Many organisations still run their most important processes on applications written decades ago, in languages and on platforms that fewer and fewer people know. The systems work, which is exactly why nobody wants to touch them. But every change takes longer, the people who understand the code are moving on, and the platform holds back new digital services.
AI has changed the economics of modernisation. Tools that can read large codebases, explain them and draft converted code make work that used to be slow and manual much faster. They do not make modernisation risk-free, and anyone who promises a one-click conversion of a core system is overselling. The value comes from using AI where it is strong and keeping experienced people firmly in charge of the rest.
What AI can realistically do
Used well, AI speeds up the parts of a modernisation that traditionally consume the most expert time:
- Explain and document old code: AI can summarise what programs do, describe data flows and produce readable documentation for code that has had none for years.
- Map dependencies: it can trace call chains between programs, shared data structures and interfaces, giving you a picture of how the system hangs together before you change anything.
- Suggest conversions: it can draft equivalent code in a modern language or framework, which engineers then review, correct and improve.
- Generate tests: it can propose test cases based on the existing behaviour of the code, helping you build the safety net that most legacy systems lack.
The common thread is that AI produces a first draft quickly. That shifts expert effort from writing and reading line by line to reviewing, deciding and verifying.
What AI cannot do for you
Legacy systems carry years of business decisions encoded in their logic: special pricing for a long-standing customer group, a rounding rule required by an old agreement, a workaround for a process that no longer exists. AI can find and describe that logic, but it cannot tell you whether a rule is still needed, why it was introduced or what breaks if it changes. Only people who know the business can answer that.
AI-generated code can also look convincing and still be wrong, especially at the edges: date handling, numeric precision, character sets, batch timing and error paths. That is why verification is not optional. Treat every converted component as unproven until it has been tested against the behaviour of the system it replaces.
Choose the right route for each application
Code conversion is only one way to modernise, and often not the right one. Decide per application, based on business value, technical health, cost and risk. The main options are:
- Rehost: move the application to new infrastructure, such as the cloud, with little or no change. Fast and low-risk, but the code and its limitations stay the same.
- Replatform: make targeted changes to run on a modern platform or managed service, for example a new database or runtime, without redesigning the application.
- Refactor: restructure or rewrite the code, often into a modern language or architecture. This is where AI-assisted conversion helps most, and also where the effort and risk are highest.
- Replace: swap the application for a standard product or SaaS solution and migrate the data. Often sensible when the system does nothing that sets you apart.
- Retire: switch off what is no longer used or needed. Usually the cheapest option, and frequently overlooked.
A safe approach, step by step
The approach that works best is incremental, test-led and reversible. In practice it looks like this:
- Assess the portfolio: understand which applications matter, what they cost, how they connect and which route suits each one. Use AI to analyse the most critical code early, so decisions rest on facts rather than assumptions.
- Build the test safety net first: before converting anything, capture how the current system behaves with automated regression tests and recorded inputs and outputs. AI can help generate these, but people decide what good coverage looks like.
- Convert in slices: move functionality piece by piece behind stable interfaces, an approach often called the strangler fig pattern, rather than replacing everything in one big switch-over.
- Validate behaviour: run old and new side by side where possible and compare outputs, including the awkward edge cases, until differences are explained or fixed.
- Keep humans in charge: experienced engineers review all generated code, domain experts confirm business rules, and accountability for quality stays with the team, as it would for any other code.
Plan the data as carefully as the code, and plan the decommissioning too. The savings only arrive when the old platform, its licences and its contracts are actually switched off.
Keep code and data secure when using AI
Legacy code often contains sensitive information: business logic you consider a trade secret, embedded credentials, test data copied from production, or personal data covered by GDPR. Before any code goes near an AI tool, decide what may be shared, with which service and under what terms.
Check how the provider handles your inputs, whether they are stored or used for training, and where processing takes place. Strip secrets and personal data from code and test sets. For the most sensitive systems, consider a privately deployed AI model that runs inside your own infrastructure or a dedicated cloud environment you control, so code never leaves your boundaries. It can take more effort to set up, but it removes a whole category of risk and makes approval from security and legal teams easier.
How Altechy can help
Our Application Modernisation service usually begins with a Legacy Modernisation Assessment: a fixed-scope review of your legacy applications, with AI-assisted analysis of the most critical code, that ends with a recommended route for each system and a sequenced roadmap. Because verification is what makes AI-assisted conversion safe, we pair it with Quality Engineering & Testing to build the regression safety net before code changes. AI is an option, never a requirement, and it can run inside your own infrastructure if you prefer.
If you would like to explore where to start, book a free 60-minute idea session with us.