Beyond PoC Failures: Unraveling the Implementation Phase of 'AI-Integrated System Development' from Wakka Inc.'s 14 Years of Expertise
Wakka Inc. releases insights based on 14 years of development experience, focusing on the implementation phase of "AI-integrated system development," which involves integrating generative AI into existing systems. The company aims to help Japanese firms move beyond Proof-of-Concept (PoC) failures, explaining success factors and market trends.
📋 Article Processing Timeline
- 📰 Published: April 28, 2026 at 19:00
- 🔍 Collected: April 28, 2026 at 10:32
- 🤖 AI Analyzed: April 28, 2026 at 11:15 (43 min after Collected)
Wakka Inc. (Headquarters: Chiyoda-ku, Tokyo; Representative Director: Hiroyuki Hirano; hereinafter "Wakka Inc."), which handles DX promotion and system development, has compiled its insights gained from its own development sites for Japanese companies considering and promoting the business introduction of generative AI.
This analysis is not a comprehensive survey report that broadly organizes market trends, but rather a unique perspective on the current state of generative AI utilization. It is based on the knowledge gained by Wakka Inc., which operates a development organization of over 150 members, through its experience in Web system, business system, and DX support development, as well as its recent surge in "AI-integrated" projects that embed generative AI into existing systems.
While some public statistics are referenced, it should be noted that our views and problem perceptions are based purely on our on-site experience and do not necessarily represent the entire industry.
■ Summary
The generative AI market is rapidly expanding both domestically and internationally, and corporate utilization is transitioning from the "trial introduction" phase to the "implementation and operation" phase.
The role of generative AI is changing from "a tool for individuals" to "a business infrastructure embedded in business systems."
In AI-integrated system development, PoC design, internal data integration, security, and operation design are critical success factors.
■ 1. Market Trends: The Generative AI Market is in a Period of Rapid Expansion
The generative AI market is growing rapidly both in Japan and globally.
According to a survey by Fuji Chimera Research, the Japanese generative AI market is expected to expand to over 1.7 trillion yen by FY2028.
Furthermore, the Ministry of Internal Affairs and Communications' "Information and Communications White Paper 2025" (IDC Japan survey) presents the following figures for the entire Japanese AI system market:
2024: 1,341.2 billion yen
2029: 4,187.3 billion yen (forecast)
The generative AI market continues to show high growth in the global market as well, expected to expand from $36.1 billion in 2024 (19.6% of the total AI market) to $356.1 billion in 2030 (43.1% of the total).
Structural factors such as corporate DX promotion, addressing labor shortages, and improving operational efficiency are strongly driving the adoption of generative AI.
■ 2. Changes in Corporate Utilization: From "Individual Tools" to "Business Infrastructure"
The corporate use of generative AI has changed significantly over the past 1-2 years.
In the initial stages of introduction, the focus was primarily on "individual operational efficiency" where employees used generative AI individually:
* Meeting minute creation
* Document generation
* Translation
* Idea generation
However, there is now an increasing trend towards integrating generative AI into business systems.
Even in projects consulted by our company, the main application areas are as follows:
* Customer Support: Inquiry response chatbots
* Content Creation: Automatic generation of marketing texts and materials
* Software Development: Code generation, review, testing support
* Data Analysis: Customer behavior analysis and report generation
Generative AI is expanding its role from "a tool for individuals" to a part of the business infrastructure of companies.
In the field of system development, designing architectures with generative AI integrated into projects is becoming common.
Related article: Can generative AI be used in system development? Thorough explanation of benefits and corporate examples
https://wakka-inc.com/blog/20625/
■ 3. Changes in Development Practices: The Rise of AI-Integrated System Development
Along with these changes in corporate utilization, the positioning of generative AI in system development practices is also evolving.
A recent characteristic is the increasing number of cases where AI is implemented not as a standalone solution, but in conjunction with existing systems.
Even in projects handled by our development team, the following implementation patterns are increasing:
* Internal knowledge search AI (RAG type)
* Inquiry response AI
* Sales support AI
* Document creation and summarization systems
This trend is backed by the widespread adoption of AI APIs (mechanisms for embedding AI functions into proprietary systems), enabling companies to integrate AI capabilities into their systems without developing AI models from scratch.
However, system development incorporating generative AI possesses fundamentally different characteristics from traditional web system development.
Since outputs are non-deterministic and data quality significantly impacts the accuracy of deliverables, a different design philosophy is required at each phase: planning, PoC, implementation, and operation.
Furthermore, in recent years, there has been an expanding demand for customized AI that utilizes company-specific data.
As a result, in the era of generative AI system development, the following four points have become crucial design themes:
* Integration with internal data (data preparation, access control design, search accuracy)
* Security design (input/output masking, log management, usage policies)
* Integration into business workflows (integration with existing systems, UI/UX design)
* AI operation management (accuracy monitoring, model...
This analysis is not a comprehensive survey report that broadly organizes market trends, but rather a unique perspective on the current state of generative AI utilization. It is based on the knowledge gained by Wakka Inc., which operates a development organization of over 150 members, through its experience in Web system, business system, and DX support development, as well as its recent surge in "AI-integrated" projects that embed generative AI into existing systems.
While some public statistics are referenced, it should be noted that our views and problem perceptions are based purely on our on-site experience and do not necessarily represent the entire industry.
■ Summary
The generative AI market is rapidly expanding both domestically and internationally, and corporate utilization is transitioning from the "trial introduction" phase to the "implementation and operation" phase.
The role of generative AI is changing from "a tool for individuals" to "a business infrastructure embedded in business systems."
In AI-integrated system development, PoC design, internal data integration, security, and operation design are critical success factors.
■ 1. Market Trends: The Generative AI Market is in a Period of Rapid Expansion
The generative AI market is growing rapidly both in Japan and globally.
According to a survey by Fuji Chimera Research, the Japanese generative AI market is expected to expand to over 1.7 trillion yen by FY2028.
Furthermore, the Ministry of Internal Affairs and Communications' "Information and Communications White Paper 2025" (IDC Japan survey) presents the following figures for the entire Japanese AI system market:
2024: 1,341.2 billion yen
2029: 4,187.3 billion yen (forecast)
The generative AI market continues to show high growth in the global market as well, expected to expand from $36.1 billion in 2024 (19.6% of the total AI market) to $356.1 billion in 2030 (43.1% of the total).
Structural factors such as corporate DX promotion, addressing labor shortages, and improving operational efficiency are strongly driving the adoption of generative AI.
■ 2. Changes in Corporate Utilization: From "Individual Tools" to "Business Infrastructure"
The corporate use of generative AI has changed significantly over the past 1-2 years.
In the initial stages of introduction, the focus was primarily on "individual operational efficiency" where employees used generative AI individually:
* Meeting minute creation
* Document generation
* Translation
* Idea generation
However, there is now an increasing trend towards integrating generative AI into business systems.
Even in projects consulted by our company, the main application areas are as follows:
* Customer Support: Inquiry response chatbots
* Content Creation: Automatic generation of marketing texts and materials
* Software Development: Code generation, review, testing support
* Data Analysis: Customer behavior analysis and report generation
Generative AI is expanding its role from "a tool for individuals" to a part of the business infrastructure of companies.
In the field of system development, designing architectures with generative AI integrated into projects is becoming common.
Related article: Can generative AI be used in system development? Thorough explanation of benefits and corporate examples
https://wakka-inc.com/blog/20625/
■ 3. Changes in Development Practices: The Rise of AI-Integrated System Development
Along with these changes in corporate utilization, the positioning of generative AI in system development practices is also evolving.
A recent characteristic is the increasing number of cases where AI is implemented not as a standalone solution, but in conjunction with existing systems.
Even in projects handled by our development team, the following implementation patterns are increasing:
* Internal knowledge search AI (RAG type)
* Inquiry response AI
* Sales support AI
* Document creation and summarization systems
This trend is backed by the widespread adoption of AI APIs (mechanisms for embedding AI functions into proprietary systems), enabling companies to integrate AI capabilities into their systems without developing AI models from scratch.
However, system development incorporating generative AI possesses fundamentally different characteristics from traditional web system development.
Since outputs are non-deterministic and data quality significantly impacts the accuracy of deliverables, a different design philosophy is required at each phase: planning, PoC, implementation, and operation.
Furthermore, in recent years, there has been an expanding demand for customized AI that utilizes company-specific data.
As a result, in the era of generative AI system development, the following four points have become crucial design themes:
* Integration with internal data (data preparation, access control design, search accuracy)
* Security design (input/output masking, log management, usage policies)
* Integration into business workflows (integration with existing systems, UI/UX design)
* AI operation management (accuracy monitoring, model...