In the vast digital infrastructure of the governments, a silent challenge persists: the enduring presence of elderly legacy IT applications.
Rooted in technologies of decades past—COBOL, Fortran, and monolithic architectures—these systems, while once revolutionary, now strain under the demands of a modern, interconnected world.
They are costly to maintain, vulnerable to cyber threats, and ill-equipped to deliver the seamless services citizens expect. Yet, within this challenge lies an extraordinary opportunity.
This situation is one of the primary challenges holding up the adoption of AI in Government. As this UK Public Accounts Committee report warns that too often Government data is of poor quality, and often locked away in out-of-date, or ‘legacy’, IT systems, making it difficult for AI to be deployed.
Ai Powered Modernization
Demonstrating the vast potential for AI is that it can be used to tackle this challenge for itself, transforming old legacy applications like COBOL into new technologies like Java.
As this Google / McKinsey presentation describes generative AI can be used to streamline app modernization by automating discovery and documentation of functionality, creating target-state code, and performing unit and functional testing.
AI, with its unparalleled capacity for analysis, automation, and innovation, stands poised to redefine the modernization of these aging systems, unlocking efficiency, security, and scalability for the public sector:
Automated Code Analysis and Refactoring:
AI can analyze legacy codebases (e.g., written in COBOL, Java, or other older languages) to identify patterns, dependencies, and inefficiencies. Detects redundant or outdated code that can be eliminated or rewritten. Suggests modern equivalents (e.g., converting monolithic code into microservices). Speeds up the refactoring process by automating repetitive tasks. Example: Tools like AI-powered code analyzers can map out a legacy system’s structure and recommend how to break it into modular components.
Legacy System Understanding:
AI can interpret poorly documented or undocumented legacy systems by analyzing code, logs, and runtime behavior. Extracts business logic and rules embedded in old applications. Reduces reliance on retiring experts who originally built the system. Example: Natural Language Processing (NLP) models can parse comments and variable names to infer functionality, while machine learning can predict system behavior.
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Migration to Cloud-Native Architectures:
AI can guide the transition from on-premises systems to cloud-native environments by recommending optimal architectures (e.g., containers, serverless). Identifies the best cloud services (AWS, Azure, Google Cloud) for specific workloads. Automates the conversion of monolithic apps into microservices or containerized deployments. Example: AI-driven tools can assess an app’s resource usage and suggest Kubernetes-based orchestration.
Testing and Quality Assurance:
What it does: AI can generate test cases, simulate user behavior, and detect bugs or vulnerabilities in modernized applications. Reduces manual testing efforts by predicting edge cases and failure points. Ensures compatibility between legacy and modern components. Example: AI-powered testing tools can mimic real-world usage patterns to validate a newly modernized app.
Real-World Impact
AI accelerates application modernization by reducing manual effort, enhancing decision-making, and ensuring the modernized systems are scalable, secure, and aligned with business goals. For instance, a company modernizing a 20-year-old banking system could use AI to refactor code, migrate data to a cloud platform, and integrate AI-driven fraud detection—all while minimizing downtime.
In summary, AI acts as a force multiplier in application modernization, making the process faster, smarter, and more cost-effective while enabling organizations to unlock the full potential of their software assets in a digital-first world.
