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30 Jul 2026
T Dao
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As artificial intelligence continues to reshape the technology landscape, its role is expanding beyond content generation and productivity assistance. AI is increasingly becoming a practical enabler of enterprise system modernization, helping businesses accelerate complex migration processes, improve software quality, and address the limitations of aging technology infrastructure.
Against this backdrop, NTQ Japan successfully hosted the seminar “AI-Powered Software Development × Migration Practices” on July 24 at Tokyo Innovation Base. The event welcomed nearly 100 clients, partners, and NTQ members, creating an opportunity for technology professionals and business leaders to exchange practical perspectives on leveraging AI throughout software development and system migration.
For many Japanese enterprises, legacy systems have supported core business operations for decades. However, as customer expectations, business models, and market conditions continue to evolve, these systems are becoming increasingly difficult and costly to maintain. As a result, system migration is no longer simply a technical upgrade. It has become a strategic requirement for enterprises seeking to enhance operational agility, improve performance, and establish a stronger digital foundation for future growth.
Nevertheless, migration projects remain highly complex. Enterprises must carefully assess existing systems, identify critical business logic, convert large volumes of source code, rebuild system components, conduct extensive testing, and maintain operational continuity throughout the transition.
Introducing AI into this process can significantly improve implementation efficiency. However, adopting AI without a clearly defined strategy may introduce new risks related to output accuracy, system quality, security, and cost. Businesses therefore need an appropriate implementation framework that combines AI capabilities with experienced human oversight and comprehensive quality-control mechanisms.
During the seminar, NTQ Japan’s experts highlighted the significant value AI can bring to system migration. By rapidly analyzing large volumes of source code, design documents, and system logs, AI can help businesses better understand complex legacy environments, identify dependencies, automate repetitive conversion tasks, generate test cases, and detect potential defects. These capabilities can substantially reduce manual workloads, shorten implementation timelines, and allow engineering teams to focus on higher-value responsibilities.
However, the speakers emphasized that AI adoption must be built on close collaboration between technology and human expertise. While AI can improve the speed and efficiency of execution, people must remain responsible for understanding business intent, validating outputs, assessing risks, and making final decisions. In other words, AI should accelerate the process, while human oversight ensures that the results remain accurate, reliable, and aligned with actual business requirements.
Practical examples shared during the seminar illustrated why such oversight is essential. Even when given the same input, an AI model may produce different outputs each time, making it difficult to establish a consistent quality baseline. AI may also “helpfully” add business logic that does not exist in the source code, potentially introducing unnecessary functions or altering system behavior. The speakers assessed these scenarios according to how difficult they are to detect, demonstrating that some errors may appear technically valid and pass compilation while remaining inconsistent with the original business logic.
These examples reflect a common challenge faced by enterprises adopting AI without sufficient control mechanisms: outputs that appear convincing may not necessarily be correct. Businesses should therefore avoid treating AI-generated results as final deliverables. Instead, they need predefined validation criteria, automated comparison mechanisms, and clear approval responsibilities to ensure that only verified outputs can proceed to subsequent stages.
Drawing on these lessons, NTQ Japan recommends designing AI-powered migration around a structured quality-control framework in which AI handles high-volume analysis and generation, while experienced specialists retain responsibility for validation and decision-making. This principle underpins Smart Rewrite, NTQ Japan’s AI-integrated approach that combines automation with Quality Gates and comparative testing between existing and modernized systems, helping enterprises improve migration efficiency without compromising business continuity or software quality.
Alongside its in-depth technology sessions, the seminar also provided a vibrant space for cultural exchange. Guests had the opportunity to experience elements of Vietnamese culture through performances and interactive activities.
These moments contributed to an open and engaging atmosphere in which participants could connect beyond business discussions. They also reflected the close collaboration between NTQ’s teams in Japan and Vietnam—combining local market understanding with a strong international technology delivery foundation.
The seminar provided a focused platform for NTQ Japan to share practical perspectives on how AI can be applied across the migration lifecycle: From system assessment and source code conversion to testing, quality control, and post-implementation optimization. More importantly, the discussions highlighted that successful AI-powered migration requires not only advanced tools, but also a clear implementation strategy, strong engineering expertise, and continuous human validation.
Through the event, NTQ Japan demonstrated its ability to transform hands-on delivery experience into practical modernization solutions for Japanese enterprises. By combining AI capabilities, deep migration expertise, and a strong delivery foundation across Vietnam and international markets, NTQ Japan continues to support clients in accelerating system transformation, improving operational efficiency, and building more scalable and sustainable technology platforms.
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