Imagine a bank struggling to launch a new mobile banking feature. Their core systems, built on decades-old COBOL, are a black box. Every change requires weeks of manual effort, costing time and money. This isn’t just a technology problem; it’s a business bottleneck. AI-powered modernization offers a solution. By automating code analysis, extracting business rules, and accelerating code translation, AI can unlock the value trapped in legacy systems, enabling faster innovation and competitive advantage.
This chapter explores how AI can revolutionize your modernization efforts, transforming legacy systems from liabilities into strategic assets. We’ll examine three key areas where AI is making a significant impact: business rule extraction from legacy code, automated code translation from COBOL to modern languages, and the overall business benefits of AI-powered modernization. By understanding these capabilities, you’ll be better equipped to leverage AI in your organization’s modernization journey.
Agility and innovation are critical in today’s business environment. Many organizations leverage valuable COBOL systems containing decades of business logic and critical data. Modernizing these systems is essential to fully leverage their potential in today’s digital landscape. AI-powered modernization offers a solution, unlocking the value within these legacy systems. By automating processes, AI reduces costs, accelerates timelines, and enhances business agility, transforming liabilities into strategic assets.
AI empowers modernization in several key ways:
A key benefit of AI in modernization is its ability to automate code analysis, documentation, and testing, saving time and money. Traditionally, modifying COBOL code required extensive manual effort. AI tools now perform these tasks efficiently.
For example, an insurance company updating its policy system for new regulations could spend months and $500,000 analyzing millions of lines of COBOL code manually. An AI-powered tool can accomplish the same task potentially 50-70% faster and at a 30-50% lower cost, freeing up resources for strategic initiatives like new product development. As importantly, AI empowers existing IT staff to understand and modify COBOL, mitigating the reliance on scarce specialists and democratizing access to legacy systems.
For example, ING Bank reduced development time by 40% and cut costs by 30% by using AI-powered tools to automate code analysis and testing during its migration of 1.5 million lines of COBOL to Java. This also allowed them to redeploy valuable staff to new strategic projects.
What This Means for Your Business: AI automation translates to reduced operational expenses and faster time-to-market. It enables quicker responses to market changes and regulatory requirements, providing a competitive edge and freeing IT staff for innovation.
Machine learning – AI’s ability to automatically find hidden patterns in data – identifies anomalies in code, improving accuracy and efficiency. Legacy systems often contain undocumented code and vulnerabilities. Manually identifying these issues is time-consuming and prone to error. Machine learning algorithms analyze code to detect patterns and anomalies that humans might miss.
For instance, a bank might use machine learning to identify security flaws in its banking system, reducing the risk of breaches and building customer trust. This could identify critical security flaws missed by manual review.
For example, a major financial institution used machine learning to identify and remediate 50 critical security vulnerabilities in its COBOL-based core banking system, reducing the risk of data breaches and regulatory fines. This proactive approach saved an estimated $2 million in potential breach-related costs.
What This Means for Your Business: Machine learning enhances the reliability and security of legacy systems, reducing the risk of errors and breaches. It also provides valuable insights for informed decisions about modernization and maintenance.
Natural language processing (NLP) helps understand and translate COBOL, bridging the language barrier. A major challenge is the shortage of COBOL experts. NLP helps by automatically translating COBOL code into natural language, making it easier for modern developers to understand.
Imagine a software company acquiring a business with a critical COBOL system. The company’s developers, skilled in languages like Java, would struggle with COBOL. NLP can generate documentation in a language they understand, facilitating knowledge transfer and enabling them to contribute to modernization. This drastically reduces reliance on specialized COBOL knowledge.
For example, a retail bank used NLP to generate documentation for its COBOL-based loan origination system, enabling its Java developers to quickly understand and modify the system to support new loan products. This reduced the onboarding time for new developers by 50%.