AI is no longer just the future – it’s the present, and Japan is at the forefront of this transformation. Let’s explore how AI is reshaping automation in the Japanese market, highlighting specific use cases and examining how this trend is expected to evolve from 2025 to 2030.
Table of Contents
AI applications status in the Japan market
Before diving into specific use cases, let’s take a look at how AI has developed across the Japanese market.
The great support of technological policies
According to The Diplomat, Japan’s technological policies are focused on driving a comprehensive rollout of AI, with the government aiming to position the nation as a leader in next-generation technology. In addition, A GLOCOM study suggests that Generative AI (Gen-AI) could unlock 148.7 trillion yen in productive capacity, over 20% of Japan’s current GDP, highlighting the vast potential of AI in enhancing economic growth. To harness this opportunity, Japan is making significant investments in subsidizing its AI and semiconductor industries, working toward greater technological autonomy and increasing its influence in global emerging markets.
Increase in patent application
The Japan Patent Office reported a significant surge in domestic applications for AI-related inventions, with around 10,300 patents filed in 2022 alone. This upward trend has been consistent since 2014, reflecting the growing interest in AI technologies across various sectors, particularly in deep learning. The rising number of patent filings highlights Japan’s strong commitment to advancing AI innovation and its potential to drive future technological developments.
Automation with AI has become a growing trend
With the widespread applications of AI, AI-powered automation is gaining significant attention in Japan’s market. Japan has long been adept at overcoming challenges posed by limited resources, especially labor, and has historically been a leader in technological innovation. Automation and robotics are a natural fit for Japan, as they can enhance human labor and address labor shortages. In fact, robotic process automation in Japan is projected to grow significantly, with a compound annual growth rate (CAGR) of 40.9% expected from 2024 to 2030 (Grand View Research). As AI continues to evolve, this growth is anticipated to accelerate further, fueled by Japan’s increasing demand for advanced automation solutions.
Automation and AI use cases
Intelligent automation (IA) combines traditional automation with AI to enhance efficiency, accuracy, and adaptability across various industries. Here are some notable use cases of intelligent automation categorized by industry:
Manufacturing
- Predictive maintenance: IA systems monitor equipment health in real-time to predict failures before they occur, allowing for timely maintenance and reducing downtime
- Quality control: Automated visual inspection systems utilize AI to identify defects in products, enhancing quality assurance and minimizing waste
- Supply chain optimization: IA improves inventory management, demand forecasting, and logistics operations, leading to cost savings and more efficient supply chains
Retail
- Inventory management: IA predicts optimal inventory levels using historical sales data, helping retailers avoid stockouts and overstock situations
- Customer service: Chatbots assist customers with inquiries and product recommendations, enhancing the shopping experience
- Returns processing: IA simplifies the returns process by guiding customers through steps and updating inventory systems accordingly.
Finance
- Fraud detection: Machine learning algorithms analyze transaction patterns in real-time to detect fraudulent activities and enhance security.
- KYC processes: Financial institutions automate Know Your Customer (KYC) tasks by extracting and validating customer data, improving compliance and efficiency.
- Loan processing: Intelligent bots streamline the loan application process by automating data extraction and credit scoring, reducing manual paperwork.
Insurance
- Claims processing: IA speeds up claims processing by automating document verification and validation against policy terms, improving customer satisfaction.
- Underwriting: Automation assists insurers in pricing risks accurately by consolidating data from various sources for better decision-making.
- Fraud detection: AI algorithms analyze claims data to identify potential fraudulent activities effectively
Pharmaceutical
- Automated invoice management: IA automates the extraction, validation, and processing of invoices. This reduces manual data entry errors and speeds up the approval cycle, allowing for quicker payments to suppliers and improved cash flow management.
- Streamlined order management: IA optimizes order processing by automating order entry, tracking, and fulfillment. This reduces cycle times and enhances accuracy in order delivery, ultimately improving customer satisfaction.
- Inventory management: Intelligent automation helps manage inventory levels by predicting demand based on historical data, ensuring that pharmaceutical companies maintain optimal stock levels without overstocking or stockouts
Forecast the trend of AI in automation 2025 – 2030
As AI continues to evolve, what can we expect in the near future? Here are some key takeaways on the trend of AI automation in the years ahead.
AI agents
According to McKinsey, AI agents represent the next frontier of generative AI, evolving from knowledge-based automation tools to systems capable of executing complex, multistep workflows. These intelligent agents can independently perform intricate tasks, make decisions, and adapt to new challenges without the need for human oversight. This trend is set to enable organizations to automate more sophisticated processes across various sectors, significantly enhancing operational efficiency while reducing reliance on human intervention.
Proprietary AI models
As AI adoption progresses, companies are transitioning from using general-purpose AI models to developing proprietary Large Language Models (LLMs) tailored to their specific needs. By leveraging unique internal datasets, these customized AI solutions are designed to automate workflows more effectively, fine-tuning models on domain-specific data for improved actionability and task-oriented outcomes.
Many companies have already started adopting this trend. For instance, JPMorgan Chase and Bloomberg are building LLMs based on their internal data to enhance efficiency and decision-making, offering unique insights and more precise reporting.
Edge computing
The rise of edge computing will complement AI applications by enabling real-time data processing closer to the source of generation. This integration is crucial for applications that require swift decision-making, such as autonomous vehicles and industrial IoT systems. This is particularly important as Japan is making significant strides with its “Zeta-class” supercomputer project, which is set to be 1,000 times faster than any existing systems. The project highlights Japan’s commitment to advancing computational power, positioning it as a leader in next-gen AI and edge computing technologies
Conclusion
Automation and AI are advancing at an unprecedented pace, especially in a promising market like Japan. For businesses, the key is to keep up with this rapid progress by staying informed and quickly implementing these technologies to optimize operations and maintain a competitive edge.
akaBot (FPT) is the operation optimization solution for enterprises based on the RPA (Robotic Process Automation) platform combined with Artificial Intelligence, Process Mining, OCR, Intelligent Document Processing, Machine Learning, Conversational AI, etc. Serving clients in 21+ countries, across 08 domains such as Banking & Finances, Retail, IT Services, Manufacturing, and Logistics…, akaBot is featured in “Voice of the Customer” for Robotics Process Automation” by Gartner Peer Insights, G2, and ranked as Top 6 Global RPA Platform by Software Reviews. akaBot also won the prestigious Stevie Award, The Asian Banker Award 2021, Everest Group’s RPA Products PEAK Matrix® 2023, etc.
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