Siili Solutions Enhances Legacy Modernization with AI Analysis

Siili Solutions Plc has introduced an AI-assisted model for modernizing legacy software systems. The company claims that AI analysis of the current state is up to 40–60% faster than traditional manual methods, condensing potentially months of code reverse-engineering into a shorter process. This approach aims to provide a structured view of existing systems, including business rules, dependencies, and risk areas.

Marko Jaanu, Head of Technology at Siili Solutions, emphasizes that AI-driven code analysis, while valuable, is only one part of the modernization effort. He cautions against treating AI output as a blueprint for new systems, as this can lead to inheriting old assumptions and limitations. True modernization, according to Jaanu, requires human decision-making and accountability for the final outcome.

The company advocates for an AI-native modernization approach where AI analyzes and extracts information, but humans retain control over design decisions and the overall strategy. This involves breaking down modernization into small, reviewable, and testable steps to ensure dependability and prevent errors from compounding. The focus is on human checkpoints between AI stages to maintain intent and accountability.

Siili Solutions notes a shift in team structures, with smaller core teams working alongside AI agents. The roles are redefined: AI agents draft and generate, while humans decide, direct, and remain accountable. This mirrors findings from Google Cloud's DORA report, which suggests AI acts as an amplifier, magnifying existing organizational strengths or weaknesses.