Know a lot of AI tools, but don't know how to make money?
The answer is actually not that complicated.
Customers don't pay just because you know ChatGPT, Claude, Midjourney or Cursor. Tools are getting cheaper and prompt words are becoming less and less valuable.
What is really valuable is the results.
Help a customer sell more goods, spend less labor, improve efficiency, shorten delivery times, or solve a problem that he cannot solve on his own.
So one of the most important principles for AI to make money is:
Don't sell "I can AI", sell "What problems can I help you solve?"
1. AI content agency
Companies have always had a demand for articles, Short Video scripts, product descriptions, emails, and social media content.
AI can help you collect information, build structures, change titles, and generate first drafts, butFact checking, content selection and final expression still require people to be responsible。
Don't say "you can write about any industry", it's best to choose one field first:
- Catering: event copy, menu introduction
- Fitness: Course content, Short Video scripts
- E-commerce: product page, advertising copy
- Recruitment: JD, Employer Brand Content
Make 3 realistic samples first, and then go to the customer.
Fees can be charged per article, content package or monthly cooperation.
Case:
A gym only has a few course introductions. You organize it into public account articles, 3 Short Video scripts and friends 'circle copywriting. Customers buy one setContent that can be posted directly, not the AI generation process.
2. AI Short Video
The really difficult thing about Short Video is not generating pictures, but:
Topic selection → script → material → dubbing → subtitles → editing → release.
Local merchants, e-commerce brands, and knowledge bloggers usually do not have a complete content team. This is an opportunity.
Don't start by pursuing the goal of generating dozens of videos a day.
Make a complete sample first and ensure that:
- Picture is normal
- voice naturally
- Accurate subtitles
- Product does not deform
- Material has permission to use
Then upgrade from a single video to a monthly content service.
Case:
The dessert shop gives you product photos and production process, and you turn them into Short Video, which are then adapted to group buying pages and social media.
Customers are not buying "AI videos", butA set of usable marketing content。
3. AI visual design
Posters, product drawings, avatars, spatial renderings, picture books, and old photos can all be restored.
But AI mapping is only the first step.
What customers really need is:
Pictures that can be published, printed, and sold.
So in addition to generation, you also need to know:
- retouching
- typesetting
- color correction
- size fit
- Word processing
- Commercial material inspection
It's best to start with one category, such as:
Specializing in making menus and group buying covers for restaurants.
Or:
Specializing in helping real estate agents make spatial renderings.
Case:
Furniture merchants only have white-based sofas. You help them generate different decoration scenes and then accurately synthesize real products into them.
Being beautiful is just the first step,Accurate availability is the delivery standard。
4. translation and localization
AI can already make good first drafts of translations, so simply verbatim translation is getting cheaper and cheaper.
What is really valuable is:
Make the content not read like translation.
In particular:
- cross-border e-commerce
- game
- App
- SaaS
- product page
- user manual
It's not just language that needs to be dealt with, but also:
- the consistency
- tone
- Date and currency format
- Button length
- Local cultural expressions
If you understand a foreign language and are familiar with an industry, this path is a great way to go.
Case:
Translating the Chinese official website of domestic software into English does not translate sentence by sentence, but re-adjusts the title, buttons and marketing expressions to make it truly like an English product page.
5. AI data analysis
Many small companies do not have data, but:
There is data, but I don't know what the data means.
You can help them:
- Cleaning Excel
- disaggregated data
- Prepare report
- Analyze customer feedback
- Looking for exceptions
- Make operating reports
AI can help you process data, but ultimately you need someone to answer:
Why did it decline?
What types of customers declined?
Since when?
What should I do next?
Case:
When the refund rate of an online store increases, you analyze orders, products and after-sales data together, and finally find that the problem is concentrated in a certain size and a certain delivery channel.
What the boss is really willing to pay is:
"What is the problem and what to do next?"
6. AI customer service
Catering, e-commerce, beauty, and training institutions have a large number of repeated questions every day:
Prices, business hours, appointments, refunds, course arrangements...
You can help your company establish:
Knowledge base + AI customer service + manual transfer.
The key is not to access the model, but to organize the company's data clearly.
First find high-frequency questions from historical chat records, and then determine standard answers and manual rules.
During the test, deliberately ask some unanswered questions to see if AI would make things up.
Charges can be:
Construction fee + monthly maintenance fee.
Don't promise to "completely replace manual labor". Important issues must be reserved for manual labor to take over.
7. AI Automation
Many companies do it every day:
Copy, paste, organize, and send.
For example:
- message classification
- Form entry
- Weekly report generation
- customer follow-up
- document filing
- Inventory reminder
This is the automation opportunity.
Don't be a super Agent in the first place.
Follow the employee through the process first and find:
Links with high repetition, clear rules, and error-prone.
For example:
Form submission → automatic extraction of information → writing to form → generation of messages → manual confirmation.
A simple process that saves a few hours per day has commercial value.
Automation must be preserved:
Failure reminder + manual takeover + operation record.
8. Websites and AI widgets
AI programming allows ordinary people to do the same:
- enterprise official website
- event page
- Registration system
- quoting tool
- browser plug-in
- internal management tool
But:
Just because the code works doesn't mean the product can be delivered.
Customers are more concerned about:
- Is the mobile phone normal?
- Is data safe?
- Are the permissions correct?
- What should I do if something goes wrong?
- Who is responsible for maintenance?
So it's best for beginners to start with small projects.
For example:
Enterprise official website, quotation calculator, data query tool.
Don't just show screenshots in your portfolio, it's better to have a Demo that you can experience directly.
9. AI training and consulting
Many companies know that AI is important, but they don't:
How should you use your own employees?
Don't do a two-hour "AI Development Trends" PPT.
Teach directly with real work:
- write emails
- Make meeting minutes
- analysis Excel
- Organize customer data
- production content
After the training, ask everyone to take at least one:
AI workflows that can be used the next day.
The same goes for counseling.
First identify the one or two links that waste the most time for customers, and then provide solutions.
What you are selling is not AI knowledge.
What you sell is "making the team really use it."
10. Digital products and vertical SaaS
This is the one that is most easily misunderstood.
When many people think about making money by AI, they think:
"I want to make a SaaS!"
In fact, the more stable order is:
Services → Templates → Workflow → Products → SaaS
Serve real customers first, and you will find that many problems will be repeated.
For example:
- E-commerce repeatedly organizes product information
- Sales repeatedly collate customer leads
- Job seekers repeatedly revise resumes
- The company makes repeated reports every week
When a problem is verified repeatedly, make it a product.
Don't sell a bunch of Tips in the first place.
What is really valuable is:
Template + process + case + checklist + usage method.
These 10 methods essentially only have 3 types of business
① One-time delivery
Content, video, design, translation, data reporting.
Features:
Do it once, collect money once.
The advantage is that it is easy to start, and the disadvantage is that it relies more on time.
② Continuous service
AI customer service, automation, monthly content, data signage, consulting and running.
Features:
The first time it was built, and the subsequent charges were continued.
Income is more stable, but long-term maintenance responsibilities are required.
③ Repeatable sales
Templates, courses, plug-ins, workflows, SaaS.
Features:
Production once and sales multiple times.
The ability to scale is the strongest, but real needs need to be verified in the early stage.
The most recommended path
If you have no idea where to start now:
Provide services first, then long-term cooperation, and finally product production.
For example:
Help companies make data reports
↓
Discover the same needs every month
↓
Make a standard template
↓
Become a monthly service
↓
automated processing
↓
and ultimately become a tool.
The biggest benefits of doing this are:
The product is not what you think of, but what the customer tells you with money.
How to determine if a direction is worth doing?
Keep these 5 questions in mind:
1. Does someone spend money?
It's much easier to have an old need on a budget than to create a new need.
2. Can the results be accepted?
Articles can be published, videos can be posted, and the system can be run, all of which are easy to price.
3. Can you find customers?
The closer you are to the customer, the easier it is to find the real needs.
4. Is the cost of error high?
Newbies give priority to projects that are low-risk, reviewable, and easy to roll back.
5. Can you repeat it?
If you start from scratch every time, you are still selling time.
If you can precipitate templates, SOPs, materials, and automated processes, you will start to own your own business assets.
How does the first order come from?
In fact, it only takes 4 steps.
Step 1: Explain what problem you are solving.
Don't say:
"I know AI."
Say:
"I will help you compress your 5 hours of reporting work per week to 1 hour."
Step 2: Make a sample.
Don't wait for the customer to come to start.
First make a Demo that is close to the real needs.
Step 3: Take the initiative to find customers.
A friend's company, local merchants, industry groups, freelance platforms, and content platforms are all available.
Don't tell customers a bunch of AI tools.
Tell him directly:
I found a problem that I can help you solve.
Step 4: Small orders first, then long-term.
You can diagnose it for free, but don't provide it for free.
The purpose of the first order is not just to make money, but also to obtain:
Case + feedback + word-of-mouth + reintroduction.
Finally, remember these 4 things
1. Don't learn ten AI tools at the same time.
Find the needs first, then learn the tools.
2. Don't hand over AI manuscripts directly to customers.
If you receive the money, you will be responsible for the results.
3. Don't believe that "AI guarantees hundreds of thousands of monthly income."
What really determines revenue is customers, demand, delivery and sales.
4. Don't start with a platform.
Solve one person's problem first.
Solve similar problems for ten people.
Finally make it into a product.
There is no mysterious entrance to AI making money
In the end, ordinary people don't need hidden secrets to make money from AI.
You just need to find one thing:
A job someone would have paid for anyway.
Then use AI to help you:
Do it faster.
Do it more steadily.
Make it cheaper.
Finally, be responsible for the results.
This is the most realistic way for AI to make money.
Don't sell AI, sell results.
Don't chase tools, chase needs.
Don't rush to make products, find customers first.
The first amount of money is often hidden in a specific problem.