The primary obstacle to visualizing legacy assets is the loss of design information. As applications age, documentation gradually disappears and update histories become difficult to trace. Recent advances in AI-based visualization technologies have made it possible to structure code and automatically generate documentation to some extent. However, they cannot recover the original intent or business context behind a system. These abstractions are rarely written into the code itself. While AI-generated explanations can help organize structural information, their ability to verify validity or evaluate design intent remains limited.

For multinational corporations, the challenge is even greater. Unlike legacy systems operating within a single country, global operations involve diverse transactions across different regulatory environments, cultures, and business practices. The same business activity may be executed differently in each region, requiring processes to move across multiple systems. As a result, a single workflow often involves external data integration, approvals, and verification steps. Once these relationships become multilayered, the number of branching business processes increases rapidly.

A lack of visibility at overseas sites is also caused by localization and insufficient integration between regional systems. Even systems with standardized specifications must adapt to local business practices and regulatory requirements. Over time, these adaptations accumulate as unique system modifications.

For example, the EU requires an advanced tax rate management system to support complex consumption tax calculations. In South America, tax incentives based on specific regional development policies further complicate import and export transactions. These localized requirements often result in customized features that differ significantly from the original system design.

In many cases, detailed records and design documents remain within regional organizations and are not fully shared with headquarters. As a result, systems gradually become black boxes. As localized modifications continue to accumulate, regional specifications become increasingly difficult to understand, creating a significant barrier to standardization when organizations attempt to modernize their systems.

The challenge becomes even greater when organizations with different corporate cultures merge. Levels of system standardization, documentation practices, and business process organization often vary widely between organizations. During mergers and site consolidations, where both parties’ histories and operational practices must be respected, simply imposing a single set of rules is rarely feasible.

Over time, repeated localization, mergers, and organizational restructuring make it increasingly difficult to understand the overall system architecture. The result is a growing gap between how systems actually operate and how well they are understood.

AI tools supporting legacy analysis and visualization have made great strides in recent years. For example, they are now capable of understanding the structure of large-scale mainframes, automatically analyzing them, and generating design-level documentation. New platforms can also analyze, convert, and restructure assets written in legacy programming languages. These tools enable code conversion from legacy languages to languages like Java, as well as data migration, automated migration design creation, and even automated test generation.

At a structural level, AI can identify dependencies, execution order, and data flows embedded in source code. This provides a foundational layer for visualizing system architecture. In many cases, these tools transform fragmented tacit knowledge and difficult-to-interpret legacy documentation into human-readable explanations and process flows. As a result, AI has significantly improved both the speed and the baseline quality of system documentation.

However, AI analysis is limited to what exists within the system itself. Many real-world business processes occur outside the system environment. Manual approvals, paper document stamping, Excel-based reconciliations, and operational steps introduced through local business practices rarely appear in event logs or source code. As a result, these activities remain invisible to automated analysis tools.

This limitation becomes particularly significant in cross-border operations. As discussed earlier, global businesses operate with diverse transactions and localized practices. Many peripheral systems and operational processes exist outside the core system environment. Because these processes leave no trace in the underlying code or system logs, AI tools cannot detect them. The reality that is not captured in the data becomes the very gap in system visualization.

What happens if organizations attempt to replace legacy systems with a new ERP platform without first fully understanding how the current system actually works? In many cases, the results fall short of expectations. Hidden dependencies and undocumented business requirements often surface later, disrupting both system migration and business operations.

For this reason, high-resolution documentation of existing business processes is essential in the early stages of modernization projects. Without a clear understanding of how work is actually performed, system renewal initiatives are unlikely to succeed.

The most important aspect of visualization and innovation is not which product to use, but how to motivate people and organizations, and how to integrate that with real-world work. AI-based automated analysis tools can accelerate underlying processes like structure understanding, impact analysis, automatic conversion, and test generation. However, AI-generated output should be understood as a starting point rather than a finished result. For this reason, the final stage of visualization still requires close interaction with operational teams and their accumulated knowledge.

AI is highly effective at analyzing the current structure of systems, but its ability to reconstruct upstream business processes remains limited. Even when AI analyzes both source code and business documentation, the output often varies depending on the quality and consistency of the available materials. Documentation standards differ widely across departments and regions, and the sheer diversity of document formats makes it difficult for AI alone to produce reliable results. Effective analysis therefore requires strong project management and a deep understanding of both business operations and system architecture to coordinate automated tools with human investigation.

Leaders of system renewal must assemble teams willing to undertake this demanding work. They must understand both the potential and the limitations of AI, interpret business context accurately, and persistently analyze processes that fall outside routine system documentation. Equally important is building an organization that maintains continuous dialogue with operational teams and works alongside them over the long term.

Legacy modernization is not a competition to produce elegant architectural diagrams. It is an effort to uncover the real work that exists between paper documents, spreadsheets, and source code, and to build a foundation that can transform this accumulated knowledge into new corporate value. Achieving this requires carefully reconstructing operational reality from both business and technical perspectives and assembling insights that are often absent from formal system data.