Press release

An Anatomy of the AI Adoption Gap: Insights from 536 Companies in Japan

First Cross-Analysis of 8 Industries and 6 Diagnostic Patterns, Revealing Three Structural Factors Behind the Gap: Company Size, Industry, and Decision-Makers

The AI Experience Center (AIXC), operated by Classmethod, Inc., surveyed 536 companies in Japan for its “Survey on the Actual State of AI Adoption among Japanese Corporations 2026” (AI Survey). On July 9, 2026, AIXC published these findings in a white paper titled, “Survey on the Actual State of AI Adoption among Japanese Corporations 2026: An Anatomy of the AI Adoption Gap, Insights from 536 Domestic Companies.”

The white paper is a primary research report quantifying how Japanese enterprises actually utilize AI. It relies on two key tools: the AIXC Score, a 140-point benchmark calculated from 13 required questions covering promotion structure, adoption phase, and policy development, alongside six diagnostic patterns that classify companies based on their combination of structure, policy, and implementation.

Rather than measuring adoption rates alone, the report addresses a different question: why these gaps occur. Through a cross-analysis across multiple axes, including promotion structure, company-wide policy, decision-maker type, and occupational composition, the report uncovers the underlying causes for the first time.

Download the white paper PDF (free):https://classmethod.jp/english/download-classmethods-2026-ai-survey-whitepaper/

Summary of Findings

1. AIXC Scores decline as company size decreases, but the sharpest gaps appear within the same size tier, driven by the presence of a dedicated team.

Average scores showed clear differences among large enterprises, mid-tier enterprises, and SMEs. However, even within the large-enterprise category, top and bottom performers coexist. The presence of a dedicated team produced a score gap larger than the differences between company size brackets. This indicates that AI adoption maturity is driven less by company size itself and more by a management decision: whether to establish a dedicated team.

2. Companies led by a dedicated executive, such as a CIO or CDO scored 32 points higher on average than those without a designated lead.

When analyzing average AIXC Scores by decision-maker type, AIXC found a gap of up to 32 points between the top group (led by dedicated executives such as CIOs or CDOs) and the bottom group (lacking a designated lead). Large differences in adoption rates persisted even among companies with similar technical access and investment levels. This suggests that having a dedicated structure to drive adoption has a higher impact than technical skill or budget alone.

3. Aside from the universal challenge of talent shortages, the breakdown of challenges varies by industry, ranging from security concerns to data infrastructure.

“A shortage of AI talent and skills” ranked first across all eight industries. However, secondary challenges varied widely. In professional services, finance, and insurance, security concerns outweighed the need to establish promotion structures. In manufacturing, establishing data infrastructure and promotion structure were rated at similar levels. In the information and communications industry, talent shortages accounted for 30% of the responses, two to four times the level in other industries. Industry-wide solutions alone are insufficient; measures must be designed to address each industry’s distinct challenges.

4. Companies that combine a dedicated team, a published company-wide policy, and clear leadership (CIOs and CDOs) maintain high scores regardless of size.

Companies fulfilling all three conditions clustered in the high end of the score range regardless of company size. Conversely, missing even one of these conditions caused scores to drop significantly. These factors do not act in isolation; they compound one another. Even with adequate company size, budget, and personnel, organization-wide adoption remains difficult to achieve unless promotion structure, company-wide policy, and a designated lead are all present.

Comment from AIXC on Publication

“The primary motivation for designing this survey was the lack of a benchmark,” said Mr. Tateno. “Are Japanese enterprises advancing or falling behind in AI adoption? As of 2026, there was no standardized framework to answer that question.

“When analyzing the data from 536 companies, the first figure that caught my attention was not the 81.9% who felt a sense of urgency about falling behind competitors on AI. Rather, it was the 22.9% rate of AI systems in production company-wide or across multiple departments. That 59.0-point gap represents a vast number of organizations that acknowledge the urgency but struggle to get their companies to move. This gap is not a technical issue; it stems mostly from management decisions around promotion structure and corporate policy.

“This is why we intend to conduct this survey annually as an ongoing diagnostic index for AI adoption at Japanese enterprises. A single snapshot cannot capture when transformation occurs or what triggered it. Only tracking change over time can. This year’s data marks our starting point.”

Tsutomu Tateno, Director, AI Experience Center, Classmethod, Inc.

Survey Overview

Survey NameSurvey on the Actual State of AI Adoption Among Japanese Corporations 2026 (AI Survey)
Conducted ByClassmethod, Inc., AI Experience Center (AIXC)
TargetEnterprises operating in Japan
Valid Responses536
Survey PeriodApril 15 – June 20, 2026
Number of Questions13 mandatory questions + 3 optional questions
Maximum Score140 points (AIXC Score)
Company Size ClassificationThree brackets aligned with the Ministry of Economy, Trade and Industry (METI) framework (large enterprises / mid-tier enterprises / SMEs)
Employee Data SourceTeikoku Databank (TDB) corporate registry supplemented by publicly available disclosures, 99.3% data coverage rate
White Paper Linkhttps://classmethod.jp/english/download-classmethods-2026-ai-survey-whitepaper/

About the AI Experience Center (AIXC)

Established by Classmethod, Inc. in October 2025, AIXC is a specialized center dedicated to supporting enterprise AI adoption. Leveraging cloud and AI technologies from AWS, Anthropic, Google and other platforms, AIXC provides end-to-end services covering diagnostics, strategy formulation, implementation, and talent development. AIXC plans to conduct this survey annually to record and publish an objective view of AI adoption at Japanese enterprises and support executive decision-making.

About Classmethod, Inc.

Classmethod, Inc. is a technical partner that supports corporate digital transformation (DX) with a focus on cloud-native technology areas, including Amazon Web Services (AWS), data analysis, mobile, IoT, and AI/machine learning. In terms of AWS support, Classmethod has been recognized as a top-tier partner continuously since 2015 and has won the “AWS Consulting Partner of the Year” (awarded to the top partner in Japan) five times. In 2022, it received the global accolade of “SI Partner of the Year,” and was named a finalist again the following year in 2023, establishing its position as a world-class AWS partner in both name and substance.

To date, Classmethod has supported approximately 5,600 companies and managed/built a cumulative total of over 40,000 AWS accounts. The company also heavily emphasizes a culture of technical knowledge sharing among its engineers, having published over 60,000 technical articles on its owned media “DevelopersIO.” Additionally, it operates “Zenn,” a knowledge-sharing platform for engineers, contributing further to the development of the tech community. Guided by the philosophy of “continuously contributing to the creative activities of all people,” Classmethod proposes optimal technologies that enhance its customers’ business value.

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