For decades, the IBM i (also known as System I or iSeries) platform has been the powerhouse for many businesses, due to its robust reliability, stability, and security. However, in an era of digital transformation, what if you plan to modernize these platforms and how today’s buzz words AI or ML can help me in my journey. That is what exactly we will be talking about in our topic 'Legacy Application Modernization in the Era of AI/ML'. We will explore how we can inject new life into our IBM i systems. It's about making our old friend smarter, faster, and ready for the future, without losing the stability and reliability we have always counted on. Please join me as I walk you through exciting possibilities of blending the strength of IBM i with the dynamic capabilities of AI/ML.
By integrating AI/ML into IBM i environments, organizations can not only extend the life of their existing systems but also enhance efficiency, reduce manual effort, and gain valuable insights. The combination of AI/ML with RPG and CL-based applications can bridge the gap between legacy operations and modern, data-driven decision-making. I want to share specific examples, from automated code analysis to intelligent data insights, proving how AI/ML can make modernization more efficient, effective, and more valuable for our business.
Below are some AI/ML use cases for IBM i modernization:
- AI-Powered Code Analysis & Refactoring:
Legacy RPG and CLP applications can be modernized using AI-powered tools that analyze and refactor code into optimized, modular structures. For example, AI can scan RPG III or old cycle-based programs and suggest converting fixed-format code into modern free-format RPG for better readability and maintainability.
- AI-Driven Data Migration and Cleansing:
Migrating data from legacy IBM i systems to modern platforms can be complex due to inconsistent formats, redundant records, and missing values. AI-powered tools can automate data migration by identifying patterns, fixing inconsistencies, and mapping legacy data structures to modern databases efficiently. For example, An AI model can analyze DB2 for i data, detect duplicate records, fill in missing fields using prediction, and automatically map RPG-based file structures to a modern relational database, reducing manual effort and ensuring high data accuracy.
- Testing Automation:
After code rewrite, manually testing IBM i applications can be time-consuming and prone to human error. AI-driven test automation can streamline the testing process by automatically generating test cases, identifying defects, and executing regression tests with minimal manual intervention. For example, An AI-powered testing framework automatically generate test scripts for different RPG programs, execute the testcases to validate after code changes.
- AI-driven Data insights:
Many IBM i systems store vast amounts of transactional data in DB2 for i. AI can automate data extraction, transformation, and analysis to generate real-time business insights. For example, A machine learning model can categorize customer complaints stored in IBM i databases, helping businesses identify trends and address concerns faster.
- AI-Powered predictive maintenance:
Instead of relying on fixed schedules for system and hardware maintenance, AI-driven models can predict failures before they happen. By analyzing historical logs and system usage data, machine learning can flag potential issues, reducing downtime and optimizing resource allocation. For example, An AI model trained on historical server performance logs can proactively alert the IT team of a potential hardware failure.
To associate AI/ML with IBM i platforms is not about replacing legacy systems but to enhance them with smart, automated capabilities. In the upcoming posts, we will explore each of these use cases with practical steps and examples.