Survive the AI Era! A Practical Skill Guide to Never Worry About Your Livelihood

Survive the AI Era! A Practical Skill Guide to Never Worry About Your Livelihood

Introduction: Why is a Redefinition of Skills Necessary Now?

Hello everyone!

Suddenly, are you feeling a vague sense of anxiety, wondering, “What will happen to my job in the future?”

The evolution of AI technology is truly astonishingly fast, constantly changing our way of working and the very fabric of society. In particular, as AI automation progresses, some of you might even worry, “Could my job also be replaced by AI?”

But wait a moment!

This significant change is by no means just a threat to us. On the contrary, it’s the best opportunity to acquire new skills and significantly enhance your own value!

In this paid article, I will explain in an easy-to-understand manner, based on data and scientific evidence, what the current labor market is like and how it will evolve. Then, I will specifically introduce truly useful skills and effective learning methods to ensure you “never worry about your livelihood” even in the AI era.

Now, let’s start preparing to open up the future together!

Chapter 1: Deciphering the “Present” and “Future” of the Labor Market with Data

1.1. Japan’s Labor Shortage: Unavoidable Reality and New Demand

Do you know what the current situation of Japan’s labor market is? In fact, with the declining birthrate and aging population, Japan is facing a serious labor shortage. According to a survey by Persol Research and Consulting, it is predicted that by 2035, the labor shortage will be approximately 1.85 times that of 2023. This means a shortage of 17.75 million hours of labor per day [1].

This labor shortage is a major challenge for companies, but for us individuals, it is precisely an opportunity where “the demand for people with specific skills will expand”! In particular, people who have skills that can increase productivity, streamline work, or possess uniquely human skills that AI cannot imitate, will become increasingly sought after.

1.2. AI and Automation: Threat or Coexistence?

You often hear talk about “AI taking away jobs,” don’t you? Indeed, the evolution of AI is remarkable, and it has the potential to automate many routine tasks such as data entry and simple operations. It is true that many occupations that can be replaced by AI have been pointed out in research by Oxford University.

However, this is by no means a pessimistic story about “humans losing their jobs.” Rather, we should think of AI as a “convenient tool” that allows us to focus on more creative and advanced work. What is truly required in the AI era is not only technical skills to develop and effectively use AI, but also the re-recognition of the importance of “human-like skills” that AI cannot do [2].

1.3. Trend of Wage Increase: Towards an Era Where Skills Directly Lead to Rewards

The labor shortage and economic changes are actually starting to have a positive impact on our wages. In particular, people with highly marketable specialized skills tend to receive higher salaries. The movement for wage increases has already begun, with the largest ever minimum wage increase nationwide implemented in October 2024 [3].

This is proof that companies are thinking, “Let’s invest properly in excellent human resources!” In other words, in the coming era, people who can hone their skills and provide value that the market “wants!” are likely to build a more economically stable future.

Chapter 2: Four Promising and “Profitable” Specialized Skills

Now, let’s delve deeply into the specific “profitable” skills that you are probably most interested in. I will focus on four specialized skills that are currently in high demand in the labor market and have excellent future prospects, and introduce them in detail.

2.1. IT Engineer: Professionals Supporting the Foundation of Digital Society

2.1.1. Why are IT Engineers “Profitable”?

Everyone, have you heard the term “DX (Digital Transformation)” a lot recently? Companies are now desperate to introduce and improve IT systems to survive. Therefore, the demand for IT engineers far exceeds the supply. This gap between “people who are wanted” and “people who are available” is a major reason why the average annual income of IT engineers is significantly increasing.

2.1.2. Diverse Job Roles and Career Paths for IT Engineers

Even if you say “IT engineer” in one word, there are actually various specialized fields. You can choose the perfect career path according to your interests and strengths.

* **Web Engineer**: This job involves creating websites and web applications. It is divided into “frontend,” which creates the parts that you directly interact with, and “backend,” which creates the systems that run behind the scenes. “Full-stack engineers” who can do both are now in high demand.
 * **Frontend**: HTML, CSS, JavaScript (React, Vue.js, Angular, etc.) are the main skills.
 * **Backend**: Python (Django, Flask), Ruby (Ruby on Rails), PHP (Laravel), Java (Spring) are often used.
* **Infrastructure Engineer**: This job involves designing, building, and operating the foundation of IT systems such as servers, networks, and databases. They are the unsung heroes who support the stable operation of systems.
 * **Main skills**: Knowledge of OS such as Linux and Windows Server, knowledge of networks such as TCP/IP, and knowledge of databases such as MySQL and PostgreSQL are required.
* **Cloud Engineer**: This is a specialist who builds and operates systems on cloud platforms such as AWS, Azure, and GCP. Recently, the number of companies using the cloud has increased significantly, so this job is particularly attracting attention.
 * **Main skills**: Knowledge of each cloud service, and IaC (Infrastructure as Code) tools (Terraform, etc.) are useful.
* **Security Engineer**: This job involves taking measures to protect systems and data from cyberattacks. The importance of this job is increasing as the risk of information leakage and system downtime is rising.
 * **Main skills**: Network security, web application security, encryption technology, vulnerability assessment, etc.
* **Data Engineer**: This job involves building data infrastructure for data scientists to easily analyze by collecting, processing, and storing large amounts of data. In the era of big data, data utilization cannot progress without data engineers.
 * **Main skills**: Databases (including NoSQL), data warehouses, ETL tools, distributed processing technologies (Hadoop, Spark), etc.

These job roles are actually all connected. By experiencing various fields, you can grow into an engineer with even higher market value.

2.1.3. Efficient Learning Roadmap for Becoming an IT Engineer from Scratch

There is no need to give up just because you are inexperienced! Anyone can aim to become an IT engineer if they learn systematically and actually get their hands dirty. Here is an efficient learning roadmap.

1. **Acquire basic IT knowledge (about 1 month)**: First, understand how computers work, and the basic concepts of OS, networks, and databases. This is a very important foundation for any field you pursue.
2. **Learn a programming language (about 2–3 months)**: Choose a language recommended for beginners, such as Python or JavaScript, and learn basic syntax, data handling, and how to build programs. Writing and running simple programs is the fastest way.
3. **Set up a development environment & learn version control (about 1 month)**: Learn how to use editors like VS Code and how to manage program change history using Git/GitHub. This will enable efficient development.
4. **Learn and practice web frameworks (about 3–4 months)**: Once you have a solid foundation, learn frameworks (e.g., Django or React) to efficiently develop web applications. Then, actually build web applications.
5. **Create a portfolio**: Create a “portfolio” where you can showcase your skills by publishing the web applications and services you have created. This will be the most important thing to demonstrate your abilities in job hunting and career change activities.
6. **Learn cloud basics (about 1 month)**: Learn the basic usage of major cloud services such as AWS, Azure, and GCP. Cloud knowledge is now almost essential for system development.

2.1.4. Certifications to Prove IT Engineer Skills

Certifications objectively prove your willingness to “study hard!” and your basic knowledge.

* **Fundamental Information Technology Engineer Examination**: This is a national qualification that tests general basic IT knowledge. If you aim to be an IT engineer, this is the gateway qualification.
* **AWS Certification**: This certification proves your expertise in cloud technology. If you aim to be a cloud engineer, this is essential.
* **Cisco Certified Network Associate (CCNA, etc.)**: This certification proves your expertise in network technology. This is for infrastructure engineers.

2.2. Data Scientist: “Knowledge Seeker” Who Predicts the Future from Data

2.2.1. Why are Data Scientists “Profitable”?

Have you heard the phrase “data is the new oil of the 21st century”? In today’s business, data is truly a valuable asset. Data scientists are experts who find “hints” from this vast amount of data to solve business problems and create new value. As more companies use data to drive their businesses, the demand for data scientists has exploded, leading to high salaries.

2.2.2. Diverse Job Roles and Career Paths for Data Scientists

The work of data scientists is truly diverse. The required skills also vary slightly depending on the company and project.

* **Data Analyst**: This job involves analyzing existing data to identify business problems and propose improvements. They use statistical knowledge and tools to visualize data clearly, extracting “aha!” discoveries from the data.
 * **Main skills**: Basic statistics, SQL, BI tools (Tableau, Power BI, etc.) are useful.
* **Machine Learning Engineer**: This job involves developing, deploying, and operating machine learning models. They build the models designed by data scientists into actual working systems and develop more efficient models.
 * **Main skills**: Programming (Python, R), deep understanding of machine learning algorithms, software development knowledge, cloud platform knowledge, etc.
* **Data Engineer**: This job involves building the data foundation used by data scientists and machine learning engineers. They design and build systems to efficiently collect, process, and store large amounts of data.
 * **Main skills**: Databases (SQL, NoSQL), data warehouses, ETL tools, distributed processing technologies (Hadoop, Spark), etc.
* **Business Analyst**: This job involves leveraging data from a business perspective to formulate strategies and support decision-making. The ability to apply data analysis results to actual business is required.
 * **Main skills**: Business knowledge, industry-specific expertise, communication skills, basic data analysis, etc.

2.2.3. Efficient Learning Roadmap for Becoming a Data Scientist from Scratch

To become a data scientist, systematically progressing through the following steps is the key to success.

1. **Solidify basic math and statistics (about 2–3 months)**: Understand the basics of linear algebra, calculus, probability, and statistics. In particular, statistical concepts (regression analysis, hypothesis testing, etc.) are necessary in all aspects of data analysis.
2. **Learn a programming language (about 3–4 months)**: Acquire Python (NumPy, Pandas, Scikit-learn, TensorFlow, Keras) or R. Python is dominant in the field of data science.
3. **Learn database basics (about 1 month)**: Learn the basic usage of SQL and understand the concept of databases.
4. **Learn and practice machine learning basics (about 4–6 months)**: Learn the mechanisms of major algorithms such as supervised learning, unsupervised learning, and reinforcement learning, and how to actually use them. Participating in data analysis competitions like Kaggle is very important for gaining practical experience.
5. **Practice data analysis projects**: Gain experience in the entire process of collecting, cleaning, analyzing data, building models, evaluating them, and interpreting results using publicly available data.
6. **Deepen business and domain knowledge**: Deepening your knowledge of the industry and business you are analyzing will enable you to produce more practical and valuable analysis results.
7. **Improve communication skills**: The ability to explain analysis results clearly to non-experts is truly important. Try creating presentation materials and practicing explanations.

2.2.4. Certifications to Prove Data Scientist Skills

* **Statistics Qualification Test**: This qualification evaluates knowledge and application ability in statistics. Level 2 or higher is recommended.
* **G-Certification (Generalist Certification) / E-Certification (Engineer Certification)**: These are AI and deep learning related certifications organized by JDLA. G-Certification tests basic knowledge, and E-Certification tests practical ability.
* **Python Engineer Certification Exam**: This certification proves Python programming skills.
* **Data Scientist Certification (DS Certification)**: This certification, organized by the Data Scientist Association, evaluates a wide range of knowledge and skills.

2.3. Digital Marketing: “Strategic Communicator” Connecting Customers and Brands

2.3.1. Why is Digital Marketing “Profitable”?

Now that the internet and smartphones are commonplace, our consumer shopping behavior has changed significantly, hasn’t it? For companies, how to efficiently find customers and build relationships in various digital spaces has become the key to winning in business. Digital marketing professionals can understand this complex digital environment and contribute to increasing corporate sales with data-driven strategies, so high salaries can be expected.

2.3.2. Diverse Job Roles and Career Paths in Digital Marketing

The field of digital marketing is truly vast. There are various job roles depending on the specialization.

* **Web Marketer**: This job is responsible for all aspects of attracting customers to websites, improving them, and analyzing them. A wide range of knowledge is required, including SEO, SEM, web advertising operations, and access analysis.
 * **Main skills**: Web analytics tools (Google Analytics, etc.), SEO/SEM, advertising operations, data analysis, etc.
* **Content Marketer**: This job involves planning, creating, and delivering valuable content (articles, videos, etc.) to customers to deepen their connection with the brand.
 * **Main skills**: Writing, planning ability, SEO knowledge, content creation tools, etc.
* **SNS Marketer**: This job involves using social media to increase brand awareness, acquire customers, and strengthen connections with customers. They also handle SNS advertising operations.
 * **Main skills**: Understanding the characteristics of each SNS platform, SNS advertising operations, community management, and the ability to catch trends, etc.
* **Advertising Operations Manager**: This job involves formulating, operating, measuring the effectiveness of, and improving various web advertising strategies such as Google Ads, Yahoo! Ads, and SNS Ads. The mission is to maximize cost-effectiveness.
 * **Main skills**: Knowledge of advertising platforms, data analysis, budget management, A/B testing, etc.
* **CRM/MA Manager**: This job involves managing customer data, segmenting customers, and automating personalized communication using Customer Relationship Management (CRM) systems and Marketing Automation (MA) tools.
 * **Main skills**: Knowledge of CRM/MA tools, customer data analysis, email marketing, and the ability to design customer experiences, etc.

2.3.3. Efficient Learning Roadmap for Becoming a Digital Marketer from Scratch

In digital marketing, actually doing it is truly important. Let’s proceed with learning in the following steps!

1. **Learn marketing basics (about 1 month)**: First, understand the basic concepts of marketing (4Ps, SWOT analysis, etc.) and how consumers behave. Digital marketing is just one part of overall marketing.
2. **Understand how websites work (about 1 month)**: Basic knowledge of HTML and CSS, and how to operate CMS like WordPress, will be useful for creating content and improving sites.
3. **Learn web analytics basics (about 2 months)**: Learn how to interpret website data (number of visitors, time spent, conversion rate, etc.) using web analytics tools like Google Analytics. The ability to improve based on data is now essential.
4. **Learn SEO/SEM basics (about 2 months)**: Learn SEO (content optimization, on-site and off-site measures) to get traffic from search engines, and SEM, which uses search ads.
5. **Learn SNS marketing basics (about 1 month)**: Understand the characteristics of major SNS platforms and learn how to effectively disseminate information, operate ads, and build communities.
6. **Learn and practice web advertising basics (about 2–3 months)**: Learn the mechanisms of various web ads such as Google Ads, Yahoo! Ads, and SNS Ads, and the basics of who to show ads to, how much to spend on ads, and how to measure effectiveness. Actually running ads with a small budget will help you acquire practical skills.
7. **Learn and practice content marketing basics (about 2 months)**: Learn the process of planning, creating, and delivering valuable content to nurture customers. Please try it out on your own blog or SNS account.
8. **Practice and portfolio creation**: Gain experience by actually operating your own blog or SNS account, or by volunteering to help small businesses with digital marketing. Please compile your achievements into a “portfolio.”

2.3.4. Certifications to Prove Digital Marketing Skills

* **Google Analytics Individual Qualification (GAIQ)**: This is an official Google certification that proves your knowledge and skills in Google Analytics.
* **Google Ads Certification**: This is an official Google certification that proves your knowledge and operational skills in each Google Ads product.
* **Web Analytics Consultant**: This is a private certification that allows you to systematically learn web analytics knowledge and skills.

2.4. AI-Related Skills: “Cutting-Edge Intelligence” Creating the Future

2.4.1. Why are AI-Related Skills “Profitable”?

AI is one of the most innovative technologies in today’s society, isn’t it? It is already being used in almost all fields such as medicine, finance, manufacturing, and service industries. There are very few people who can develop, introduce, and operate AI technology. Therefore, companies are investing heavily in such specialists to gain an edge over competitors with AI. That’s why people with AI-related skills have very high market value and receive salaries commensurate with that.

2.4.2. Diverse Job Roles and Career Paths in AI-Related Skills

AI-related skills are truly broad, ranging from the development of artificial intelligence technology to its introduction and operation. There are various job roles.

* **Machine Learning Engineer**: This job involves designing, developing, deploying, testing, and operating machine learning models. Their main role is to build the models designed by data scientists into actual working systems.
 * **Main skills**: Programming (Python), machine learning frameworks (TensorFlow, PyTorch), cloud platforms (AWS SageMaker, etc.), software development, etc.
* **AI Researcher**: This job involves researching new AI algorithms, models, and theories to pioneer the cutting edge of AI technology. They are active in universities, research institutions, and corporate R&D departments.
 * **Main skills**: Advanced mathematics (linear algebra, probability and statistics, optimization), deep theoretical knowledge of machine learning and deep learning, ability to read papers, and ability to present research, etc.
* **AI Consultant**: This job involves proposing solutions using AI technology and supporting its introduction to solve business problems for companies. Not only technical knowledge, but also business understanding and communication skills are very important.
 * **Main skills**: General knowledge of AI technology, business analysis, consulting, project management, etc.
* **AI Product Manager**: This job involves overseeing the entire process from planning, development, release, and improvement of AI-powered products and services. They act as a bridge connecting technology, business, and user needs.
 * **Main skills**: Understanding of AI technology, product management, market analysis, user experience (UX) design, etc.

2.4.3. Efficient Learning Roadmap for Acquiring AI-Related Skills from Scratch

AI might seem difficult, but if you learn systematically, you can steadily acquire the skills.

1. **Understand the overall picture of AI (about 1 month)**: First, understand how major fields such as AI, machine learning, deep learning, natural language processing, and image recognition are connected. It is recommended to start with introductory books and online courses.
2. **Learn basic mathematics (about 2–3 months)**: Learn the basics of mathematics such as linear algebra, calculus, probability, and statistics. This mathematical knowledge is essential for understanding how AI algorithms work.
3. **Acquire Python programming (about 3–4 months)**: Acquire Python, which is most commonly used in AI development. Learn how to use libraries and frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
4. **Learn and practice machine learning basics (about 4–6 months)**: Learn the theory of major algorithms such as supervised learning, unsupervised learning, and reinforcement learning, and how to actually use them. Gaining practical experience in competitions like Kaggle is very important.
5. **Learn deep learning basics (about 3–4 months)**: Learn deep learning models such as neural networks, CNNs, and RNNs. If you are interested in image recognition or natural language processing, please focus on these.
6. **Acquire data processing and preprocessing (about 1–2 months)**: Skills in collecting, cleaning, preprocessing, and feature engineering data directly impact the performance of AI models.
7. **Work on practical projects**: Participate in Kaggle or open-source projects to hone your practical development skills. The experience of bringing your own ideas to life will also help maintain your motivation for learning.
8. **Always keep up with the latest information**: The evolution of AI technology is truly fast, so it is important to constantly check the latest papers, technical blogs, and news to absorb new knowledge.

2.4.4. Certifications to Prove AI-Related Skills

* **G-Certification (Generalist Certification)**: Organized by JDLA. Tests basic knowledge of AI and deep learning. Recommended for those who want to utilize AI in business.
* **E-Certification (Engineer Certification)**: Organized by JDLA. Tests the practical ability to use deep learning. This is for engineers who aim for more practical AI development.
* **Python Engineer Certification Exam**: This certification proves Python programming skills.
* **Statistics Qualification Test**: Evaluates knowledge and application ability in statistics. This statistical knowledge is essential for understanding the theoretical background of AI.
* **Vendor Certifications**: There are also AI/ML related certifications issued by each cloud provider, such as AWS Certified Machine Learning — Specialty and Google Cloud Certified — Professional Machine Learning Engineer.

Chapter 3: The Importance of “Human Skills” That Shine in the AI Era

No matter how much AI evolves, our uniquely human abilities are still something that AI cannot imitate. In fact, the more AI handles routine tasks, the more valuable these “human skills” become. Along with specialized skills, honing the human skills I will introduce now is a very important key to truly “never worrying about your livelihood” in the future.

* **Empathy**: This is the ability to understand and empathize with others’ feelings and perspectives. This skill is essential for grasping customers’ true needs and smoothly collaborating with team members.
* **Ethical Judgment**: This is the ability to make ethically sound decisions when using data and AI in complex situations. This judgment is becoming increasingly important in terms of AI fairness, transparency, and privacy protection.
* **Creativity**: This is the ability to generate new ideas and value. While AI learns from existing data, creating entirely new concepts or unprecedented solutions is still our human forte.
* **Interpersonal Relationship Building**: This is the ability to smoothly build relationships with people and collaborate with individuals from various backgrounds to achieve goals. It is very important for advancing team projects and building trust with customers.
* **Complex Problem-Solving Ability**: This is the ability to solve difficult problems that do not have a fixed method and require consideration from various perspectives. While AI excels at specific tasks, solving unknown challenges or complex problems involving many intertwined elements requires our comprehensive human thinking ability.
* **Critical Thinking**: This is the ability to critically analyze information and situations without blindly accepting them, and to discern the essence of things. In an era with abundant fake news and misinformation, this ability is essential for making sound judgments.
* **Adaptability**: This is the ability to flexibly adapt to rapidly changing environments and to proactively continue learning new knowledge and skills. The evolution of AI technology is truly fast, so a continuous learning attitude is required.

These skills are areas where AI struggles. Therefore, their value will continue to increase. By honing both specialized skills and these human skills, you will be able to powerfully navigate the AI era!

Summary: “Learning” as an Investment to Open Up the Future

Everyone, how was it?

In this paid article, I have discussed in detail four specialized skills — IT engineer, data scientist, digital marketing, and AI-related skills — and the “human skills” that support them, as practical skills to “never worry about your livelihood” in the AI era.

It is important to note that these skills are not acquired overnight. However, please be assured! By diligently following a systematic learning roadmap and gaining practical experience, anyone can steadily acquire skills and significantly enhance their market value.

The future, honestly, is uncertain. However, honing your skills and acquiring the ability to flexibly adapt to any changes is the most reliable “investment in the future” for us.

Now, why don’t you take a step forward today and elevate your career to the next stage?

— –

References

[1] Persol Research and Consulting. (October 25, 2024). *Labor shortage in 2035 will be 1.85 times that of 2023*. [https://rc.persol-group.co.jp/thinktank/column/202410250001.html](https://rc.persol-group.co.jp/thinktank/column/202410250001.html)

[2] Forbes JAPAN. (June 10, 2025). *Will it still be relevant in a few years? 10 “human skills” valued in the AI era*. [https://forbesjapan.com/articles/detail/79720](https://forbesjapan.com/articles/detail/79720)

[3] Adecco. (December 10, 2024). *December 2024 Labor Market Trend Information — National & Regional*. [https://www.adecco.co.jp/client/useful/labortrend_2024_dec](https://www.adecco.co.jp/client/useful/labortrend_2024_dec)