Accelerating Mainframe Modernization with AI: Key Insights from AWS Transform
Unpacking the Dual Aspects of Modernization
The Importance of Comprehensive Context in Mainframe Projects
Understanding Platform-Specific Behaviors
Ensuring a Traceable Foundation for Compliance
Setting AI Up for Success with AWS Transform
The Need for an End-to-End Solution in Enterprise Transformation
Strategies for Successful AI Integration in COBOL Modernization
Empowering Enterprise Clients: Success Stories and Results
Meet the Author: Dr. Asa Kalavade, Leading AWS Transform
Navigating the Future: AI-Driven Mainframe Application Modernization
In today’s fast-paced technological landscape, artificial intelligence (AI) is stirring excitement across industries, particularly in the realm of mainframe application modernization. As businesses seek to innovate, boards are pressing CIOs for actionable plans that leverage AI to streamline and modernize legacy systems. The promise of AI as an accelerator for COBOL modernization is undeniable, but to truly harness its potential, we must look beyond the source code.
Two Halves of Modernization
Having worked with over 400 enterprise customers, we’ve noted that mainframe modernization can be divided into two distinct halves:
- Reverse Engineering: Understanding existing systems and their functionalities.
- Forward Engineering: Building the new applications based on that understanding.
The first half is where projects are either launched successfully or doomed to stall. While AI coding assistants excel at forward engineering—given the right specifications and context—they struggle with the complexities inherent in legacy systems.
What Does Successful Modernization Demand?
1. Bounded, Complete Context
Mainframe applications can be labyrinthine. A single COBOL program might comprise tens of thousands of lines, accessing shared data definitions, interacting with various subroutines, and utilizing JCL to orchestrate processes. AI’s current capabilities are limited to analyzing small chunks of code, making it difficult to recognize broader dependencies.
To tackle this, we ensure all implicit dependencies are extracted first. By providing AI with complete, bounded units of code—free of extraneous information—AI can effectively focus on what it does best: understanding business logic and generating precise specifications.
2. Platform-Aware Context
Lesser-known to many is that COBOL source code behaves differently depending on its compiler and runtime environment. Details like memory management and data handling are outside the source code but crucial for accurate function. Many enterprises make the mistake of moving raw code without resolving these nuances.
Our experience shows that providing AI with clean, platform-aware inputs leads to more accurate results. Feeding AI raw source code without considering the environment it was built for often results in outputs that are technically correct yet functionally flawed, especially in critical sectors such as finance.
3. A Traceable Foundation
For companies in regulated industries like banking and insurance, traceability is non-negotiable. Regulators demand full accountability, requiring that AI outputs must demonstrate a clear and auditable connection to the original systems of record.
Simply relying on AI to analyze source code doesn’t meet these requirements. Instead, structural clarity is key; creating precise, bounded units of code allows us to trace each output back to its source. This level of traceability can be the difference between a project progressing or stalling under regulatory scrutiny.
Setting AI Up for Success with AWS Transform
Enter AWS Transform, a platform engineered to modernize mainframe applications at scale. Our approach ensures that AI operates on a solid foundation. We create a complete, deterministic model of the application that takes into account the interconnectedness of all components—eliminating the guesswork for AI.
- Specialized Agents: These agents extract complete code structure, runtime behavior, and intrarelationships across the entire system.
- Dependency Graphs: They create accurate dependency graphs aligned with the actual compiler semantics, validating behavior across programs and middleware.
- Decomposing Large Programs: Applications are broken down into manageable units, resolving platform-specific behaviors so AI can process them effectively.
Once the groundwork is laid, AI can extract business logic in natural language and validate outputs against the deterministic evidence gathered earlier. This guarantees that every specification correlates back to the original source.
Beyond Individual Applications: An End-to-End Platform
Modernization isn’t a one-off project. Businesses face vast portfolios of interconnected applications, each requiring a tailored modernization approach. AWS Transform automates the entire lifecycle—from analysis and test planning to refactoring and reimagination.
Our key takeaway is the need for a diversified modernization strategy. Different applications deserve different paths: some may need complete re-imagining, while others may just require straightforward conversion or migration. Our platform is built to accommodate these varying needs.
Conclusion: Success Awaits
The question isn’t whether organizations should adopt AI for COBOL modernization; it’s about how to effectively set up AI to achieve tangible results. Our experience with AWS Transform has taught us that deterministic analysis forms a solid foundation for AI acceleration, producing traceable, compliant results.
The successes we’ve witnessed are inspiring:
- BMW Group reduced testing time by 75% while boosting test coverage by 60%.
- Fiserv completed a project that would take over 29 months in just 17 months.
- Itau cut discovery and testing time by more than 90%, enabling a 75% faster modernization process.
As we delve deeper into an AI-fueled future, the possibilities for mainframe modernization are indeed limitless. With the right approach, organizations can not only adapt but thrive amid evolving technological advancements.
About the Authors
Dr. Asa Kalavade is the lead for AWS Transform, guiding enterprises through the complexities of modernizing their infrastructure and applications. Holding a PhD in electrical engineering and computer science from UC Berkeley, Asa has accumulated over 40 patents and a rich history of venture-backed startups.
AI is not just a tool; it’s an opportunity for reinvention. How will you leverage it for your modernization journey?