Jonathan Lalou's Blog

Lecturer

Cameron Jenkins acts as a Managing Director in the Mainframe Modernization group at Accenture, overseeing sales, marketing, and technology products with decades of experience in legacy system transformations. Shri Kai occupies a senior role at T. Rowe Price, serving as the executive sponsor for modernization initiatives, with prior successes at Experian and CoreLogic. Joel Rosenberger functions as the AWS Mainframe Modernization Lead and Chief Architect at Accenture, strengthening partnerships and architecting programs like Go Big for large-scale migrations.

Abstract

This in-depth analysis scrutinizes the strategic value of mainframe modernization in financial services, focusing on T. Rowe Price’s migration to Amazon Web Services facilitated by Accenture’s refactoring and generative artificial intelligence tools. It dissects the methodologies for automating legacy code analysis, generating artifacts, and enhancing decision-making, while considering contextual drivers like agility and cost savings. The article evaluates implications for business users, risk mitigation, and future patterns, advocating a hybrid approach combining deterministic tools with emerging AI capabilities.

Strategic Drivers and Organizational Support

Mainframe modernization in finance yields enhanced flexibility, superior client interactions, and reduced expenses. At T. Rowe Price, the decision to decommission the mainframe and relocate core applications stems from these benefits, supported by executive buy-in from the CEO, CTO, COO, and CDO. This high-level endorsement mitigates risks associated with legacy systems, aligning technology with business objectives.

The initiative transcends cost reduction, positioning technology as a competitive advantage. Historical projects lacking such support often faltered, emphasizing the need for strategic alignment. AWS was selected due to its leadership in cloud services and proximity advantages, facilitating seamless integration.

Methodological Approaches to Code Transformation

Accenture’s tools automate analysis of legacy languages like COBOL, Assembler, and PL/1, producing technical and business documentation. Generative AI augments this by creating artifacts valuable to IT architects and business stakeholders, fostering collaboration and informed decisions.

Patterns include refactoring for twelve applications, with some sunsetting pre-migration. Post-migration flexibility allows microservices development, end-of-life planning, or incremental enhancements, tailored to business needs.

Testing remains pivotal for confidence-building, with AI generating test suites to address outdated data, reducing risks.

Code sample for basic COBOL to Java refactoring simulation in Python:

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