿研究的优先级设定提供依据。

This creates compounding advantages: execution generates insights, insights inform innovation, innovation produces new capabilities, capabilities democratize, democratization frees specialist capacity for next frontier.
这一闭环形成了复利式优势：执行工作产生行业认知，认知指导创新方向，创新打造新能力，新能力实现普惠化，普惠化释放专业人才精力，进而推动新的前沿探索。

Democratizing AI Visibility: Innovation Teams and Fluid Specialization

AI 曝光度能力普惠化：创新团队与灵活专业化

The three-tier architecture risks creating rigid hierarchies if specialists remain locked into single tiers. The solution: democratize baseline AI capabilities across all employees while maintaining specialized depth where needed.
若专业人才被固定在单一层级，三层架构可能会形成僵化的层级体系。解决这一问题的关键是：在所有员工中实现 AI 基础能力的普惠化，同时在需要的领域保持专业深度。

Democratization of AI fluency means individuals can be Tier 1 frontier specialists in one area while operating as Tier 2 or Tier 3 in others. The same person pioneering ChatGPT research can join adjacent squads as capable executor or contribute platform insights to financial services optimization. This individual tier fluidity allows organizations to staff squads with the right expertise mix for each client and operational need.
AI 应用能力的普惠化，意味着员工可在某一领域担任第一层的前沿创新专家，同时在其他领域承担第二层或第三层的工作。例如，主导 ChatGPT 平台研究的专家，可加入其他团队担任高效的执行者，或为金融服务行业的 AI 曝光度优化提供平台专业认知。这种个人层级的灵活性，能让企业根据不同客户与运营需求，为团队配备最优的能力组合。

The Democratization Imperative

能力普惠化的必要性

Traditional enterprise approach—centralized units staffed by expensive specialists—creates bottlenecks unsuitable for AI visibility optimization’s rapid iteration requirements. Geoff Woods argues in The AI-Driven Leader that organizations must focus on “10x’ing the impact of every employee” by empowering marketers, content creators, and subject matter experts to optimize for platform citations without requiring specialized intermediaries (Woods, 2024).
企业的传统运营模式——组建集中化的团队并配备高成本的专业人才——易形成人才瓶颈，无法适配 AI 曝光度优化工作的快速迭代需求。杰夫·伍兹在《人工智能驱动的领导者》一书中提出，企业应聚焦于“让每位员工的工作影响力提升 10 倍”，赋能营销人员、内容创作者与领域专家，使其能直接开展平台引用优化工作，无需专业中介的参与（伍兹，2024）。

Andreas Welsch reinforces this in the AI Leadership Handbook, emphasizing transformation requires “turning new-to-AI employees into passionate multipliers” rather than building separate AI teams. Applied to visibility optimization, this means integrating AI citation optimization into existing workflows, not creating “AI visibility specialists” who become organizational bottlenecks (Welsch, 2024).
安德里亚斯·韦尔施在《人工智能领导力手册》中进一步印证了这一观点，强调企业转型需要“将人工智能零基础的员工培养为富有热情的能力放大器”，而非单独组建人工智能团队。将这一理念应用于曝光度优化工作，即把 AI 引用优化融入现有工作流程，而非打造会形成组织瓶颈的“AI 曝光度专业团队”（韦尔施，2024）。

Innovation Squads Over Specialist Departments

创新小组替代专业部门

Rather than centralized units, organizations should establish small autonomous innovation squads combining diverse skillsets with clear mandates for experimentation.
企业应摒弃集中化的部门架构，组建小型、自主的创新小组，融合多元能力，并为其明确实验探索的工作目标。

Squad Structure (5-8 people maximum): Content creator, technical marketer, vertical subject matter expert, data analyst, product/platform user. Each squad “owns” a combinatorial approach combining AI platform with related specialized vertical applications.
小组架构（最多 5-8 人）：配备内容创作者、技术营销人员、垂直行业领域专家、数据分析师及产品/平台用户代表。每个小组独立负责一种 AI 平台与相关垂直行业融合的组合式优化策略。

Tier assignment based on combinatorial specialization: Squads function as Tier 2 when working with key clients with novel requirements. Same squads function as Tier 3 when applying proven approaches across standard use cases. Tier assignment is fluid and dependent upon use case.
基于组合专业化的层级分配：当为有全新需求的核心客户提供服务时，小组承担第二层的工作；当将成熟策略应用于标准化场景时，小组承担第三层的工作。层级分配具备灵活性，完全依据具体工作场景调整。

Bidirectional learning flows between tiers: When Tier 3 squads encounter optimization challenges beyond established playbooks, they escalate to Tier 2 mode or share learnings. Tier 2 squads developing novel solutions document approaches for Tier 3 application, sharing experimental insights with Tier 1 frontier teams. Learning flows continuously: Tier 3 → Tier 2 (execution insights inform innovation), Tier 2 → Tier 1 (productization challenges inform research), Tier 1 → Tier 2 (discoveries enable new specializations).
层级间的双向学习闭环：当承担第三层工作的小组遇到现有方法论无法解决的优化难题时，可升级为第二层工作模式，或共享问题与认知；承担第二层工作的小组在开发全新解决方案后，将方法论形成文档，供第三层落地使用，同时将实验认知共享给第一层的前沿创新团队。层级间形成持续的学习闭环：第三层→第二层（执行认知指导创新）、第二层→第一层（产品化难题指导研究）、第一层→第二层（创新发现推动新的专业化方向）。

Autonomous operation with aligned objectives: Squads operate with high autonomy within guardrails. Leadership defines success metrics but doesn’t prescribe approaches. This autonomy proves essential where winning tactics emerge through experimentation, not planning.
目标一致下的自主运营：创新小组在企业设定的框架内拥有高度的自主运营权，管理层仅明确成功指标，不规定具体执行方法。在 AI 曝光度优化领域，有效的策略往往源于实验探索而非规划，因此这种自主运营权至关重要。

How AI Amplifies Each Tier Differently

人工智能对各层级的差异化赋能

Within the Three-Tier Architecture, AI amplification serves distinct purposes:
在三层架构中，人工智能技术对各层级的赋能具备差异化的目标：

Tier 3: AI amplifies execution velocity through assisted workflows applying proven approaches at scale.
第三层：通过辅助工作流赋能，推动成熟策略的规模化落地，提升执行效率。

Tier 2: AI enables specialists to test more hypotheses faster, rapidly prototyping optimization variations and accelerating the innovation→productization cycle.
第二层：赋能专业人才，使其能更快地测试更多假设，快速打造优化策略的原型，加快从创新到产品化的周期。

Tier 1: AI expands exploration capacity, allowing frontier teams to monitor emerging platforms, analyze unconventional citation patterns, and experiment with novel techniques.
第一层：拓展探索能力，支持前沿创新团队监测新兴 AI 平台、分析非常规的引用模式，并实验全新的优化技术。

Without this tier-specific distinction, organizations risk using AI merely to scale execution without building innovation capacity—creating efficient mediocrity rather than defensible competitive advantages.
若忽视这种层级间的差异化赋能，企业可能仅将人工智能用于执行工作的规模化，而未打造创新能力，最终形成“高效的平庸”，无法构建具备竞争壁垒的优势。

Managing the Democratization Transition

能力普惠化转型的管理策略

Governance Without Gate-Keeping—Tier-Specific Frameworks: Governance requirements differ significantly across tiers.
无壁垒管控——层级专属框架：各层级的管控要求存在显著差异。

Tier 3 Governance: Squads execute proven playbooks requiring clear guidelines defining brand voice boundaries, compliance requirements, quality thresholds, and escalation paths. Tier 3 can publish content without review as long as error rates remain below defined thresholds and execution follows documented playbooks.
第三层管控：小组负责落地成熟方法论，因此需要明确的指导框架，包括品牌语料边界、合规要求、质量标准及问题升级路径。只要错误率低于设定阈值且执行过程遵循文档化方法论，第三层团队可自主发布内容，无需审核。

Tier 2 Governance: Core teams create new approaches requiring governance protecting brand integrity while permitting strategic experimentation. Tier 2 specialists can deviate from established playbooks when developing novel optimizations but must document rationale, measure results, and obtain approval before democratizing approaches to Tier 3. Error budgets are higher—Tier 2 can test unproven tactics.
第二层管控：核心组合团队负责开发全新策略，因此管控框架需在保护品牌形象的同时，允许战略性实验。第二层专业人才在开发新型优化策略时，可偏离现有方法论，但需记录设计思路、量化结果，并在将策略推广至第三层前获得审批。该层级拥有更高的试错空间，可测试未经验证的策略。

Tier 1 Governance: Frontier innovation teams explore uncharted territory requiring minimal governance constraints. Tier 1 operates in “safe-to-fail” mode—experiments that fail provide learning without material business risk because Tier 1 doesn’t touch production work. Governance focuses on learning capture and ethical boundaries, not execution standards.
第一层管控：前沿创新团队探索未知领域，因此仅需最低限度的管控约束。该层级采用“安全试错”模式，因不参与实际业务落地，实验失败仅能带来学习经验，不会造成实质性的商业风险。管控的核心是学习经验的沉淀与道德边界的把控，而非执行标准的要求。

Lessons from Digital Agency Transformations

数字营销机构转型的经验启示

Digital agencies provide instructive precedents. The mobile-social revolution between 2007-2025 fundamentally dismantled traditional advertising agency structures, forcing wholesale organizational reinvention.
数字营销机构的转型为企业提供了具有参考价值的先例。2007-2025 年的移动社交革命，从根本上颠覆了传统广告公司的架构，迫使行业进行全面的组织重构。

From Silos to Squads: Traditional pre-2007 structures operated in rigid departmental silos with waterfall processes. From 2012-2015, cross-functional pod structures began replacing silos. Influenced by agile methodologies, agencies assembled small multi-disciplinary squads of 5-8 people taking end-to-end ownership of client work.
从部门孤岛到小组协作：2007 年前的传统架构采用僵化的部门孤岛模式，执行瀑布式工作流程。2012-2015 年，跨职能小组架构开始取代部门孤岛，受敏捷方法论的影响，数字营销机构组建 5-8 人的小型跨学科小组，对客户工作进行全流程负责。

The SmartBug Media model exemplifies mature pod implementation: each pod led by senior strategist (10+ years experience) who owns revenue, manages 5-7 accounts with supporting consultants, eliminating traditional account manager gatekeepers (HubSpot, 2025).
SmartBug Media 公司的模式是跨职能小组成熟落地的典型案例：每个小组由拥有 10 年以上经验的高级策略师带领，负责团队营收，管理 5-7 个客户账户，并配备顾问提供支持，摒弃了传统的客户经理中间层（HubSpot，2025）。

Agile Methodology Adoption: When properly implemented, agile squads test ideas 5-10x faster, execute campaigns 2-3x faster than non-agile teams, while spending 10-30% less on marketing execution and achieving 20-30% increases in marketing revenues (McKinsey & Company, 2024).
敏捷方法论的落地：若落地得当，敏捷小组的创意测试速度比非敏捷团队快 5-10 倍，营销活动执行速度快 2-3 倍，同时营销执行成本降低 10%-30%，营销营收提升 20%-30%（麦肯锡咨询公司，2024）。

Size-Based Adaptation: Large enterprise agencies faced greatest structural inertia with three-year restructuring cycles. Mid-sized regional agencies (50-500 employees) proved more agile than holding companies but more resourced than boutiques. Boutique agencies under 50 employees proved most naturally adapted with flat structures enabling projects completed 2-3x faster.
基于规模的差异化适配：大型企业级数字营销机构的架构惯性最强，组织重构周期约为三年；中型区域机构（50-500 名员工）的敏捷性优于大型控股公司，同时拥有比小型精品机构更丰富的资源；员工数不足 50 人的小型精品机构则最适配敏捷模式，扁平化的架构使其项目完成速度比传统模式快 2-3 倍。

Critical Success Factors: Agencies thriving in 2025 share common characteristics: strategic clarity in positioning, operational excellence in systems, AI capability investment, financial discipline, client relationships structured as advisory not transactional, adaptability enabling quick pivots, and innovation mindset with continuous experimentation.
核心成功因素：2025 年实现良好发展的数字营销机构具备共同特征：清晰的战略定位、卓越的运营体系、人工智能能力的持续投入、严格的财务管控、顾问式而非交易式的客户关系、快速转型的适配能力，以及持续实验的创新思维。

Practical Considerations

实操考量

Resource Allocation Challenges

资源配置的挑战

Platform specialist hiring involves multi-month lead times and competitive compensation. Vertical specialist development requires sustained training periods. Cross-functional coordination requires initial setup investment and ongoing maintenance for tools, measurement platforms, and collaboration infrastructure.
AI 平台专业人才的招聘周期长达数月，且薪酬竞争激烈；垂直行业专业人才的培养需要持续的培训周期；跨职能协作则需要在工具、监测平台与协作基础设施方面进行初期投入，并开展持续的维护工作。

Organizations must secure executive commitment and develop realistic budget expectations before launching AI visibility initiatives.
企业在启动 AI 曝光度优化项目前，必须获得管理层的支持，并制定符合实际的预算预期。

The Patience Problem: Executive stakeholders accustomed to traditional digital marketing expect rapid results. AI visibility optimization operates on longer timelines with less certain outcomes. Manage expectations proactively. Establish realistic KPIs focused on Share of Voice and citation quality rather than traffic and revenue.
耐心难题：管理层受传统数字营销模式的影响，往往期望项目快速取得成果，而 AI 曝光度优化的周期更长、结果的确定性更低。企业需主动管理管理层的预期，制定以声量占比和引用质量为核心的、符合实际的关键绩效指标，而非单纯关注流量与营收。

Coordination and Collaboration Issues

协同合作的问题

Platform-Vertical Conflicts: Platform specialists optimize for algorithm behavior. Vertical specialists protect brand integrity, regulatory compliance, and audience trust. These priorities sometimes conflict. Resolve through clear escalation frameworks, shared success metrics balancing platform performance with brand integrity, and regular dialogue.
平台-行业的目标冲突：AI 平台专业人才的核心目标是适配算法行为，而垂直行业专业人才则聚焦于保护品牌形象、确保合规及维护用户信任，二者的工作重点有时会产生冲突。企业可通过明确的问题升级框架、平衡平台表现与品牌形象的共享成功指标，以及常态化的沟通机制解决这一问题。

Siloed Expertise: Specialists develop deep knowledge but may lose sight of broader organizational objectives. Combat through unified AI visibility mission statements, shared team goals, regular cross-functional meetings for knowledge sharing, and rotation opportunities.
专业能力的孤岛化：专业人才会积累深厚的领域知识，但可能忽视企业的整体目标。企业可通过制定统一的 AI 曝光度使命宣言、设定团队共享目标、召开常态化的跨职能知识分享会，以及提供岗位轮换机会，打破专业能力的孤岛化。

Adaptation and Evolution Challenges

适配与迭代的挑战

Platform Algorithm Changes: AI platforms update frequently with less transparency than traditional search engines. Build organizational resilience through continuous experimentation capacity, rapid hypothesis testing when performance changes, documentation of historical approaches, and accepting uncertainty as inherent to AI visibility optimization.
平台算法的更新：AI 平台的算法更新频率高，且透明度低于传统搜索引擎。企业需通过打造持续的实验能力、在表现变化时快速开展假设测试、沉淀历史方法论，以及接受不确定性为 AI 曝光度优化的固有特征，构建组织的抗风险能力。

Emerging Platform Uncertainty: New platforms launch constantly. Should you invest early or wait for market consolidation? Balance exploration (Tier 1 frontier teams investigate) with focus (Tier 2 and 3 concentrate on proven platforms).
新兴平台的不确定性：新的 AI 平台持续涌现，企业应提前布局还是等待市场整合？核心策略是平衡探索与聚焦——由第一层前沿创新团队负责新兴平台的研究，第二层与第三层团队则聚焦于成熟平台的运营。

Conclusion: Building for an Uncertain Future

结论：为充满不确定性的未来构建适配组织

The transition from click-based to citation-based digital visibility represents as fundamental a shift as mobile and social media transformations. Organizations that built specialized team structures for those transitions achieved 40-60% performance advantages over competitors maintaining rigid hierarchies.
从基于点击到基于引用的数字曝光模式转型，是与移动社交变革具有同等重要性的根本性转变。在移动社交变革中，搭建了专业化团队架构的企业，比保持僵化层级体系的竞争对手拥有 40%-60%的效能优势。

Today’s opportunity window is narrowing. ChatGPT launched November 2022—we’re approximately 26 months into this transition. Organizations have 12-18 months remaining to establish defensible positions before competitive advantages solidify and best practices commoditize.
当前的行业战略窗口期正不断收窄，ChatGPT 于 2022 年 11 月上线，行业已进入转型期约 26 个月。企业仅剩 12-18 个月的时间构建具备竞争壁垒的市场地位，此后行业竞争优势将趋于固化，最佳实践也将逐渐商品化。

The three-tier architecture with fluid team assignments creates sustainable advantages through continuous learning flows connecting execution efficiency, innovation capacity, and exploration capability.
团队分配灵活的三层架构，通过连接执行效率、创新能力与探索能力的持续学习闭环，为企业构建可持续的竞争优势。

As detailed earlier, LLM’s fundamental mathematical constraints—the “agreeable average” problem, catastrophic forgetting, and lateral thinking limitations—create strategic windows unlikely to close soon. Organizations combining specialized expertise in underrepresented domains with human expertise users trust create moats competitors cannot easily replicate.
如前文所述，大语言模型固有的数学约束——合意均值问题、灾难性遗忘与横向思维受限——造就了短期内难以关闭的战略窗口期。企业若能将长尾领域的专业细分能力，与用户信任的人类专业能力融合，便能打造竞争对手难以复制的竞争壁垒。

Your competitive advantage won’t come from technology alone. It emerges from organizational structures that combine platform expertise with vertical specialization, that democratize execution while concentrating innovation, that build for continuous adaptation rather than static excellence.
企业的竞争优势并非仅源于技术，而是源于能实现平台专业能力与垂直行业专业化融合的组织架构，源于能在执行环节实现普惠化同时聚焦创新的运营模式，源于为持续适配变化而非追求静态卓越而搭建的团队体系。

The question isn’t whether to transform your organization for AI visibility optimization. It’s whether you’ll do it while the strategic window remains open.
企业面临的问题，并非是否需要为 AI 曝光度优化进行组织转型，而是能否在战略窗口期关闭前完成这一转型。

For practical implementation guidance, see the [Appendix: Implementation Frameworks and Templates] in the full essay, which contains detailed templates to guide this organizational transformation.**
**如需具体的落地指导，可参考完整版文章中的《附录：落地框架与模板》，其中包含指导本次组织转型的详细模板。

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via：

• Dr. Robert Li | From Click to Citation: Part 1 - Practical Research-informed Technical Strategies for AI Visibility
  https://drli.blog/posts/citation-attention-short/
• Dr. Robert Li | From Click to Citation: Part 2 - Building Teams for an Agentic Web
  https://drli.blog/posts/citation-attention-pt2-teams/
• Dr. Robert Li | Emerging Practices in Team Building for an Agentic Internet
  https://drli.blog/posts/emerging-practices-3t-team/