In the article, Enden highlights how AI can help organizations gain visibility into complex legacy systems, making modernization faster, less risky, and more realistic than before.
With our AI Legacy approach, organizations can gain visibility into old and poorly documented systems that were previously difficult to understand. By analyzing millions of lines of code, AI can uncover system behavior, business logic, and dependencies, providing a reliable foundation for modernization decisions.
We have developed a model for modernizing legacy systems that uses AI to analyze and rebuild old, complex, and often poorly documented systems. Its first phase, AI Legacy Archaeology, helps organizations understand what they have today before deciding what to transform tomorrow.
In many large enterprises and public sector organizations, business-critical systems hold millions of lines of code, thousands of database tables, and decades of business logic built on technologies from different eras. Traditional methods haven't been enough to reveal the real structure and business logic of these large, decades-old systems, making modernization uncertain and risky. AI changes that. It can read and parse entire software systems, making business logic, dependencies, integrations, and workflows visible far faster than before.
"'The biggest barrier to modernizing legacy systems usually isn't technology: it's a lack of visibility and knowledge. Organizations no longer know exactly how their systems, built over decades, actually work, or what changing them would mean for the business. AI helps make that visible far faster than before. At the same time, it creates a foundation for modernization that can happen in a more controlled way and with less risk than before. Many of our customers have told us they've never had as clear a picture of their business processes as they did after this phase", says Enden.
AI Legacy Archaeology is the first phase in our multi-phase modernization model. In this phase, AI analyzes the system's code, data structures, configurations, and other technical material, then builds a structural picture of how the system works and the business requirements and processes it supports. With that understanding, the organization can move toward modernization in a controlled way.
After the archaeology phase, the organization can move on to setting the target state, requirement definition and prototyping, AI-assisted implementation, and quality assurance. AI automates much of the analysis and development work, but human experts still own decision-making, target architecture, quality, security, and risk management. AI doesn't replace decision-making. It gives decisions a stronger foundation, faster and more efficiently than before.
“'AI has been used in software development for several years already, but over the past year, that work has moved into a new phase. Instead of individual AI assistants, we can now use multiple parallel agents that analyze systems, produce documentation, create specifications, build software, and run tests. This isn't experimental "vibe coding," but a controlled, enterprise-level operating model,' says Enden.
According to us, agentic AI is moving digitalization into a new phase, one where fast-paced modernization of long-untouched legacy systems becomes a realistic option for the business. For many organizations, this is the first chance to modernize business-critical systems in step with changes in their business and operating environment.
Read the article in Finnish in Tivi.
