AI Use Cases - Business Ideas#

Business ideas#

Every time a new technology emerges, new opportunities open up. As the internet spread, smartphones arrived, and Bitcoin gained traction, the industrial landscape shifted and new winners appeared. Now we’re in the era of AI. There are many opportunities, and I believe it’s a better time than ever for individuals to take a shot.

One of the most important elements of economic activity is capital, and capital moves toward where it sees value. Many people worry that “AI will replace humans,” but more precisely, AI replaces or reshapes parts of what humans used to do, changing the “shape of work.” In that flow, the people who create something valuable will be the ones who attract capital.

The end-to-end process—coming up with a business idea, planning and building a product, launching it, running marketing, analyzing data, and improving the product—has become far more accessible than before. There are more services for each step, and AI services act as guides throughout the journey. To help with the very first step—generating business ideas—I’ll share the ideas I’ve organized below.

Here are the assumptions I used while整理ing these ideas:

AI can understand a user’s request in natural language and generate responses in natural language.
AI is strong at translation, and compared to dedicated translation APIs, it often has more freedom to adjust context, tone, and format together.
AI can generate images / videos in natural language, and can partially edit images / videos based on the input, but there are still constraints.
AI can understand and analyze images.
AI can perform Text-to-Speech and Speech-to-Text.
AI can generate music in natural language. However, for music generation, whether an API exists and the quality varies widely by service/model, and copyright/licensing issues must be considered. For now, it may be more realistic to generate music via online services and use it as a resource.
AI is trained on massive amounts of data and can answer many questions, but it does not know non-public information and can hallucinate in convincing ways.
Ways to deal with information AI does not know include RAG, fine-tuning, and Function Calling.
It’s important to provide AI with context effectively.
There are many AI models, and each has its own characteristics.
Many AI models are released as open source, and derived models continue to be published.
High-performance models like GPT, Gemini, and Claude are also available via APIs.
Even at this moment, services that solve various needs using AI are being released nonstop.

Identify and leverage your assets#

When generating business ideas, it’s important not only to imagine “What might take off?” but also to carefully examine what assets you already have and how you can leverage them. Here, “assets” can be tangible (a business, products, inventory, space, equipment, distribution channels, etc.) or intangible (knowledge, experience, content, network, trust, personal skills, character/taste, repeatable operational know-how, etc.). It may sound “obvious,” but there’s a big difference between ideating with vague assumptions in your head and writing your assets down and explicitly checking strengths, constraints, and scalability. It makes it much clearer whether an idea is grounded in reality, how hard it is to execute, what the initial costs are, and what value you can deliver to whom.

Tangible assets are relatively easy to identify and analyze. If you already run a business, you can directly ask: “How can I apply AI to this business to improve performance?” For example, if you run a farm, you might consider demand forecasting, optimizing work schedules, or automating customer support / order processing. If you run a restaurant, you can look for automation opportunities in menu composition, cost management, review analysis, and reservation handling. If you sell products, you can go further: producing content that makes the product look more attractive, generating marketing copy by target segment, running ad experiments (creative A/B tests), responding to customer inquiries, and extracting improvement points from reviews. The key is not “let’s do something with AI,” but finding the application points based on where the bottleneck is in your existing structure (channel, customers, product, operating 방식).

Intangible assets are harder to identify and analyze. Because it’s less obvious “how they can be used,” you often need to intentionally pull them out and shape them into something concrete. If you teach, you can think about how to reach more customers (content repackaging, expanding distribution channels, messages by customer segment) and how to create better materials with less effort (draft generation, examples/exercises generation, automatic updates). Or if you wanted to be a YouTuber but gave up because filming/editing felt too heavy, you can consider trying again by using AI directly or by relying on AI-based services to split the work across planning, scripting, thumbnails, and editing. In other words, intangible assets become more valuable when you find the points where AI reduces the “execution barrier.”

In many cases, starting with existing assets puts you in a better position than starting from scratch with a completely new idea. “Assets” already embed some value and traces of validation, while a new idea is not yet verified as having real value. So when thinking about business ideas, it can be effective to first write down your assets concretely and narrow down realistic ideas based on “What problem can this asset solve for whom?” and “How much can AI improve cost / time / quality?”

Creating digital content#

If you don’t have clear tangible assets you can leverage in a business (a business, a product, space, equipment, etc.), a realistic business approach for an individual is creating digital content. Digital content often requires little initial capital, can be distributed repeatedly once created, and can keep generating value. Above all, as AI advances, the barrier to “having the ability to create content” is dropping rapidly, making this an increasingly favorable option for individuals.

Digital content comes in many forms. It includes traditional formats like writing, illustrations, and video, but also “software as content” such as applications and games. If you create and distribute writing, you become a blogger. If you create and distribute illustrations, you become an illustrator (or creator). If you create and distribute videos, you become a YouTuber. And if you create and distribute applications or games, you become a developer (or a solo product maker). But today, rather than locking in a role first—“I’ll be a blogger / YouTuber / developer”—it can make more sense to start by deciding what kind of content you want to create. As AI begins to assist in writing, image generation, video editing, and code generation, large parts of content production are shifting from “full mastery” to a process of “good instructions + iterative refinement.”

Of course, there are still technical limitations. For example, perfectly 구현ing the exact details in your head is still difficult, and pushing quality beyond a certain level usually requires human judgment and edits. But what matters is not the “current position” of those limitations, but how fast they’re being broken. Work that used to take hours now yields a draft in minutes, and results that once required experts are increasingly possible for individuals at a decent baseline. And this change may arrive faster than we expect.

So the strategy at this point is simple: pick a topic you care about (or a problem you want to solve), choose a content format that fits it, and use AI to create something small, publish it quickly, observe the response, and improve in a loop. If you build experience early, you can catch great opportunities at a much better timing as the technology keeps improving. Becoming “someone who can create content” can be a powerful starting point even for individuals without tangible assets.

Aggregating, processing, and republishing information#

One area where AI is particularly strong is extracting the main content from web pages, identifying what matters, summarizing it, and restructuring it. In the past, to pull the data you needed from a specific site, you had to build a crawler, keep fixing it whenever the page structure changed, and manually refine the summary text. Now it’s much easier to handle requests like “extract the title/body/price/table from this page” or “summarize only the shared 핵심 across these articles in 10 lines,” and it’s also possible to automate those tasks and run them repeatedly. In other words, the cost of collecting source information → processing it → republishing it in a more readable form has dropped dramatically.

This pattern resembles how internet services have evolved in the past. After e-commerce became widespread, “price comparison sites” emerged. After blogging became widespread, “meta-blog” sites appeared. The common point is that they didn’t simply copy what original producers (shops, bloggers) created; they aggregated information across sources and processed it into a form that helped users decide faster. In that sense, businesses that “collect, process, and enable more valuable consumption of information” have repeatedly shown a path to success. AI significantly boosts productivity in exactly this processing step (classification, summarization, comparison, perspective structuring, formatting).

You can also see demand signals in everyday life. In group chats, you may have seen people share “today’s news summary” every morning. In many cases, the summary isn’t something they wrote themselves—it’s something they received elsewhere and re-shared. Yet people still consume it consistently. That suggests news summaries are valuable content on their own. And AI is exceptionally good at producing such summary data: extracting overlapping facts and issues across articles, organizing them in a quick-to-scan format, splitting them by perspectives, and rewriting them in a desired tone (neutral/friendly/business) in one go.

To turn this idea into a business, it’s important to go beyond “just summaries” and clarify “summaries for whom.” For example, you can create a morning briefing focused on a particular industry (games/e-commerce/real estate), or highlight decision-relevant points from the viewpoint of a specific role (CEO/marketer/developer). If you also keep basic principles—source citation and copyright—and provide links/original references/evidence, trust increases as well. Ultimately, the key is not using AI as a “replacement writer,” but designing it as a system that automates aggregation and processing to republish consistently.

Building learning or counseling assistants#

Modern AI is close to a “universal expert” that can propose answers to most questions—except for what it truly does not know—and it is also a language genius that can handle well over a hundred languages. Beyond understanding and generating text, it excels at translation and tasks that convert language across modalities, such as Speech-to-Text (STT) and Text-to-Speech (TTS). This combination enables software experiences that feel like “a teacher is always by your side.”

For example, an English learning service may no longer require a human English teacher in every case. It can take a user’s spoken sentence and correct grammar/pronunciation/naturalness, rewrite the same meaning in a more formal / more casual / business email tone, and even run role-playing conversations as a situational dialogue. It can adapt word difficulty to the user’s level and offer personalized review, like “make a quiz using the 10 expressions I learned today.” If you add STT/TTS, you can automate the “listen → speak → get feedback → repeat” loop, making it possible for small teams or individuals to quickly build and test educational content / learning apps.

Another direction is a “counseling assistant.” I’ve seen cases where someone used AI for 고민 counseling and experienced real, practical benefits. AI’s strengths include not getting tired, maintaining context by summarizing the user’s statements, and helping the user organize thoughts through questions. It can more consistently apply “conversation techniques,” such as separating emotions from facts, checking assumptions, and expanding options through structured inquiry. It can be especially useful when you don’t have someone to talk to immediately, or when you want to put 반복 thoughts into writing to clarify them.

However, counseling requires caution. AI can be convincingly wrong, cannot accurately diagnose a person’s condition, and it can be dangerous to handle situations that require professional intervention (depression/anxiety, self-harm thoughts, violence/abuse, addiction, etc.) too lightly. If you build this as a service, you should clearly state that “this is not medical/psychological treatment,” provide guidance for crisis situations, define privacy principles, and clarify the scope of responsibility for responses. With these safety measures, AI-based learning/counseling assistants can become a strong content/service format worth exploring for individuals and small teams.

Building expert chatbots for a specific domain#

We now live in a world where it’s very easy to build chatbot services using the APIs provided by AI companies. In the past, building conversational services meant training NLP models yourself or writing detailed rule-based scenarios. Now you can quickly build a prototype with “a chat model + prompts + a bit of backend.” The important question is not “Can you build a chatbot?” but how you create a reason for users to use your chatbot instead of ChatGPT or Gemini.

The key is to clearly compensate for what general-purpose models do poorly in a specific domain. For that, you can actively use RAG and Function Calling. RAG can retrieve internal materials—your company documents, manuals, policies, and knowledge base—and attach them as grounding. Function Calling can query databases or integrate external systems to perform “real work” and answer based on actual results. In other words, you borrow the fluency of a general model while using your unique data and functions to raise accuracy and practicality. No matter how convenient a department store is, when people want a special bread, they’ll line up for a small, worn-looking bakery with a hundred-year tradition. Domain expert chatbots work the same way: if you solve a specific problem faster and more accurately than a general service, users will choose you.

The service I’m developing at my company is an example of this approach. It’s an expert chatbot that helps with advertising analysis and execution, and it integrates multiple functions on top of a proprietary database. For example, it can pull campaign performance under specific conditions, analyze the cause, and suggest the next actions (e.g., budget adjustments, targeting changes, creative tests) in a single flow. This combination of “internal data + execution functions” is difficult for general services like ChatGPT or Gemini to provide as-is, and users receive not just advice but outcomes that actually move the work forward.

Another strong idea is a customer support chatbot focused on Voice of the Customer (VoC). Beyond accurately answering product questions and reducing repetitive FAQs, it can structure and store complaints/opinions, and notify the right owner when certain keywords (e.g., delivery delays, refunds, product defects) exceed a threshold. It can also summarize support logs into a CRM and auto-classify recurring issues into improvement tasks. In the end, the competitiveness of an expert chatbot is not the “conversation” itself, but delivering an experience that handles domain information most accurately and completes the necessary work end-to-end.