How Generative AI Modernized a 450 Man-Month Legacy System in 2 Months | Kakuyasu Case Study

Legacy core systems often become impenetrable black boxes for organizations after decades of use. For Kakuyasu, a liquor wholesaler with over 100 years of history, a 30-year-old core system had reached a point where manual analysis seemed insurmountable. Generative AI changed that trajectory.

📑Table of Contents

1. The “Unopenable Box” Problem in Legacy Core Systems
2. The 450 Man-Month Estimate and Its Real-World Barriers
3. How Generative AI Enabled Decoding in Two Months
4. Kakuyasu’s 100-Year “Rebirth” Strategy and the Role of AI
5. Practical AI Adoption Points for Enterprise IT Teams Today
6. Traditional vs Generative AI-Assisted Analysis Comparison
7. Frequently Asked Questions (FAQ)
8. Summary

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The “Unopenable Box” Problem in Legacy Core Systems

A core system that had run for 30 years had grown so complex that understanding its full structure was nearly impossible. Traditional methods required experts to manually trace code and dependencies, leading to high risks of knowledge concentration in a few individuals. The system had effectively become a black box, making any meaningful updates or extensions extremely difficult. Kakuyasu Group’s subsidiary faced exactly this challenge with its long-running core system.

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The 450 Man-Month Estimate and Its Real-World Barriers

Initial estimates put the analysis effort at 450 man-months, requiring a large dedicated team over an extended period. In practice, resource constraints and the risk of knowledge silos often stalled such projects before they could begin. The sheer scale of the estimate itself became a barrier to action for many IT departments. Independent reports indicate the actual effort was reduced to roughly one month through generative AI.

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How Generative AI Enabled Decoding in Two Months

By leveraging generative AI through Amazon Bedrock, the team achieved a dramatic reduction in analysis time. The AI automatically parsed the source code, mapped dependencies, and generated documentation. What would have taken hundreds of person-months was reduced to approximately two months for the core decoding work. Automated documentation generation further lightened the load on the small team. Details are available in the PR TIMES press release and digital-gorilla.co.jp AI Lab report.

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Kakuyasu’s 100-Year “Rebirth” Strategy and the Role of AI

Founded in 1921, the Kakuyasu group views IT modernization as a strategic rebirth rather than a simple upgrade. The adoption of generative AI is part of a broader effort to ensure the company’s relevance for the next century. A presentation at AWS Summit Japan 2026 by project leader Nobuaki Ishii is planned to share these insights with the industry.

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Practical AI Adoption Points for Enterprise IT Teams Today

IT teams facing similar legacy challenges should start with small-scale pilots using services like Amazon Bedrock. This allows validation of the approach with minimal upfront investment. Establishing a hybrid workflow where humans review AI-generated outputs helps maintain accuracy and builds internal expertise. Security controls were appropriately applied in the Kakuyasu case.

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Traditional vs Generative AI-Assisted Analysis Comparison

Item	Traditional Approach	Generative AI Support
Analysis Duration	450 man-months	2 months (effectively ~1 month)
Required Experts	Large dedicated team	Small team + AI
Risk	Knowledge concentration	Automated documentation
Scalability	Low	High

Source: PR TIMES press release and digital-gorilla.co.jp AI Lab report (as of June 2026)

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Frequently Asked Questions (FAQ)

Can generative AI truly understand legacy code?

Generative AI models trained on vast codebases can identify structures, dependencies, and logic patterns effectively. However, they serve best as powerful assistive tools rather than complete replacements for human oversight. Human review remains essential.

How are security and confidential information handled?

When using cloud-based AI services, organizations should consider data anonymization or private deployment options to mitigate leakage risks. The Kakuyasu case was executed under appropriate security controls.

How much was decoded in two months?

The team completed mapping of the primary code structure, dependency relationships, and foundational documentation. Full refactoring remains a longer-term effort, but the initial analysis phase was significantly accelerated. Independent sources report issue resolution within one month.

Is this approach reproducible at other companies?

Reproducibility depends on system size and complexity. Smaller or moderately complex legacy systems often see even faster results. Reviewing official documentation and independent case studies is recommended before starting.

What happens to ongoing maintenance?

AI-generated documentation provides a foundation for sustainable maintenance processes. Regular use of AI tools helps prevent knowledge silos and keeps system visibility high over time.

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Summary

Generative AI offers a practical path to untangle even the most entrenched legacy core systems. The Kakuyasu group’s experience demonstrates how a century-old company can leverage modern tools for strategic renewal. IT teams are encouraged to begin with targeted pilots and adapt the approach to their specific environment. For further details, consult official sources and related articles on this topic.

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