Education Sector AI Adoption at 76.9%, Healthcare at 13.8% — A 5.6x Gap Between Industries
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What you'll learn from this article
A survey conducted by Surisuta Co., Ltd. in May 2026 found significant differences in how AI is being used depending on the industry.
The survey was conducted among 400 company employees nationwide. The results showed that the industry with the highest AI usage was "Education & HR" at 76.9% — meaning nearly 8 out of every 10 people in that sector are using AI.
On the other end of the spectrum, "Healthcare & Social Welfare" recorded the lowest AI adoption rate at 13.8% — roughly 1 in 10 people. The gap between the top and the bottom is as large as 5.6 times.
These figures show that even within Japan, the pace of AI adoption varies dramatically depending on the industry.
Why is AI usage so high in the Education & HR industry? There are two main reasons.
The first is that the nature of the work aligns well with AI. This industry involves a lot of text-based tasks — creating educational materials, counseling, writing proposals, and so on. Generative AI tools (AI that automatically produces text and images) like ChatGPT (an AI that generates text through conversational interaction) are particularly well-suited to exactly these kinds of tasks.
The second reason is a culture that is open to adopting new technologies. Because the education sector constantly deals with the latest knowledge and skills, there tends to be less resistance to AI technology. In other words, there is an industry-wide attitude of "let's give it a try."
"IT, Software & Telecommunications," which came in second at 71.4%, also posted a high figure. In this industry, 34.3% of workers use AI on a daily basis — the highest frequency of any sector. With many engineers in the workforce and easy access to AI tools, high adoption rates come naturally.
The reason Healthcare & Social Welfare's AI adoption rate is as low as 13.8% comes down to circumstances that demand caution.
For one, the industry handles patient information — data with an extremely high level of confidentiality. Personal information protection laws (legislation protecting individual privacy) and medical regulations are strict, making it difficult to simply start using AI tools. Entering patient data into a cloud-based AI (AI accessed via the internet) like ChatGPT carries the risk of information leaks.
There is also the issue of medical liability. If AI makes an incorrect diagnosis, who is responsible? Because this question remains unresolved, healthcare settings have no choice but to proceed with caution.
It should be noted, however, that this refers to general-purpose generative AI. The healthcare industry has separately introduced specialized systems such as medical imaging AI (AI that analyzes X-ray and CT images) and medical interview support AI (AI that infers illnesses from patient symptoms). In other words, while AI tailored specifically to the medical field is advancing, the actual situation is that the adoption rate of general-purpose AI (AI that can be used for a wide range of applications), such as ChatGPT, remains low.
Interestingly, the AI incident rate in Healthcare & Social Welfare was 3.4% — the lowest of any industry — while the rate of guideline development stood at 0.0%. This reflects a situation of "no accidents because it isn't being used, but no rules being developed either."
Behind the industry gap lie challenges that Japan as a whole must address.
The first is a shortage of talent. There is a critical lack of data scientists (specialists who analyze data and apply insights to business) and engineers capable of developing and operating AI. AI education at universities and other institutions is also lagging, and another problem is that talented individuals are being drawn away to overseas companies that offer better compensation.
The second is a lack of understanding among management. Many executives at Japanese companies do not fully grasp the value of AI or its impact on business. While companies in the United States and China actively invest with a "try it first" mentality, Japan is notably more inclined to sit on the sidelines and wait.
The third is a conservative attitude toward data use. In Japan, the management of personal information is extremely strict, creating an environment in which companies find it difficult to make free use of customer data. Furthermore, data formats vary widely across companies and government agencies, making cross-organizational (spanning multiple organizations) data utilization difficult.
Large enterprises can establish dedicated AI promotion teams and allocate experimental budgets that allow for failure. Small and medium-sized businesses, on the other hand, often rely on existing staff to take on AI-related duties in addition to their regular roles, and even subscription costs (monthly fees) of several hundred thousand yen can bring adoption to a halt when "the results aren't visible." This difference in organizational structure and investment approach is another factor driving the gap.
Here are the AI adoption rates for all 10 industries revealed in this survey.
Where did your industry land? The top three sectors (Education & HR, IT, and Advertising) all exceed 60%, meaning the majority of workers in those fields are already using AI on a daily basis.
Meanwhile, Finance & Insurance (25.0%) and Public Sector & Government (23.8%) posted surprisingly low figures. Like healthcare, these industries handle personal information and sensitive data, and that caution is reflected in the numbers.
Manufacturing (41.4%) sits in the middle of the pack, but AI's potential applications — factory automation, quality control, and more — are said to be wide-ranging. It is one of the industries most likely to see its adoption rate rise in the future.
Here is a summary of the key findings from this survey.
Closing this gap will require thinking about approaches to AI adoption that are tailored to each industry's unique characteristics. Industries with strict regulations, such as healthcare and finance, will need dedicated secure AI systems to be developed. For small and medium-sized businesses, meanwhile, it is important for management to understand the value of AI, start small, and build up a track record of success.
AI is not a cure-all, but when used appropriately, it is a tool that can significantly improve operational efficiency. Why not use these survey results as a reference and think about how AI could be put to work in your own industry?
This article is a cross-post from AI Friends.
