Generated AI settings reduce enterprise data migration costs and usher in a new era of IT modernization
Generative AI provides a proven method to solve database migrations. As AI is further integrated into core operations, enterprises with modern data infrastructure will lead. The risk of delaying modernization is left behind, trapped in traditional TSHinking and systems.
Generated AI is revolutionizing enterprises, especially in database migration. New analysis and industry reports show that AI technologies, especially large language models (LLMS), can greatly reduce migration costs and timelines. Some reports show that potential savings are as high as 70%, while narrowing the project schedule from more than a year to several months.
This is a key breakthrough for global businesses that are burdened by outdated infrastructure: the path to greater agility and innovation.
Bottlenecks in digital conversion
Database migration is complex, risky and expensive. Old databases (usually decades old) are deeply integrated into core business functions. Moving them to a modern platform requires a lot of manual effort, a lot of budget and meticulous planning. Maintaining these traditional systems can consume most of the IT budget, sometimes up to 70%.
Generative AI changes this by automating most of the technology’s conversion workload. Some AI-driven solutions demonstrate that tasks such as pattern conversion and overall migration processes are automated up to 70-90%.
“The past only has more than a year of projects completed in just a few months,” said Prasad Sundaramoorthy, a technician and leading expert in AI-driven data modernization. “In some cases, this acceleration effectively reduces the timeline by 10 times, which is unprecedented in enterprises.”
How to automatically migrate AI
Training in extensive datasets, LLMS understands programming syntax, pattern design, and logic, enabling them to automate critical migration steps:
- Pattern translation and mapping: AI quickly analyzes and translates database schemas, and draws data types and constraints across platforms. This process is tedious and manual error-prone.
- Code refactoring: A major challenge is refactoring complex stored programs, triggers, and the functionality of legacy systems. Generate AI analytics logic and automatically rewrite the code, which greatly reduces manual work.
- Test case generation: AI generates relevant test cases and verification scripts based on transformed code and data. This speeds up testing and helps ensure data integrity.
This automation reduces manual labor, speeds up the process, and minimizes human error. Reports show that AI-driven automation can reduce manual efforts by more than 60%.
“These models are like expert translations of databases, understand old codes and recreate their purpose,” explains Harshinigadam, a veteran of the Enterprise AI platform.
The broader meaning of cross-industry
AI-driven database migration affects sectors that rely on complex data infrastructure: financial services, healthcare and the public sector all benefit significantly. For industries with expensive downtime and high complexity, AI-powered migration offers a faster and safer path. It also facilitates the adoption of cost-effective open source databases by simplifying transformations.
Retrieve IT budgets to innovate
Financial benefits are considerable. By cutting immigration time and resources extensively, organizations can free up a large amount of IT budgets and people. These resources can be redirected to strategic plans, such as developing new products or enhancing customer experience. Industry analysis shows that organizations using AI automation can significantly improve operational efficiency.
“Enterprises no longer need to choose between modern core systems and investment innovation – AI allows them to have both,” said Mahesh Kumar Goyal, a long-time consultant for the enterprise IT team in the data field.
Come on for the AI-driven future
With the enterprise’s “AI-Ready”, this technological transformation is crucial. Modern scalable data infrastructure is the foundation that leverages advanced AI applications. Old databases are often not available for high-throughput, real-time data processing required for AI. AI-led migration helps businesses quickly transition to cloud-native, scalable databases. Global AI spending is expected to reach $337 billion in 2025 and may double to $749 billion by 2028, highlighting the need for the modern data base adopted by modern AI.
Migrate from database to end-to-end IT conversion
Experts believe that AI's functions in understanding and transforming structured data systems only start with database migration.
“The newer LLM model enhances the ability to understand structured data systems, and there is no limit to this,” Sundaramoorthy said. “This technology can be extended to application modernization, API integration, and even the entire business workflow. We are talking about real enterprise translation engines.”
This vision is explored through programs such as the AI-powered enterprise transformation initiative, which shows that complex migrations between major database platforms are successful.
Industry experts verify: Strengthen discovery
Independent experts have verified the importance of discovery. “This experiment represents a potential turning point in enterprise data management,” said Dr. Ashish Khanna, a world doctoral and top researcher. “The ability to quickly migrate between database platforms without large-scale engineering projects without large-scale engineering projects may change the organization's data architecture decisions.”
Research behind the breakthrough
These insights are supported by peer-reviewed academic research. The paper, “Using Generative AI for Database Migration: An Integrated Migration Method”, was written by Mahesh Kumar Goyal, Prasad Sundaramoorthy, and Harshinigadam, published in the Journal of Computational Analysis and Applications (Volume 33, Issue 8, Issue 8). This study highlights the transformative potential of LLMS in undermining traditional data migration methods.
Complete research available: Eudoxus Press – Journal of Computational Analysis and Application
What's next?
For CIO and the person in charge of transformation, the message is obvious: Generative AI provides a verification method to resolve database migration. As AI is further integrated into core operations, enterprises with modern data infrastructure will lead. The risk of delaying modernization is left behind, trapped in traditional TSHinking and systems.
