# How to Build An AI Operator for Your Businesses
**作者**: Nick Spisak
**日期**: 2026-04-25T20:51:04.000Z
**来源**: [https://x.com/NickSpisak_/status/2048142772352528711](https://x.com/NickSpisak_/status/2048142772352528711)
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You're the salesperson. The closer. The PM. The strategist. The one fixing payroll at 11pm on Thursday. That's not a service business. That's a job that pays on an unreliable schedule.
The thing missing isn't another tool. It's an operator. Every generic AI you've tried is a search engine in a polite voice, not the chief of staff you need.
In under 5 minutes you'll learn:
The 4 architecture patterns behind a working AI operator
The 4-part system prompt that turns a Claude Project into a chief of staff
The 6 specialist modes that run my service business week
The exact bottleneck each mode unlocks (pricing, closing, ops, cash, positioning)
How to build your own operator for one expert in a single afternoon
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## What an AI operator actually does
An AI operator does four things a generic AI won't:
- Routes to the right framework instead of averaging every expert into mush
- Runs the cadence with you... dial counts, follow-ups, weekly cash reviews
- Names the bottleneck you're avoiding, not the one you brought
- Refuses to flatter weak offers, weak proposals, weak hires
If your AI never disagrees with you, it's not advising. It's flattering. Flattery is the most expensive feature in AI right now.
## The 4 architecture patterns
Four phases, in order:
1. Curate, don't scrape. Every expert gets a manifest of authoritative sources, tagged by priority and category. YouTube channel, audiobooks (highest signal per byte), guest podcast appearances (where the off-script reasoning lives), long-form posts. A min-duration filter strips clips and shorts so reaction videos don't dilute the corpus.
A manifest row looks like this:
The advisor is only as sharp as the sources you give it.
2. Caption-first transcription. Before spending a dollar on paid transcription, check how much of the corpus already has YouTube captions. Pull those. Whisper the rest. This one decision drops build cost from hundreds of dollars per expert to a few cups of coffee.
3. Distill, don't dump. Loading 600 hours of raw transcript into a Claude Project gives you a noisy chatbot. The distill step is the work: chunk the corpus, run each chunk through a Claude pass that pulls out frameworks, voice patterns, and decision rules. Aggregate, dedupe, rank by evidence and specificity. Output is two short files that get loaded as Project knowledge.
A frameworks file entry looks like this:
A voice patterns file entry looks like this:
4. Eval before you trust. Every operator ships with a suite of test prompts. Each one has a "strong answer should" checklist of 3-4 specific things the answer must hit. Two Claude calls per prompt: one answers with the Project knowledge loaded, one judges against the checklist. Failures send you back to the distill step.
A test prompt looks like this:
That's the difference between a Project that sounds smart and one you'll trust near a real client decision.
## The system prompt that makes it work
The knowledge files are the brain. The system prompt is the personality and the refusal behavior. Without it, you get a polite chatbot. With it, something that tells you your offer is weak in the first sentence.
Every operator runs on a 4-part project-instructions file:
Copy that structure verbatim. Swap in expert-specific tone markers per advisor. Done.
The week below runs on this stack.
## 1. Monday: pricing reset (Hormozi mode)
Most service businesses are 30-50% underpriced. Hormozi mode runs the Value Equation against my offer: dream outcome, perceived likelihood, time delay, effort. It tells me where the offer is generic, what risk reversal is missing, and what the price floor is given the proof.
A 30-minute conversation produces a packaged offer with a higher ceiling. ChatGPT gives you "consider exploring." Hormozi mode tells you which lever to move first.
## 2. Tuesday: six-figure proposal prep (Voss mode)
You know the deals where the prospect goes quiet on price and you start pre-discounting? I run discovery notes through Voss mode for the calibrated questions I missed. It names the labels and mirrors that would have exposed the real objection, then rewrites the follow-up with tactical empathy instead of the apologetic tone that creeps in when a deal wobbles.
## 3. Wednesday: pipeline cadence (Blount mode)
Every service business hits a sales plateau. Almost always the cause is the same: not enough top-of-funnel activity, dressed up as a strategy problem.
Blount mode does the math you don't want to do. Dial counts, contact rates, conversion ratios, the actual number of new conversations you need this week to hit your quarter. It calls out the LinkedIn scrolling that masquerades as prospecting.
## 4. Thursday: ops fire (Herold mode)
The agency owner who can't take two weeks off without revenue dropping 30% has an org design problem, not a marketing problem.
Herold mode treats the business like a COO. It diagnoses where you're still the bottleneck, what role to hire next, and what filter so the new person isn't a project. Pairs with Dan Martell's Buyback Principle: hours bought back go into the next bottleneck.
## 5. Friday: cash flow check (Michalowicz mode)
Service businesses go broke profitably every day of the week. Michalowicz mode runs Profit First mechanics on a service P&L: real account allocations on real numbers. It surfaces margin leaks inside operations that look healthy on paper.
The Friday check is fifteen minutes. The answer is either "you're fine" or "you have eight weeks of runway and didn't know it."
## 6. Sunday: positioning audit (Thompson mode)
Once a month I drop the niche, offer, and ideal client into Thompson mode and ask whether the strategic position actually compounds. Aggregation Theory, supply economics, long-term defensibility.
Most service businesses run on a position that worked three years ago. The market moved. The position didn't. Thompson mode pulls me out of the week and into the year.
## Build your own (one expert, one afternoon)
You don't need a pipeline. You need one expert and four steps that mirror the architecture above:
- Curate: Pick the expert whose frameworks unlock your biggest bottleneck. Pricing? Hormozi. Closing? Voss. Cash? Michalowicz. List 8-12 of their highest-signal videos and audiobooks.
- Transcribe: Pull YouTube captions where they exist. MacWhisper handles the rest for free. Most of a corpus by lunch.
- Distill: Write two markdown files: frameworks (named, with the math) and voice patterns (how they push back, refuse, prioritize).
- Eval: Create a Claude Project, load both files, paste the 4-part system prompt above. Write 6-10 prompts from the last 30 days of decisions, each with a "strong answer should" checklist of 3-4 bullets. If the answers miss the checklist, the distill is too thin. Sharpen and rerun.
That is one operator. Same four-step recipe repeats for the next twelve.
## The closer
Generic AI averages every expert into polite mush: never wrong, never useful enough to act on. A specialist mode only knows one expert's frameworks. It tells you to raise prices when ChatGPT tells you to A/B test.
13 specialists. 4 architecture patterns. One operator that flips between modes based on what the week brings. Boring on purpose. The bench is the moat. The operator that knows which framework to run, when to run it, and what to refuse is the asset that compounds.
You don't need more advice. You need an operator. Build one this afternoon.
PS: If you enjoy content like this, you'll love my weekly newsletter.
Every week I give you tools, prompts, and automations you can deploy the same day to turn AI into ROI in your business.
Subscribe (free) here.
## 相关链接
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- [4:51 AM · Apr 26, 2026](https://x.com/NickSpisak_/status/2048142772352528711)
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*导出时间: 2026/4/26 11:53:05*
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## 中文翻译
# 如何为你的企业打造一个 AI 运营官
**作者**： Nick Spisak
**日期**: 2026-04-25T20:51:04.000Z
**来源**： [https://x.com/NickSpisak_/status/2048142772352528711](https://x.com/NickSpisak_/status/2048142772352528711)
---
你是销售人员。是最终拍板的人。是项目经理。是战略家。是周四晚上 11 点还在处理工资单的那个人。这根本不是一门服务业生意。这是一份付款时间还不可靠的工作。
你缺的不是又一个工具。而是一位运营官。你试过的每一个通用 AI，都只是说话客气的搜索引擎，而不是你真正需要的幕僚长。
在不到 5 分钟内，你将学到：
- 可用的 AI 运营官背后的 4 种架构模式
- 能把 Claude Project 变成幕僚长的 4 部分系统提示词
- 支撑我服务业务每周运转的 6 种专家模式
- 每种模式所解锁的确切瓶颈（定价、成交、运营、现金流、定位）
- 如何在一个下午为某位专家搭建你自己的运营官
附言：如果你喜欢这类内容，你会爱上我的每周简报。
每周我都会给你提供工具、提示词和自动化方案，你当天就能部署，让 AI 在你的业务中变成真正的回报。
在此订阅（免费）。
## AI 运营官到底做什么
AI 运营官能做四件通用 AI 不会做的事：
- 路由到正确的框架，而不是把所有专家平均成一团浆糊
- 和你一起执行节奏……拨号数量、跟进、每周现金流复盘
- 点出你正在回避的瓶颈，而不是你主动提出的那个
- 拒绝奉承软弱的产品、糟糕的提案、不称职的员工
如果你的 AI 从不反对你，那它不是在提供建议，而是在讨好你。讨好是当下 AI 中最昂贵的功能。
## 4 种架构模式
四个阶段，按顺序进行：
1. 策展，而非抓取。每位专家都有一份权威来源清单，按优先级和类别打上标签。YouTube 频道、有声书（每字节信号量最高）、客串播客节目（即兴推理的所在）、长篇帖子。一个最短时长过滤器会剔除片段和短视频，这样反应类视频就不会稀释语料库。
清单中的一行大概长这样：
顾问的敏锐度，取决于你给它的资料质量。
2. 字幕优先的转录。在花一分钱购买付费转录之前，先检查语料库中有多少已经带有 YouTube 字幕。先把它们拉取下来。其余的再用 Whisper 转录。仅这一个决策，就能把每位专家的构建成本从数百美元降到几杯咖啡钱。
3. 提炼，而非倾倒。把 600 小时的原始转录文本塞进一个 Claude Project，你得到的只是一个嘈杂的聊天机器人。提炼这一步才是真正的工作：把语料库分块，让每一块经过一次 Claude 处理，提取出框架、语言风格模式和决策规则。汇总、去重、按证据和具体性排序。输出是两个简短的文件，作为 Project 知识加载。
框架文件中的一个条目长这样：
语言风格文件中的一个条目长这样：
4. 先评估，再信任。每个运营官都附带一套测试提示词。每条都有一个“好的回答应该”清单，列出回答必须命中的 3-4 个具体要点。每条提示词进行两次 Claude 调用：一次在加载 Project 知识的情况下作答，另一次依据清单进行评判。失败则回到提炼步骤。
一条测试提示词长这样：
这就是“听起来很聪明的 Project”和“你敢让它参与真实客户决策的 Project”之间的区别。
## 让它运转起来的系统提示词
知识文件是大脑。系统提示词是性格和拒绝行为。没有它，你得到的只是一个客气的聊天机器人。有了它，它会在第一句话就告诉你，你的产品很弱。
每个运营官都运行在一个 4 部分的项目指令文件上：
原样照搬这个结构。按顾问不同换上特定专家的语气标记。完成。
下面这一周的计划就运行在这套架构之上。
## 1. 周一：定价重置（Hormozi 模式）
大多数服务业务的定价偏低 30-50%。Hormozi 模式用价值方程来审视我的产品：梦想结果、感知可能性、时间延迟、付出努力。它会告诉我产品的哪些部分流于平庸、缺少什么样的风险逆转，以及基于现有证据的价格底线是多少。
一次 30 分钟的对话，就能产出一个上限更高的打包产品。ChatGPT 会给你“可以考虑探索”。Hormozi 模式会告诉你先移动哪个杠杆。
## 2. 周二：六位数提案准备（Voss 模式）
你有没有遇到过那种情况：潜在客户对价格沉默不语，而你开始预先打折？我把需求挖掘的笔记放进 Voss 模式，找出我错过的校准问题。它会指出哪些情感标注和重复话术本可以揭示真正的异议，然后用战术性同理心重写跟进信息，而不是交易一有风吹草动就悄悄冒出来的那种道歉式语气。
## 3. 周三：销售管道节奏（Blount 模式）
每个服务业务都会遇到销售平台期。原因几乎总是同一个：销售漏斗顶端的活动量不足，却被包装成了战略问题。
Blount 模式会替你做那些你不想做的数学题。拨号数量、联系率、转化率、以及为了完成本季度目标你这周实际需要的新对话数量。它会直接点破那种伪装成客户开发的 LinkedIn 刷屏行为。
## 4. 周四：运营救火（Herold 模式）
如果你离开两周，收入就下滑 30%，那你面对的是组织设计问题，而不是营销问题。
Herold 模式像 COO 一样对待这门生意。它会诊断你在哪里仍然是瓶颈、下一个该招聘什么职位，以及设置什么样的筛选机制来避免招来又一个项目包袱。它与 Dan Martell 的回购原则搭配使用：买回来的时间投入到下一个瓶颈中。
## 5. 周五：现金流检查（Michalowicz 模式）
服务业务每天都在盈利中走向破产。Michalowicz 模式在服务业务损益表上运行“利润优先”机制：用真实数字进行真实的账户分配。它能揭示那些纸面上看起来健康、实际却存在利润流失的运营环节。
周五检查只需十五分钟。答案要么是“你没问题”，要么是“你其实只剩八周的生存期，而你并不知道”。
## 6. 周日：定位审计（Thompson 模式）
每个月一次，我会把细分市场、产品服务和理想客户投喂给 Thompson 模式，询问当前的战略定位是否真正具备复利效应。聚合理论、供给经济学、长期防御性。
大多数服务型企业的定位停留在三年前。市场变了，定位没变。Thompson 模式把我从“这一周”拉到“这一年”的视角。
## 打造你自己的（一位专家，一个下午）
你不需要一条流水线。你只需要一位专家和四个步骤，复刻上述架构：
- 策划：选出那位其方法论能解开你最大瓶颈的专家。定价？Hormozi。成交？Voss。现金流？Michalowicz。列出他们信号密度最高的 8-12 个视频和有声书。
- 转录：有字幕的 YouTube 视频直接拉取字幕。其余的用 MacWhisper 免费处理。一顿午饭前就能搞定大部分语料。
- 提炼：写两个 Markdown 文件：方法论框架（带命名、含数字模型）和语言风格（他们如何反驳、拒绝、排优先级）。
- 评估：创建一个 Claude Project，加载这两个文件，粘贴上面的 4 部分系统提示词。从过去 30 天的决策中写 6-10 个提示词，每个都配一份包含 3-4 条要点的“好的回答应该”清单。如果回答没覆盖清单要点，说明提炼得太薄。打磨后重跑。
这就是一位运营者。同样的四步配方再重复十二次即可。
## 结语
通用型 AI 会把所有专家平均化成一团礼貌的糊状物：从不出错，也永远不够有用到可以付诸行动。而一个专家模式只懂一位专家的方法论。当 ChatGPT 让你做 A/B 测试时，它会让你直接涨价。
13 位专家。4 种架构模式。一个运营者根据这一周遇到的情况在模式间切换。刻意保持无趣。专家储备就是护城河。而那个知道该运行哪个框架、何时运行、该拒绝什么的运营者，才是真正具备复利效应的资产。
你不需要更多建议。你需要一个运营者。今天下午就动手打造一个吧。
PS：如果你喜欢这类内容，你一定会爱上我的每周通讯。
每周我都会为你提供当天就能部署的工具、提示词和自动化方案，让 AI 在你的业务中转化为 ROI。
点击这里订阅（免费）。
## 相关链接
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*导出时间: 2026/4/26 11:53:05*
