Classmethod Analyzes AI Adoption Gap Across 536 Japanese Companies — First-Ever Structural Insights Revealed Through Cross-Industry, Decision-Maker, and Organizational Analysis Across 8 Industries and 6 Diagnostic Patterns

Classmethod, Inc.'s AI Experience Center (AIXC) has conducted the 'Domestic Corporate AI Adoption Reality Survey 2026' targeting 536 Japanese companies and published the results in a white paper titled 'Domestic Corporate AI Adoption Reality Survey 2026 — Dissecting the AI Adoption Gap Revealed by 536 Japanese Companies' (hereinafter, 'the report') on July 9, 2026.

This report is a primary research document that quantifies the current state of AI adoption among Japanese companies, based on the AIXC Score—a metric out of 140 points calculated from 13 questions covering governance structure, implementation phase, and policy formulation—and six diagnostic patterns that classify companies by their combination of organizational structure, policy, and implementation.

Rather than a simple usage survey, this report is the first to uncover the root causes of disparities through cross-analysis across multiple dimensions such as organizational structure, policy, decision-makers, and job composition.

Download the free white paper (PDF) here: https://classmethod.jp/download/aixc-aisurveyreport/

Survey Summary

1. AI adoption levels decrease with company size, yet disparities within the same size category due to presence or absence of dedicated teams are striking

Clear differences in average scores were observed between large, mid-sized, and small-to-medium enterprises (SMEs). However, even within the 'large enterprise' category, companies with the highest and lowest performance coexist. The presence or absence of a dedicated team alone creates a score gap larger than differences between company sizes. This suggests that AI adoption is determined less by company size and more by the managerial decision to establish a dedicated team.

2. Companies led by dedicated executives such as CIOs or CDOs scored 32 points higher on average than those without a designated leader

Analysis of average AIXC Scores by type of decision-maker revealed a maximum 32-point gap between the top group (led by dedicated executives like CIOs or CDOs) and the bottom group (no designated leader). Even when technological capabilities and investment scales are similar, significant differences in adoption levels emerge. This indicates that the presence of a governance structure to drive AI initiatives has a greater impact than technical capability or budget.

3. Beyond the common challenge of 'talent shortage,' industries face different structural challenges such as 'security' and 'data infrastructure'

'AI talent and skill shortages' ranked as the top challenge across all eight industries. However, the structure of challenges beyond the top issue varies by industry. In professional services and finance/insurance, security concerns surpassed the need for governance setup as a challenge. In manufacturing, data infrastructure and governance setup were equally pressing. In the information and communications sector, talent shortage accounted for 30% of challenges—two to four times higher than in other industries. Since industry-wide solutions cannot address these differences, companies must identify their industry-specific challenges before formulating responses.

4. Companies with all three elements—dedicated teams, published policies, and clear leadership (e.g., CIO)—maintain high adoption levels regardless of size

Companies that have all three—dedicated teams, published company-wide policies, and clearly defined leaders such as CIOs—consistently achieved high scores regardless of size. Conversely, a significant drop in scores was observed when any one of these elements was missing. These factors do not simply add up; they work synergistically. Even with sufficient scale, budget, and talent, organizational adoption remains difficult without the alignment of structure, policy, and leadership.

Comment from AIXC Representative

The starting point for designing this survey was the fact that 'there was no benchmark.' In 2026, there was no common framework to answer whether Japanese companies' AI adoption was progressing or lagging.

When the data from 536 companies was compiled, the first thing that stood out was not the 81.9% figure reflecting competitive anxiety over AI adoption delays, but the underlying 22.9%—the rate of full-scale deployment across the entire company and multiple departments during the AI adoption phase. The 59.0-point gap represents the depth of companies that 'have awareness but cannot fully mobilize their organization.' This gap was not due to technical issues. The presence or absence of two managerial decisions—organizational structure and policy—explained the majority of the score differences.

This is precisely why we intend to continue this survey annually as a 'health record of Japanese corporate AI adoption.' Only by tracking changes over time, rather than taking a one-time snapshot, can we record when and what triggered transformation. This year's data is the first page of that record.

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

Survey Overview

Survey Name

Domestic Corporate AI Adoption Reality Survey 2026 (AI Survey)

Conducted by

Classmethod, Inc. AI Experience Center (AIXC)

Target

Japanese domestic companies

Valid Responses

536 responses

Survey Period

April 15, 2026 – June 20, 2026

Number of Questions

13 mandatory + 3 optional

Maximum Score

140 points (AIXC Score)

Size Classification

Three categories as defined by the Ministry of Economy, Trade and Industry (Large / Mid-sized / SME)

Employee Data

Based on Teikoku Databank (TDB) registration data supplemented with public information; 99.3% coverage

White Paper

https://classmethod.jp/download/aixc-aisurveyreport/

About AI Experience Center (AIXC)

AIXC is a specialized center established by Classmethod, Inc. in October 2025 to support corporate AI adoption. It provides end-to-end services including diagnosis, strategy formulation, implementation support, and talent development, leveraging cloud and AI technologies from AWS, Anthropic, Google, and others. This survey will be conducted annually to objectively record and disseminate the actual state of AI adoption among Japanese companies and support corporate decision-making.

【About Classmethod】

Classmethod, Inc. is a technology partner that supports corporate digital transformation (DX), focusing on cloud-native technologies such as Amazon Web Services (AWS), data analytics, mobile, IoT, AI/machine learning, and more. Since 2015, it has been continuously recognized as a top-tier AWS partner and has won the 'AWS Consulting Partner of the Year' award five times. In 2022, it received the global 'SI Partner of the Year' award and was a finalist in 2023, establishing itself as a world-class AWS partner. To date, it has supported approximately 5,600 companies and managed over 40,000 AWS accounts. The company also promotes a culture of technical outreach, publishing over 60,000 technical articles on its owned media platform 'DevelopersIO' and operating 'Zenn,' a knowledge-sharing platform for engineers, contributing to the growth of the technical community. Guided by the mission to 'continuously contribute to the creative activities of all people,' Classmethod delivers optimal technologies that enhance its clients' business value.