From legacy to leverage: How AI and Platform Engineering are redefining modernisation

CIOs and architects do not need another “AI will fix legacy” promise. They need a safer, faster and more governable way to modernise at scale. Our approach to legacy modernisation is built on the fundamentals of effective software transformation: a clear business case, a sound architecture and a well-defined modernisation strategy come first — and AI accelerates.

AI assists with software decomposition, migration planning and development, while strengthening testing and quality assurance throughout the process. The result is lower risk, greater confidence and measurable outcomes that prepare organisations for long-term adaptability.

Why this matters now

• Risk and value are often seen as a trade-off. Test-first modernisation turns that around: it reduces risk in every increment while unlocking long-term value. Think “verify, then trust” — AI proposes, tests decide.
• The cost of delay is real. By starting with a narrow pilot and a living test suite, you can deliver value within weeks and expand iteratively without the risk of a big-bang rewrite.
• And because compliance is not optional, guardrails built into the platform — such as policy-as-code, dependency allow-lists, model usage policies and audit trails — ensure that every change remains compliant by default.

What AI actually does 

• Code intelligence: identifies dependencies, hotspots and cross-cutting concerns, and extracts business rules embedded in years of legacy code.
• Test synthesis: generates unit and characterisation tests that capture the system’s current expected behaviour, allowing humans to review and refine what’s retained.
• Guided transformation: suggests refactors, interface extractions and migration scaffolds — such as strangler-pattern entry points — prioritised by impact and risk.