AI driven legacy modernization
Many of the most critical, complex applications still run on-premises, with modernization delayed by risk, cost, and complexity. This has been the reality for years.
Cloud adoption is a complex process that most large enterprises have started but not yet completed. Many of the most critical, complex applications still run on-premises, with modernization delayed by risk, cost, and complexity. This has been the reality for years.
Technology leaders have long struggled to migrate these legacy systems, knowing they hold excellent value that is difficult to unlock.
But things are changing. The rise of Generative AI is making it possible to tackle these challenges more effectively. We have entered an era of AI-led and AI-assisted modernization—where what once took years and carried substantial risk can now become a faster, value-focused transformation.
Four pillars of AI driven legacy modernization
Many cloud providers offer AI tools that help with moving systems to the cloud. These tools make it easier to solve common problems and speed up the modernization process.
Pillar 1. Impactful discovery
The first step in any modernization project is knowing what you already have. Many old systems are poorly documented, with important business rules buried in millions of lines of code written in languages like COBOL or PL/I.
Now, AI-powered tools can automatically scan and understand this code, find unused parts, and explain complex logic in plain language. This cuts the analysis time from years to months and gives a clear, data-based plan for the next steps.
Pillar 2. Fast track transformation to target technology stack
Manually rewriting old code takes a lot of time, can introduce errors, and may lose important business rules.
Many well-known gen AI models, trained on substantial amounts of code, can automatically convert old languages like COBOL into modern ones like Java/C#/Node/Python. This makes the migration faster, more accurate, and helps build a flexible, cloud-based system for the future organization – that includes highly scalable cloud native environments.
Pillar 3. Reduce regression time!
Testing is often the biggest slowdown in a modernization project. It is essential to make sure the new system works the same way as the old one before going live.
Instead of writing tests by hand, AI can now analyze both the old and new code to automatically create complete test sets. This checks all parts of the system, increases test coverage, saves time, and builds confidence in the result.
Pillar 4. Operational excellence
The job not done once the move to the cloud completed. The real goal is to run your systems more efficiently and at a lower cost. AI tools (AIOps) can help manage your cloud environment by automatically placing workloads, spotting issues early, and improving performance. This helps reduce costs (TCO) and keeps your systems running smoothly and reliably from the start.