{"content_id":"aesecc3chi","slug":"ai-era-survival-skills-3","locale":"en","schema_type":"Article","category":"ai_data","category_name":"AI Data","title":"3 Skills That Will Keep People from Being Replaced in the Age of AI","summary":"In the age of AI, competitiveness does not come from simply knowing how to use tools or engaging in automated side jobs, but rather from deeply integrating AI into one’s work and accumulating unique data and judgment. In particular, the ability to formulate questions, a critical mindset, and insight that combines experience and intuition are what make one irreplaceable.","key_points":["Content on AI automation and monetization can serve as a reference for understanding technological trends, but it is not a sustainable competitive advantage in and of itself.","As generative AI becomes more widespread, what sets organizations apart is not access to models, but the unique data and context that individuals and organizations possess.","While some of the coordination and reporting tasks performed by mid-level managers can be replaced by AI agents, the ability to make responsible judgments and manage people remains crucial.","In the age of AI, creativity extends beyond the ability to create something from nothing to include an aesthetic sensibility that identifies and refines the best options among those generated by AI.","An irreplaceable person is someone who possesses a refined way with words, a keen sense of social issues, and the experience and intuition gained on the ground."],"content_markdown":"## Conclusion at a Glance\n\nIn the age of AI, the people who make money and survive aren’t simply those who know a lot about the latest AI tools. What’s more important is accurately defining the problems in your work, building up unique data and context that AI can utilize, and developing the discernment to evaluate the results.\n\nYouTube and online courses are full of messages claiming that you can work just 30 minutes a day with AI and make a fortune. While some do feature real-life examples of productivity gains, a significant number are essentially just selling content that teaches how to make money with AI. Therefore, the key question is this: It’s not about what you can automate with AI, but rather which bottlenecks in your work you will solve with AI.\n\n## 1. Breaking Free from the Illusion of AI Automation Profits\n\n### The Competitive Edge of Automation-Based Side Hustles Doesn’t Last Long\n\nAfter generative AI became mainstream, monetization methods such as automated text generation, image generation, short video production, automated blog posting, and chatbot operation spread rapidly. However, in fields where anyone can use the same tools, competition quickly becomes overheated. If the output is low-quality or repetitive, it’s easy to be shunned by platforms, search engines, and users alike.\n\nGoogle Search Central explains that mass-generated low-quality content or “scaled content abuse” intended to manipulate search rankings are subject to its spam policies. In other words, it is not the fact that AI was used that is risky, but rather the practice of mass-producing content that offers no real value to people.\n\n### AI Is Not a Side Hustle Generator, but a Productivity Engine\n\nSustainable revenue comes not from AI tools themselves, but from increasing the efficiency of existing work and businesses.\n\n| Approach | Short-Term Appeal | Long-Term Limitations | Better Direction |\n|---|---:|---|---|\n| Mass-producing automated blog posts and Shorts with AI | Easy to get started | Low differentiation and difficult quality control | Producing content that includes unique experiences, data, and verification |\n| Selling prompt collections | Can be created quickly | Vulnerable to changes in tools | Refining into templates that solve specific job or industry problems |\n| Automating simple, repetitive tasks | Immediate time savings | Anyone can replicate it | Systematizing work by integrating with the organization’s data and processes |\n| Integrating AI into Core Business Operations | Initial training required | Implementation can be challenging | Potential for sustainable cost savings and quality improvements |\n\nFor example, a restaurant owner can use AI not only to write menu descriptions but also to analyze inventory records, seasonal sales figures, review data, and local event information to adjust order volumes and promotions. Planners can go beyond simply having AI generate summaries; they can input past proposals and examples of failed projects to improve the quality of their decision-making.\n\n## 2. Your True Weapons Are Your Own Data and Insight\n\n### When Everyone Uses Powerful AI, the Difference Lies in the Data\n\nHigh-performance AI services like ChatGPT, Claude, and Gemini are accessible to many people simply by paying a subscription fee. The era when access to these models alone was a scarce competitive advantage is rapidly coming to an end. Even when using the same AI, results vary because the context, data, criteria, and feedback provided by users differ.\n\nExamples of personal and organizational data that can be provided to AI include the following:\n\n- Past planning documents, reports, proposals, and meeting minutes\n- Customer inquiries, reviews, and consultation records\n- Personal notes, idea journals, and reading logs\n- Root cause analysis reports for failed projects\n- Work checklists and decision-making criteria\n- On-site work videos, observation logs, and know-how documents\n\nOrganizing this material creates a personal wiki or organizational knowledge base. It transforms the data into an asset that is not merely stored but is searchable, summarizable, and recombinable.\n\n### Practical Ways to Create a Personal Wiki\n\nA personal wiki doesn’t have to be a complex system. The key is to gather scattered records and organize them into a structure that AI can understand.\n\n1. Gather materials: Consolidate notes from note-taking apps, cloud documents, emails, local files, and screenshots into one place.\n2. Record dates and sources: You must be able to trace the basis for the AI’s summaries later on.\n3. Add topic tags: Keep tags simple—such as “customers,” “products,” “ideas,” “failures,” “lessons learned,” and “recurring tasks.”\n4. Remove sensitive information: Delete or anonymize personal information, contractual secrets, and customer identifiers.\n5. Create a list of questions: Prepare in advance what you want to ask the AI.\n\nHere are some example questions:\n\n- What are the recurring strengths and weaknesses in my proposals over the past three years?\n- Which types of customer complaints are most likely to result in actual revenue loss?\n- What common patterns do the tasks I frequently procrastinate on share?\n- What are the differences in decision-making between successful and failed projects?\n\n### Insight: Quantifying Tacit Knowledge and Judgment Criteria\n\nGenerally, “tacit knowledge” is translated into Korean as “암묵지.” It refers to knowledge that is difficult to fully explain in writing or through manuals but is instinctively understood by skilled practitioners. The “insight” discussed here refers specifically to the sense—within tacit knowledge—that distinguishes what is good from what is awkward and determines which choices are appropriate for the situation.\n\nFor example, the following types of knowledge are difficult to express using only numbers or sentences:\n\n- A designer’s intuition that the white space on a screen looks awkward\n- A chef’s ability to judge the degree of doneness using their fingertips and sense of smell\n- A salesperson’s intuition for detecting a customer’s hesitation in the flow of conversation\n- An editor’s ability to recognize the rhythm of a sentence and identify points where readers might lose interest\n- A field manager’s ability to intuitively sense a work workflow that could lead to an accident\n\nWhile AI can learn from vast amounts of text and images, it cannot automatically acquire the on-the-ground intuition accumulated by specific organizations and individuals. Therefore, future competitiveness depends on how well we document our tacit knowledge, illustrate it with examples, and transform it into feedback data.\n\n## 3. The Workplace Will Be Reorganized Around Work Units\n\n### Work Units Will Change Before Entire Occupations Do\n\nIt is difficult to conclude that AI will eliminate entire occupations. In reality, change will first occur at the level of the work units that make up a job. Tasks with a high degree of predictability—such as data research, drafting, schedule coordination, summarization, categorization, and report formatting—are highly likely to be automated quickly. On the other hand, human capabilities remain crucial for responsible decision-making, conflict resolution, on-site judgment, ethical judgment, and building trust through face-to-face interactions.\n\nThe World Economic Forum’s *Future of Jobs Report 2025* predicts that technological change, the green transition, and shifts in economic structure will simultaneously create new jobs and replace existing ones. The key point is not the simple conclusion that jobs will disappear, but rather that the required skill sets are changing rapidly.\n\n### The Crisis and Redefinition of Middle Management\n\nMiddle managers have traditionally been responsible for breaking down goals into tasks, assigning them to people, monitoring progress, and reporting to upper management. Some of these tasks fall within the scope of what AI agents and automated systems can handle effectively.\n\n| Middle Management Tasks | Likelihood of AI Replacement | Value That Humans Must Retain |\n|---|---:|---|\n| Scheduling and status reporting | High | Interpreting critical delay signals and determining priorities |\n| Assigning repetitive tasks | Medium to high | Assignments that consider individual development stages and team dynamics |\n| Compiling performance data | High | Assessing context that numbers cannot explain |\n| Writing meeting minutes and summaries | High | Identifying the core of debates and determining accountability |\n| Strategic decision-making | Low to medium | Judgments involving uncertainty, ethics, and accountability |\n| Managing organizational culture | Low | Building trust, motivation, and psychological safety |\n\nTherefore, the future of middle managers is less about disappearance and more about redefinition. Roles limited to mere messengers and report managers are becoming obsolete, while “orchestrator”-type managers—who structure problems and coordinate people and AI—are becoming increasingly important.\n\n### The Value of Junior Employees Won’t Disappear, but Their Roles Will Change\n\nAs AI takes over drafting and research tasks, traditional training opportunities for new hires and junior employees may decrease. At the same time, new opportunities are emerging for junior employees. This is because the ability to quickly review an AI-generated “80-point” draft, incorporate on-the-spot feedback, and refine it by adding a sense of current trends will become increasingly important.\n\nHowever, it’s difficult to assume that the value of junior staff will automatically skyrocket. The key lies in whether companies position junior staff not merely as support personnel, but as experimenters and field data collectors who work alongside AI. Junior staff, too, must grow not as people who simply submit AI-generated output as-is, but as individuals who identify errors and provide contextual context.\n\n## 4. Three Competencies for Thriving in the AI Era\n\n### 1) Prompting Skills: Designing Questions That Unlock AI’s Potential\n\nA prompt is not merely a command. A good prompt includes the objective, context, constraints, criteria, examples, and output format. Since AI constructs answers based on the conditions provided by the user, ambiguous questions lead to ambiguous answers.\n\nThe components of a good prompt are as follows.\n\n| Component | Description | Example |\n|---|---|---|\n| Objective | Clearly specify what you want to achieve | Categorize the causes of customer churn into 5 categories |\n| Context | Describe the industry, target audience, and situation | It’s a beauty subscription service targeting women in their 20s |\n| Data | Provide data for analysis | Base your analysis on 300 reviews from the past 6 months |\n| Criteria | Outline the criteria for a good answer | Evaluate in order of feasibility, cost, and effectiveness |\n| Constraints | Specify conditions to avoid | Do not infer personal information, and avoid unfounded assumptions |\n| Format | Specify the structure of the output | Organize the results into tables and prioritized lists |\n\nA bad prompt demands an answer. A good prompt designs a thought process.\n\n### 2) Problem Awareness: The Sense of Turning Inconvenience into an Opportunity for Automation\n\nPeople who use AI effectively are generally sensitive to inconvenience. Here, “complaints” refer not to emotional frustration but to the ability to detect problems.\n\n- Why is this report written the same way every week?\n- Why do people have to read customer inquiries from scratch every time?\n- Why are the decisions and responsible parties unclear after a meeting ends?\n- Why are new employee training materials always delivered only verbally?\n- Why are failure cases not documented and simply forgotten?\n\nThese questions are the starting point for AI adoption. Rather than looking for technology first, you must first identify recurring inefficiencies and bottlenecks, and then determine whether AI is the right solution.\n\n### 3) The Combination of Experience and Intuition: The Discerning Eye for Selecting AI Outputs\n\nAI rapidly generates many options. However, as the number of options increases, the ability to choose becomes an even more critical skill. The discernment needed to distinguish between good results and merely plausible ones stems from a combination of experience and intuition.\n\nIf you have only experience but fail to read trends, you may become trapped in outdated judgments. Conversely, if you have only intuition but lack experience, you may make choices that seem plausible but are unfeasible. Decision-makers in the AI era must possess both.\n\n| Competency | Meaning in the Past | Meaning in the AI Era |\n|---|---|---|\n| Knowledge | Knowing a lot | A standard for verifying errors and gaps in AI responses |\n| Experience | Having done something for a long time | The ability to know what questions to ask |\n| Instinct | Intuitive taste | The ability to detect subtle awkwardness in AI outputs |\n| Creativity | The ability to create something new | The ability to select valuable combinations from countless alternatives |\n| Execution | The ability to handle things directly | The ability to integrate AI and human input to finalize results |\n\n## 5. An Action Checklist Individuals Can Start Right Away\n\n### Today’s Tasks\n\n- Gather all documents and notes created over the past year into a single folder.\n- List 10 recurring tasks and record the time required and frequency.\n- Distinguish between tasks that are risky to delegate to AI and those that are safe to delegate.\n- Save frequently used prompts and compare the quality of the results.\n- Record errors in AI responses to create your own verification checklist.\n\n### This Month’s Tasks\n\n- Create a basic classification system for your personal wiki or team knowledge base.\n- Organize feedback from customers, colleagues, and users by topic.\n- Choose one task and compare the time it takes before and after applying AI.\n- Try converting your tacit knowledge into an explainable checklist.\n- Create an evaluation rubric for AI-generated outputs.\n\n### Essential Principles\n\n- Do not indiscriminately enter personal information or trade secrets into external AI services.\n- Treat AI responses as drafts and hypotheses, and establish verification procedures for important decisions.\n- For content requiring sources, provide verifiable evidence.\n- Prioritize unique experiences and verified information over low-quality mass production.\n- Expand the purpose of AI adoption beyond mere cost reduction to include quality improvement and faster learning.\n\n## Conclusion: AI Is Not a Magic Wand, but an Amplifier\n\nAI is not a magic wand that automatically makes money for people who do nothing. AI is more like an engine that significantly amplifies the ability of people who are already identifying problems, accumulating data, and striving to produce better results.\n\nGoing forward, the difference will arise from something more fundamental than simply knowing the latest tools. What matters is how deeply you understand the context of your work, how well you organize your own data, whether you have the discernment to evaluate AI-generated results, and whether you have the attitude to see difficult problems through to a resolution.\n\nThe people who won’t be replaced by AI aren’t those who reject it. They are the ones who integrate AI into the structure of their work to achieve a higher level of judgment and execution.","content_html":"\u003ch2\u003e\u003ca href=\"#conclusion-at-a-glance\" class=\"anchor\" id=\"conclusion-at-a-glance\"\u003e\u003c/a\u003eConclusion at a Glance\u003c/h2\u003e\n\u003cp\u003eIn the age of AI, the people who make money and survive aren’t simply those who know a lot about the latest AI tools. What’s more important is accurately defining the problems in your work, building up unique data and context that AI can utilize, and developing the discernment to evaluate the results.\u003c/p\u003e\n\u003cp\u003eYouTube and online courses are full of messages claiming that you can work just 30 minutes a day with AI and make a fortune. While some do feature real-life examples of productivity gains, a significant number are essentially just selling content that teaches how to make money with AI. Therefore, the key question is this: It’s not about what you can automate with AI, but rather which bottlenecks in your work you will solve with AI.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#1-breaking-free-from-the-illusion-of-ai-automation-profits\" class=\"anchor\" id=\"1-breaking-free-from-the-illusion-of-ai-automation-profits\"\u003e\u003c/a\u003e1. Breaking Free from the Illusion of AI Automation Profits\u003c/h2\u003e\n\u003ch3\u003e\u003ca href=\"#the-competitive-edge-of-automation-based-side-hustles-doesnt-last-long\" class=\"anchor\" id=\"the-competitive-edge-of-automation-based-side-hustles-doesnt-last-long\"\u003e\u003c/a\u003eThe Competitive Edge of Automation-Based Side Hustles Doesn’t Last Long\u003c/h3\u003e\n\u003cp\u003eAfter generative AI became mainstream, monetization methods such as automated text generation, image generation, short video production, automated blog posting, and chatbot operation spread rapidly. However, in fields where anyone can use the same tools, competition quickly becomes overheated. If the output is low-quality or repetitive, it’s easy to be shunned by platforms, search engines, and users alike.\u003c/p\u003e\n\u003cp\u003eGoogle Search Central explains that mass-generated low-quality content or “scaled content abuse” intended to manipulate search rankings are subject to its spam policies. In other words, it is not the fact that AI was used that is risky, but rather the practice of mass-producing content that offers no real value to people.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#ai-is-not-a-side-hustle-generator-but-a-productivity-engine\" class=\"anchor\" id=\"ai-is-not-a-side-hustle-generator-but-a-productivity-engine\"\u003e\u003c/a\u003eAI Is Not a Side Hustle Generator, but a Productivity Engine\u003c/h3\u003e\n\u003cp\u003eSustainable revenue comes not from AI tools themselves, but from increasing the efficiency of existing work and businesses.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eApproach\u003c/th\u003e\n\u003cth\u003eShort-Term Appeal\u003c/th\u003e\n\u003cth\u003eLong-Term Limitations\u003c/th\u003e\n\u003cth\u003eBetter Direction\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMass-producing automated blog posts and Shorts with AI\u003c/td\u003e\n\u003ctd\u003eEasy to get started\u003c/td\u003e\n\u003ctd\u003eLow differentiation and difficult quality control\u003c/td\u003e\n\u003ctd\u003eProducing content that includes unique experiences, data, and verification\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSelling prompt collections\u003c/td\u003e\n\u003ctd\u003eCan be created quickly\u003c/td\u003e\n\u003ctd\u003eVulnerable to changes in tools\u003c/td\u003e\n\u003ctd\u003eRefining into templates that solve specific job or industry problems\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAutomating simple, repetitive tasks\u003c/td\u003e\n\u003ctd\u003eImmediate time savings\u003c/td\u003e\n\u003ctd\u003eAnyone can replicate it\u003c/td\u003e\n\u003ctd\u003eSystematizing work by integrating with the organization’s data and processes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntegrating AI into Core Business Operations\u003c/td\u003e\n\u003ctd\u003eInitial training required\u003c/td\u003e\n\u003ctd\u003eImplementation can be challenging\u003c/td\u003e\n\u003ctd\u003ePotential for sustainable cost savings and quality improvements\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eFor example, a restaurant owner can use AI not only to write menu descriptions but also to analyze inventory records, seasonal sales figures, review data, and local event information to adjust order volumes and promotions. Planners can go beyond simply having AI generate summaries; they can input past proposals and examples of failed projects to improve the quality of their decision-making.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#2-your-true-weapons-are-your-own-data-and-insight\" class=\"anchor\" id=\"2-your-true-weapons-are-your-own-data-and-insight\"\u003e\u003c/a\u003e2. Your True Weapons Are Your Own Data and Insight\u003c/h2\u003e\n\u003ch3\u003e\u003ca href=\"#when-everyone-uses-powerful-ai-the-difference-lies-in-the-data\" class=\"anchor\" id=\"when-everyone-uses-powerful-ai-the-difference-lies-in-the-data\"\u003e\u003c/a\u003eWhen Everyone Uses Powerful AI, the Difference Lies in the Data\u003c/h3\u003e\n\u003cp\u003eHigh-performance AI services like ChatGPT, Claude, and Gemini are accessible to many people simply by paying a subscription fee. The era when access to these models alone was a scarce competitive advantage is rapidly coming to an end. Even when using the same AI, results vary because the context, data, criteria, and feedback provided by users differ.\u003c/p\u003e\n\u003cp\u003eExamples of personal and organizational data that can be provided to AI include the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePast planning documents, reports, proposals, and meeting minutes\u003c/li\u003e\n\u003cli\u003eCustomer inquiries, reviews, and consultation records\u003c/li\u003e\n\u003cli\u003ePersonal notes, idea journals, and reading logs\u003c/li\u003e\n\u003cli\u003eRoot cause analysis reports for failed projects\u003c/li\u003e\n\u003cli\u003eWork checklists and decision-making criteria\u003c/li\u003e\n\u003cli\u003eOn-site work videos, observation logs, and know-how documents\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOrganizing this material creates a personal wiki or organizational knowledge base. It transforms the data into an asset that is not merely stored but is searchable, summarizable, and recombinable.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#practical-ways-to-create-a-personal-wiki\" class=\"anchor\" id=\"practical-ways-to-create-a-personal-wiki\"\u003e\u003c/a\u003ePractical Ways to Create a Personal Wiki\u003c/h3\u003e\n\u003cp\u003eA personal wiki doesn’t have to be a complex system. The key is to gather scattered records and organize them into a structure that AI can understand.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eGather materials: Consolidate notes from note-taking apps, cloud documents, emails, local files, and screenshots into one place.\u003c/li\u003e\n\u003cli\u003eRecord dates and sources: You must be able to trace the basis for the AI’s summaries later on.\u003c/li\u003e\n\u003cli\u003eAdd topic tags: Keep tags simple—such as “customers,” “products,” “ideas,” “failures,” “lessons learned,” and “recurring tasks.”\u003c/li\u003e\n\u003cli\u003eRemove sensitive information: Delete or anonymize personal information, contractual secrets, and customer identifiers.\u003c/li\u003e\n\u003cli\u003eCreate a list of questions: Prepare in advance what you want to ask the AI.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eHere are some example questions:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat are the recurring strengths and weaknesses in my proposals over the past three years?\u003c/li\u003e\n\u003cli\u003eWhich types of customer complaints are most likely to result in actual revenue loss?\u003c/li\u003e\n\u003cli\u003eWhat common patterns do the tasks I frequently procrastinate on share?\u003c/li\u003e\n\u003cli\u003eWhat are the differences in decision-making between successful and failed projects?\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca href=\"#insight-quantifying-tacit-knowledge-and-judgment-criteria\" class=\"anchor\" id=\"insight-quantifying-tacit-knowledge-and-judgment-criteria\"\u003e\u003c/a\u003eInsight: Quantifying Tacit Knowledge and Judgment Criteria\u003c/h3\u003e\n\u003cp\u003eGenerally, “tacit knowledge” is translated into Korean as “암묵지.” It refers to knowledge that is difficult to fully explain in writing or through manuals but is instinctively understood by skilled practitioners. The “insight” discussed here refers specifically to the sense—within tacit knowledge—that distinguishes what is good from what is awkward and determines which choices are appropriate for the situation.\u003c/p\u003e\n\u003cp\u003eFor example, the following types of knowledge are difficult to express using only numbers or sentences:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eA designer’s intuition that the white space on a screen looks awkward\u003c/li\u003e\n\u003cli\u003eA chef’s ability to judge the degree of doneness using their fingertips and sense of smell\u003c/li\u003e\n\u003cli\u003eA salesperson’s intuition for detecting a customer’s hesitation in the flow of conversation\u003c/li\u003e\n\u003cli\u003eAn editor’s ability to recognize the rhythm of a sentence and identify points where readers might lose interest\u003c/li\u003e\n\u003cli\u003eA field manager’s ability to intuitively sense a work workflow that could lead to an accident\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhile AI can learn from vast amounts of text and images, it cannot automatically acquire the on-the-ground intuition accumulated by specific organizations and individuals. Therefore, future competitiveness depends on how well we document our tacit knowledge, illustrate it with examples, and transform it into feedback data.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#3-the-workplace-will-be-reorganized-around-work-units\" class=\"anchor\" id=\"3-the-workplace-will-be-reorganized-around-work-units\"\u003e\u003c/a\u003e3. The Workplace Will Be Reorganized Around Work Units\u003c/h2\u003e\n\u003ch3\u003e\u003ca href=\"#work-units-will-change-before-entire-occupations-do\" class=\"anchor\" id=\"work-units-will-change-before-entire-occupations-do\"\u003e\u003c/a\u003eWork Units Will Change Before Entire Occupations Do\u003c/h3\u003e\n\u003cp\u003eIt is difficult to conclude that AI will eliminate entire occupations. In reality, change will first occur at the level of the work units that make up a job. Tasks with a high degree of predictability—such as data research, drafting, schedule coordination, summarization, categorization, and report formatting—are highly likely to be automated quickly. On the other hand, human capabilities remain crucial for responsible decision-making, conflict resolution, on-site judgment, ethical judgment, and building trust through face-to-face interactions.\u003c/p\u003e\n\u003cp\u003eThe World Economic Forum’s \u003cem\u003eFuture of Jobs Report 2025\u003c/em\u003e predicts that technological change, the green transition, and shifts in economic structure will simultaneously create new jobs and replace existing ones. The key point is not the simple conclusion that jobs will disappear, but rather that the required skill sets are changing rapidly.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#the-crisis-and-redefinition-of-middle-management\" class=\"anchor\" id=\"the-crisis-and-redefinition-of-middle-management\"\u003e\u003c/a\u003eThe Crisis and Redefinition of Middle Management\u003c/h3\u003e\n\u003cp\u003eMiddle managers have traditionally been responsible for breaking down goals into tasks, assigning them to people, monitoring progress, and reporting to upper management. Some of these tasks fall within the scope of what AI agents and automated systems can handle effectively.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMiddle Management Tasks\u003c/th\u003e\n\u003cth\u003eLikelihood of AI Replacement\u003c/th\u003e\n\u003cth\u003eValue That Humans Must Retain\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eScheduling and status reporting\u003c/td\u003e\n\u003ctd\u003eHigh\u003c/td\u003e\n\u003ctd\u003eInterpreting critical delay signals and determining priorities\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAssigning repetitive tasks\u003c/td\u003e\n\u003ctd\u003eMedium to high\u003c/td\u003e\n\u003ctd\u003eAssignments that consider individual development stages and team dynamics\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompiling performance data\u003c/td\u003e\n\u003ctd\u003eHigh\u003c/td\u003e\n\u003ctd\u003eAssessing context that numbers cannot explain\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWriting meeting minutes and summaries\u003c/td\u003e\n\u003ctd\u003eHigh\u003c/td\u003e\n\u003ctd\u003eIdentifying the core of debates and determining accountability\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStrategic decision-making\u003c/td\u003e\n\u003ctd\u003eLow to medium\u003c/td\u003e\n\u003ctd\u003eJudgments involving uncertainty, ethics, and accountability\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eManaging organizational culture\u003c/td\u003e\n\u003ctd\u003eLow\u003c/td\u003e\n\u003ctd\u003eBuilding trust, motivation, and psychological safety\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eTherefore, the future of middle managers is less about disappearance and more about redefinition. Roles limited to mere messengers and report managers are becoming obsolete, while “orchestrator”-type managers—who structure problems and coordinate people and AI—are becoming increasingly important.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#the-value-of-junior-employees-wont-disappear-but-their-roles-will-change\" class=\"anchor\" id=\"the-value-of-junior-employees-wont-disappear-but-their-roles-will-change\"\u003e\u003c/a\u003eThe Value of Junior Employees Won’t Disappear, but Their Roles Will Change\u003c/h3\u003e\n\u003cp\u003eAs AI takes over drafting and research tasks, traditional training opportunities for new hires and junior employees may decrease. At the same time, new opportunities are emerging for junior employees. This is because the ability to quickly review an AI-generated “80-point” draft, incorporate on-the-spot feedback, and refine it by adding a sense of current trends will become increasingly important.\u003c/p\u003e\n\u003cp\u003eHowever, it’s difficult to assume that the value of junior staff will automatically skyrocket. The key lies in whether companies position junior staff not merely as support personnel, but as experimenters and field data collectors who work alongside AI. Junior staff, too, must grow not as people who simply submit AI-generated output as-is, but as individuals who identify errors and provide contextual context.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#4-three-competencies-for-thriving-in-the-ai-era\" class=\"anchor\" id=\"4-three-competencies-for-thriving-in-the-ai-era\"\u003e\u003c/a\u003e4. Three Competencies for Thriving in the AI Era\u003c/h2\u003e\n\u003ch3\u003e\u003ca href=\"#1-prompting-skills-designing-questions-that-unlock-ais-potential\" class=\"anchor\" id=\"1-prompting-skills-designing-questions-that-unlock-ais-potential\"\u003e\u003c/a\u003e1) Prompting Skills: Designing Questions That Unlock AI’s Potential\u003c/h3\u003e\n\u003cp\u003eA prompt is not merely a command. A good prompt includes the objective, context, constraints, criteria, examples, and output format. Since AI constructs answers based on the conditions provided by the user, ambiguous questions lead to ambiguous answers.\u003c/p\u003e\n\u003cp\u003eThe components of a good prompt are as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eObjective\u003c/td\u003e\n\u003ctd\u003eClearly specify what you want to achieve\u003c/td\u003e\n\u003ctd\u003eCategorize the causes of customer churn into 5 categories\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eContext\u003c/td\u003e\n\u003ctd\u003eDescribe the industry, target audience, and situation\u003c/td\u003e\n\u003ctd\u003eIt’s a beauty subscription service targeting women in their 20s\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eData\u003c/td\u003e\n\u003ctd\u003eProvide data for analysis\u003c/td\u003e\n\u003ctd\u003eBase your analysis on 300 reviews from the past 6 months\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCriteria\u003c/td\u003e\n\u003ctd\u003eOutline the criteria for a good answer\u003c/td\u003e\n\u003ctd\u003eEvaluate in order of feasibility, cost, and effectiveness\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConstraints\u003c/td\u003e\n\u003ctd\u003eSpecify conditions to avoid\u003c/td\u003e\n\u003ctd\u003eDo not infer personal information, and avoid unfounded assumptions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat\u003c/td\u003e\n\u003ctd\u003eSpecify the structure of the output\u003c/td\u003e\n\u003ctd\u003eOrganize the results into tables and prioritized lists\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eA bad prompt demands an answer. A good prompt designs a thought process.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#2-problem-awareness-the-sense-of-turning-inconvenience-into-an-opportunity-for-automation\" class=\"anchor\" id=\"2-problem-awareness-the-sense-of-turning-inconvenience-into-an-opportunity-for-automation\"\u003e\u003c/a\u003e2) Problem Awareness: The Sense of Turning Inconvenience into an Opportunity for Automation\u003c/h3\u003e\n\u003cp\u003ePeople who use AI effectively are generally sensitive to inconvenience. Here, “complaints” refer not to emotional frustration but to the ability to detect problems.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhy is this report written the same way every week?\u003c/li\u003e\n\u003cli\u003eWhy do people have to read customer inquiries from scratch every time?\u003c/li\u003e\n\u003cli\u003eWhy are the decisions and responsible parties unclear after a meeting ends?\u003c/li\u003e\n\u003cli\u003eWhy are new employee training materials always delivered only verbally?\u003c/li\u003e\n\u003cli\u003eWhy are failure cases not documented and simply forgotten?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese questions are the starting point for AI adoption. Rather than looking for technology first, you must first identify recurring inefficiencies and bottlenecks, and then determine whether AI is the right solution.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#3-the-combination-of-experience-and-intuition-the-discerning-eye-for-selecting-ai-outputs\" class=\"anchor\" id=\"3-the-combination-of-experience-and-intuition-the-discerning-eye-for-selecting-ai-outputs\"\u003e\u003c/a\u003e3) The Combination of Experience and Intuition: The Discerning Eye for Selecting AI Outputs\u003c/h3\u003e\n\u003cp\u003eAI rapidly generates many options. However, as the number of options increases, the ability to choose becomes an even more critical skill. The discernment needed to distinguish between good results and merely plausible ones stems from a combination of experience and intuition.\u003c/p\u003e\n\u003cp\u003eIf you have only experience but fail to read trends, you may become trapped in outdated judgments. Conversely, if you have only intuition but lack experience, you may make choices that seem plausible but are unfeasible. Decision-makers in the AI era must possess both.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCompetency\u003c/th\u003e\n\u003cth\u003eMeaning in the Past\u003c/th\u003e\n\u003cth\u003eMeaning in the AI Era\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eKnowledge\u003c/td\u003e\n\u003ctd\u003eKnowing a lot\u003c/td\u003e\n\u003ctd\u003eA standard for verifying errors and gaps in AI responses\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExperience\u003c/td\u003e\n\u003ctd\u003eHaving done something for a long time\u003c/td\u003e\n\u003ctd\u003eThe ability to know what questions to ask\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInstinct\u003c/td\u003e\n\u003ctd\u003eIntuitive taste\u003c/td\u003e\n\u003ctd\u003eThe ability to detect subtle awkwardness in AI outputs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCreativity\u003c/td\u003e\n\u003ctd\u003eThe ability to create something new\u003c/td\u003e\n\u003ctd\u003eThe ability to select valuable combinations from countless alternatives\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExecution\u003c/td\u003e\n\u003ctd\u003eThe ability to handle things directly\u003c/td\u003e\n\u003ctd\u003eThe ability to integrate AI and human input to finalize results\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca href=\"#5-an-action-checklist-individuals-can-start-right-away\" class=\"anchor\" id=\"5-an-action-checklist-individuals-can-start-right-away\"\u003e\u003c/a\u003e5. An Action Checklist Individuals Can Start Right Away\u003c/h2\u003e\n\u003ch3\u003e\u003ca href=\"#todays-tasks\" class=\"anchor\" id=\"todays-tasks\"\u003e\u003c/a\u003eToday’s Tasks\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eGather all documents and notes created over the past year into a single folder.\u003c/li\u003e\n\u003cli\u003eList 10 recurring tasks and record the time required and frequency.\u003c/li\u003e\n\u003cli\u003eDistinguish between tasks that are risky to delegate to AI and those that are safe to delegate.\u003c/li\u003e\n\u003cli\u003eSave frequently used prompts and compare the quality of the results.\u003c/li\u003e\n\u003cli\u003eRecord errors in AI responses to create your own verification checklist.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca href=\"#this-months-tasks\" class=\"anchor\" id=\"this-months-tasks\"\u003e\u003c/a\u003eThis Month’s Tasks\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCreate a basic classification system for your personal wiki or team knowledge base.\u003c/li\u003e\n\u003cli\u003eOrganize feedback from customers, colleagues, and users by topic.\u003c/li\u003e\n\u003cli\u003eChoose one task and compare the time it takes before and after applying AI.\u003c/li\u003e\n\u003cli\u003eTry converting your tacit knowledge into an explainable checklist.\u003c/li\u003e\n\u003cli\u003eCreate an evaluation rubric for AI-generated outputs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003ca href=\"#essential-principles\" class=\"anchor\" id=\"essential-principles\"\u003e\u003c/a\u003eEssential Principles\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDo not indiscriminately enter personal information or trade secrets into external AI services.\u003c/li\u003e\n\u003cli\u003eTreat AI responses as drafts and hypotheses, and establish verification procedures for important decisions.\u003c/li\u003e\n\u003cli\u003eFor content requiring sources, provide verifiable evidence.\u003c/li\u003e\n\u003cli\u003ePrioritize unique experiences and verified information over low-quality mass production.\u003c/li\u003e\n\u003cli\u003eExpand the purpose of AI adoption beyond mere cost reduction to include quality improvement and faster learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca href=\"#conclusion-ai-is-not-a-magic-wand-but-an-amplifier\" class=\"anchor\" id=\"conclusion-ai-is-not-a-magic-wand-but-an-amplifier\"\u003e\u003c/a\u003eConclusion: AI Is Not a Magic Wand, but an Amplifier\u003c/h2\u003e\n\u003cp\u003eAI is not a magic wand that automatically makes money for people who do nothing. AI is more like an engine that significantly amplifies the ability of people who are already identifying problems, accumulating data, and striving to produce better results.\u003c/p\u003e\n\u003cp\u003eGoing forward, the difference will arise from something more fundamental than simply knowing the latest tools. What matters is how deeply you understand the context of your work, how well you organize your own data, whether you have the discernment to evaluate AI-generated results, and whether you have the attitude to see difficult problems through to a resolution.\u003c/p\u003e\n\u003cp\u003eThe people who won’t be replaced by AI aren’t those who reject it. They are the ones who integrate AI into the structure of their work to achieve a higher level of judgment and execution.\u003c/p\u003e\n","tags":["AI","Career","Productivity","Data","Future Careers"],"faqs":[{"question":"What is the first thing we should do in the age of AI?","answer":"Rather than blindly learning the latest tools, it’s better to first organize your repetitive tasks, decision-making bottlenecks, and scattered data. AI significantly boosts productivity when there are clear problems and good data."},{"question":"If I start a side hustle using AI automation, can I earn money consistently?","answer":"While it may be possible to generate some short-term profits, competition quickly intensifies in areas where everyone uses the same tools. In the long run, leveraging AI in conjunction with one’s core business, expertise, and proprietary data offers greater stability."},{"question":"Why is it important to be able to write good prompts?","answer":"The quality of an AI’s responses varies significantly depending on the goals, context, constraints, and criteria provided by the user. A good prompt is not just a simple question, but a statement designed to guide the AI to solve the problem correctly."},{"question":"How Does Personal Data Become a Competitive Advantage in AI?","answer":"Past documents, notes, customer feedback, and examples of failures contain context that others do not have. By structuring this information, AI can generate analyses and recommendations tailored to individuals or organizations."},{"question":"Do \"explicit knowledge\" and \"tacit knowledge\" mean the same thing?","answer":"Tacit knowledge refers to expert knowledge that is difficult to fully explain in words or in writing. Among these, “anmokji” can be understood as a term that emphasizes the sense of discernment, taste, and on-the-spot judgment needed to distinguish between what is good and what is awkward."},{"question":"Will middle managers disappear because of AI?","answer":"Certain administrative tasks—such as compiling schedules, summarizing reports, and checking the status of work—can be automated. However, roles that require a human touch—such as conflict resolution, making responsible decisions, and fostering team growth—will continue to be important."},{"question":"Is AI a threat or an opportunity for young people?","answer":"It’s both. While tasks such as simple research and drafting may decrease, the value of junior staff—who review AI outputs, incorporate field data, and conduct rapid experiments—may increase."},{"question":"Is it okay to use the output generated by AI as is?","answer":"It is safer not to use it as-is for important tasks. You need to verify facts, check sources, review personal information, and ensure the content meets your organization’s quality standards."},{"question":"How is creativity changing in the age of AI?","answer":"Since AI can generate countless drafts and ideas, creativity now includes the ability to select and combine good alternatives and adapt them to fit reality."},{"question":"Where should an organization start when adopting AI?","answer":"It is best to start with tasks that occur frequently, for which data is available, and where the cost of failure is low. Afterward, you should measure the results, establish security, accountability, and verification procedures, and then expand to core tasks."}],"sources":[{"url":"https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier","title":"McKinsey \u0026 Company: The Economic Potential of Generative AI","type":"source"},{"url":"https://www.weforum.org/publications/the-future-of-jobs-report-2025/","title":"World Economic Forum: The Future of Jobs Report 2025","type":"source"},{"url":"https://www.nist.gov/itl/ai-risk-management-framework","title":"NIST AI Risk Management Framework","type":"source"},{"url":"https://developers.google.com/search/docs/essentials/spam-policies","title":"Google Search Central: Spam Policies for Google Web Search","type":"source"},{"url":"https://support.google.com/youtube/answer/2801973","title":"YouTube Help: Policies on Spam, Deceptive Practices, and Scams","type":"source"}],"images":[{"id":46,"url":"https://injoys.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDU5LCJwdXIiOiJibG9iX2lkIn19--51d80840fafe5e5dfa73a2b377fed0f3058f0998/ai-cfb5038c.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"AI 네트워크 앞에 선 사람과 질문, 분석, 보석 아이콘이 있는 역량 인포그래픽","caption":"AI 시대에 필요한 질문력, 분석력, 고유한 가치를 상징적으로 보여준다.","description":null},"en":{"alt":"Person facing an AI network with question, analysis, and diamond icons in an infographic","caption":"The illustration frames human skills around an AI system and supporting data panels.","description":null},"ja":{"alt":"AIネットワークの前に立つ人物と、疑問・分析・宝石のアイコンを配した図解","caption":"AIを中心に、人に求められる問い、分析、独自の価値を表している。","description":null},"es":{"alt":"Persona frente a una red de IA con iconos de pregunta, análisis y diamante en una infografía","caption":"La ilustración relaciona habilidades humanas clave con un sistema de IA y paneles de datos.","description":null},"id":{"alt":"Orang berdiri di depan jaringan AI dengan ikon tanda tanya, analisis, dan berlian","caption":"Ilustrasi ini menautkan keterampilan manusia dengan sistem AI dan panel data di sekitarnya.","description":null},"pt":{"alt":"Pessoa diante de uma rede de IA com ícones de pergunta, análise e diamante","caption":"A ilustração conecta habilidades humanas a um sistema de IA e painéis de dados.","description":null},"zh-hant":{"alt":"人物站在 AI 網路前，周圍有提問、分析與鑽石圖示的資訊圖","caption":"插圖以 AI 系統呈現提問、分析與獨特價值等人類能力。","description":null}}},{"id":47,"url":"https://injoys.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDcwLCJwdXIiOiJibG9iX2lkIn19--fbaebf6ec937c20d5f56e47169dccd1d103083b8/ai-d7eab3e9.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"회로 나무와 금고, 나침반 앞에서 도형을 고르는 사람","caption":"사람이 데이터의 흐름 속에서 선택과 방향을 찾아가는 장면입니다.","description":null},"en":{"alt":"Person sorting shapes before a circuit tree, vault, winding data paths, and compass","caption":"A person navigates choices in a landscape of technology and direction.","description":null},"ja":{"alt":"回路の木と金庫、コンパスの前で図形を選ぶ人物","caption":"人物がテクノロジーの風景の中で選択と進む方向を探している。","description":null},"es":{"alt":"Persona eligiendo formas ante un árbol de circuitos, una bóveda y una brújula","caption":"Una persona busca rumbo entre datos, decisiones y tecnología.","description":null},"id":{"alt":"Seseorang memilih bentuk di depan pohon sirkuit, brankas, jalur data, dan kompas","caption":"Seseorang menavigasi pilihan dalam lanskap teknologi dan arah.","description":null},"pt":{"alt":"Pessoa escolhendo formas diante de uma árvore de circuitos, cofre e bússola","caption":"Uma pessoa busca direção entre dados, escolhas e tecnologia.","description":null},"zh-hant":{"alt":"人物在電路樹、保險庫與指南針前挑選形狀","caption":"人物在科技與資料交織的場景中尋找選擇與方向。","description":null}}}],"published_at":"2026-07-06T11:04:07+09:00","updated_at":"2026-07-06T11:04:07+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant"],"url":"https://injoys.com/en/articles/ai-era-survival-skills-3"}