Without this set of logic, your "lobsters" are doomed to die.

Since the Spring Festival until today, the hottest term in the Internet circle is no longer large models or computing power, but "raising lobsters".

This "lobster" is called OpenClaw in English. Its icon is a red Boston lobster, so netizens call the process of tinkering with it "raising lobsters".

Ma Huateng posted a WeChat Moments message early yesterday morning, saying, "I didn't expect it to be so popular." Lei Jun posted three consecutive Weibo posts about something called "Xiaomi Lobster" (Miclaw). At the entrance of Tencent's headquarters, there was a long queue of people waiting to install the "lobster".

Why is it so popular? Because people suddenly found that AI has finally "crawled" out of the chat box that could only chat and started to really do work for you - writing documents, adjusting data, running processes, and even helping you monitor the stock market and trade stocks.

But here comes the problem. Many bosses are eager to jump in and ask: How can I buy this thing? How do I use it after buying? Can my employees learn it right away and double their efficiency tomorrow?

The answer is: No.

Because tools can be bought, but "people who can use tools" and "organizations that can use tools" are nurtured.

Today, we won't teach you how to configure and deploy the "lobster". We'll just talk to you about how to turn your organization into a real "AI - enhanced organization".

1. Three - step implementation: First, have the right people, then make changes

Many companies like to do one thing when promoting AI: conduct training.

They gather employees in a meeting room, invite a lecturer to teach for two days, issue a completion certificate, and that's it. What's the result? The day the training ends is the day the use of AI ends.

Why? Because training only solves the problem of "knowing", not "being able to use", and even less "daring to use". AI is not learned, but "tinkered" with.

Step 1: Incubation. First, find a few "lobster players"

The first thing you need to do is to select a few people from the company. They can be from technology, product, or operations. The only criterion is: having enthusiasm for AI, loving to tinker, and getting itchy hands when seeing new tools.

These people form an "AI innovation group" directly managed by the boss.

They specifically do three things:

First, explore new tools. They need to figure out right away: How exactly does this thing work? Can it be connected to DingTalk or Feishu? Can it access the data in our CRM (Customer Relationship Management software)? They need to know these things earlier than others outside the company.

Second, conduct technical breakthroughs. When the sales department says, "The email responses written by AI are too fake, like a robot," they need to adjust the prompts to make them more human - like. When the HR department says, "AI's resume screening is inaccurate," they need to optimize the "skill package" (called Skill in OpenClaw) for screening.

Third, set security standards. Just two days ago, the Ministry of Industry and Information Technology issued a warning: If OpenClaw is not configured properly, it can easily lead to cyber - attacks and information leakage. They need to set the rules on who can access what data, who can use what functions, and which operations are in violation.

In terms of the mechanism, there should be "monthly exams" + "subsidies". Have an exam every month and replace those who are not suitable. Give a subsidy of 1,000 - 2,000 yuan per month. The money is not much, but the signal is strong: The company cares about this, and you are doing something valuable.

Step 2: Empowerment. Each department "raises a lobster"

Of course, many things can't be done just by the above "AI innovation group".

How can sales use AI to write follow - up emails to increase the response rate? How can the marketing department use AI to analyze competitors to find differentiated selling points? How can the finance department use AI to reconcile accounts to detect anomalies?

The technical group can't answer these questions. The answers lie with the business departments themselves.

You can select 1 - 2 key employees who understand the business and are interested in AI from departments such as sales, marketing, HR, and R & D, and appoint them as "AI evangelists".

Their task is to translate business pain points into technical requirements. For example, the sales director used to worry about the low email response rate every day. Now, he should be able to clearly say, "I hope AI can help me analyze the customer profile and then automatically generate follow - up emails in different styles."

They also specifically do three things:

First, discover scenarios. Where can AI be used in the sales department? Where can it be used in the HR department? They need to conduct on - site research to find those repetitive, time - consuming, and regular jobs.

Second, put forward requirements. They don't need to know the specific technical details, but they need to tell the technical group, "I hope AI can do this thing to solve this problem for me." The technical group will make adaptations according to the instructions and turn business requirements into automated workflows.

Third, teach colleagues. After the technical group has paved the way and put the tools at hand, who will teach front - line salespeople how to use them? It's still these business - savvy evangelists. They can translate technical language into plain language: "From now on, as long as you click this button, AI will analyze the customers for you."

In terms of incentives, it is also recommended to give subsidies. They are the bridge between technology and business and are the new middle - management in the AI era.

After OpenClaw became popular, many securities firms did just that. The financial engineering teams of Founder Securities and GF Securities specifically wrote dozens of pages of research reports to teach analysts how to "raise lobsters" to assist in stock selection and write financial report analyses. Those analysts who understand both finance and AI have suddenly become very popular.

Step 3: Internalization. Make "raising lobsters" part of the company culture

Now, the AI innovation group has in - depth business knowledge, and the "AI evangelists" have a wide range of business knowledge. It is natural to promote it to all employees.

Why? Because the path has been paved, the translation has been done, and all that's left is to get everyone on board.

Hard indicators should be strict. New employees must pass the AI training exam when they join the company. The exam questions are automatically generated by AI based on the training content. This is itself an example of AI application. Conduct an AI ability assessment every quarter. If a department is weak in this regard, provide targeted training.

Soft culture should be soft. Set up an "AI application award". Those who use AI well, share their experiences, get bonuses, and are given priority for promotion. Those who do well share their experiences, and those who do poorly receive "one - on - one assistance" to help them improve.

You can see from the actions of large companies. Alibaba adjusted its organizational structure, reduced the number of partners, and focused on AI investment. ByteDance reorganized its Seed team and adjusted its AI R & D strength.

Yesterday, Tencent not only launched its own OpenClaw - like product, WorkBuddy, but also connected QQ and WeChat, allowing users to directly command AI to work in the chat box.

All these signals are saying one thing: AI is not a project, but a strategy.

Let's summarize the logic of these three steps:

Step 1: Find "people who love to tinker with technology" to understand the "temper" of AI. This is from 0 to 1.

Step 2: Find "people who understand business pain points" to translate business requirements to technology. This is from 1 to 100.

Step 3: When technology and business are both ready, get all employees on board. This is from 100 to N.

After these three steps, your organization will no longer be just an "organization that has bought AI tools", but a real AI - enhanced organization.

2. Three - layer architecture to build a system

After getting the right people, you also need a system.

Many people ask what the difference is between OpenClaw and ChatGPT. For example: ChatGPT is like asking a remote consultant online, while OpenClaw is like hiring a 24 - hour live - in assistant.

The requirements for a "home" are completely different for a live - in assistant and a remote consultant.

Layer 1: Security and compliance. Build a "cage" for AI

Why build a "cage"? Because this "assistant" has too much authority. It can read and write your files, execute system commands, and access your database. If it is exploited by bad people or you feed it inappropriate data, the consequences will be unimaginable.

Also, many companies allow employees to use public AI tools with their personal accounts. Financial data, core codes, and customer information may flow to public models through employees' personal accounts, resulting in the leakage of company secrets.

What to do? You can deploy OpenClaw locally and privately, and the data can be stored on the company's own servers or virtual machines. You need an AI workbench with clear permissions and data isolation. Especially in industries sensitive to data such as finance and government affairs, private deployment is almost a must - have option.

Layer 2: Knowledge access. Install a "brain" for AI

There is a problem with public models like DeepSeek, Doubao, and Qianwen: They don't understand you.

If you ask it, "Why did our company's sales volume decline in Q3 last year?", it can't answer. If you ask it, "What are the most frequently complained - about product problems by customers?", it also doesn't know. Because it doesn't have your knowledge base.

You need to feed the company's knowledge base, customer cases, and even failure lessons to AI.

OpenClaw has a very clever design: It uses three files to manage AI's behavior - SOUL.md (personality), MEMORY.md (memory), and AGENTS.md (behavior norms). By modifying these files, you can make AI a real AI that understands your business, rather than a general large model.

This work is not a one - time thing. The knowledge base needs to be continuously updated, and the "AI innovation group" mentioned earlier can be responsible for this task.

Layer 3: Automated processes. Give AI "hands and feet"

AI can't just stay in the chat box. It should be able to access CRM data, issue instructions to ERP, and automatically create a group in DingTalk or Feishu.

The breakthrough of OpenClaw lies here. Through the "skill package" (Skill) and MCP (context) protocol, it can integrate and work with more than 50 platforms.

For example: When a salesperson receives a customer's inquiry, AI automatically retrieves the customer's historical data from CRM (Customer Relationship Management software), generates a quotation, sends it to the approval process, and automatically replies to the customer by email after approval. In the whole process, AI is the executor, and humans are the supervisors.

This is what an AI - enhanced organization is.

3. Avoid two pitfalls

Well, now both people and the system are in place, but there is one thing that can't be ignored: mindset.

1. Copy - and - paste approach

Some companies think that AI tools can be used as soon as they are bought. It's like buying a photocopier. Just plug it in, and employees will naturally know how to use it.

That's completely wrong.

After OpenClaw became popular, what's the most ironic phenomenon? There are a bunch of "on - site lobster - raising" services on Xianyu and Xiaohongshu - 50 - 100 yuan for remote installation, 500 yuan for on - site installation once. Some even put up signs like "Guaranteed to teach you, 24/7 support, and offer cooking services" to compete.

Why? Because many people find that they can't install it after struggling for a long time, or they don't know how to use it after installation. It's better to pay someone to solve the problem.

A netizen left a message on the Alibaba Cloud Developer Community, saying that after installing OpenClaw, he installed hundreds of skills from ClawHub (the official skill library) at once. As a result, the system ran slower and slower, the functions conflicted with each other, and finally AI kept reporting errors, and he had to reinstall everything.

This is a typical "copy - and - paste approach" - having the tool but not knowing how to "tame" it.

What does this show? It shows that even if it's "zero - threshold", it only solves the "installation" problem. The subsequent "taming" and "continuous evolution" are the real values.

Each company's processes, culture, and knowledge bases are different. AI tools must be adapted to be effective.

So, the copy - and - paste approach won't work. AI tools need to be adapted, which takes time and continuous investment from the AI innovation group and evangelists. The time you invest is your company's moat.

2. The boss doesn't take charge personally

The AI strategy must be a top - down project. Many people have heard this, but they may not understand why.

As mentioned earlier, Ma Huateng posted a WeChat Moments message, and Lei Jun posted three consecutive Weibo posts.

On March 7th, Gao Wen, a deputy to the National People's Congress and an academician of the Chinese Academy of Engineering, said at a group meeting of the Guangdong delegation, "Now everyone is extremely anxious, afraid of not being able to 'raise a lobster'."

On the same day, Longgang District in Shenzhen quickly introduced the "Ten Measures for Lobsters" - encouraging the establishment of "lobster service areas" for free deployment, giving a maximum subsidy of