AI as a Risk Mitigator in Modernisation: From Legacy Burden to Future Agility

For large enterprises, modernisation is rarely a matter of if but when. Core systems, often dating back decades, remain the backbone of critical processes. Yet they are also brittle, undocumented, and resistant to change.

The cost of doing nothing is mounting: Gartner predicts that by 2028, infrastructure and operations leaders who adopt structured approaches to managing technical debt will reduce obsolete systems by 50%. At the same time, Forrester notes that firms embedding AI into their engineering pipelines are already accelerating delivery and innovation, clear evidence that waiting carries greater risk than moving forward.

From Black Box to Business Context

The “black box” problem remains one of the greatest barriers to legacy transformation. Systems accumulate undocumented rules and hidden dependencies over years, making even small changes hazardous. AI is shifting this dynamic. By using abstract syntax tree (AST) parsing, dependency mapping, and function point analysis, AI can surface what was previously opaque: business rules hidden deep in legacy code. These outputs feed into auto-generated documentation and visual dependency graphs, allowing architects to see which modules will be affected by changes — before any line of code is rewritten.

Importantly, this does not replace expertise; it augments it. Subject matter experts still validate business rules, but they are no longer required to comb through COBOL or PL/1 files line by line.

Case in Point: Compliance at Scale

A recent compliance change, undertaken using CI&T’s FLOW framework, demonstrates its impact. When Brazil’s Revenue Service announced a shift to a new alphanumeric taxpayer registration format, a client faced the daunting task of updating references across its systems. Traditionally, this would have required:

- Inventory and mapping: cataloguing thousands of files and their integrations;
- Manual code analysis: identifying each point where the identifier was used;
- Refactoring: replacing numeric logic with alphanumeric support
- Unit and integration testing: validating changes across databases, APIs, and user flows.

This would have taken weeks of manual effort by multiple teams.

Instead, an AI agent scanned 2,805 files, generated dependency maps, applied automated refactoring, and created unit tests — all in just under an hour. Human experts validated the outputs, ensuring continuity and compliance. The result: a high-risk update became routine, and the defect risk associated with manual intervention was dramatically reduced.

Talent, Bandwidth, and Innovation

Modernisation has historically monopolised the time of a company’s key stakeholders. AI is changing that equation. McKinsey’s State of AI 2025 report shows that two-thirds of executives are already redeploying top engineers or others from maintenance to innovation initiatives. With automated code analysis and test generation pipelines, SMEs can now contribute strategically — reviewing architecture decisions or validating outputs — instead of being buried in manual checks.

Towards Autonomous Modernisation

The next horizon is agentic AI — autonomous systems capable of chaining together tasks such as code interpretation, refactoring, regression testing, and even generating CI/CD pipeline scripts.

McKinsey's report identifies this as one of the fastest-growing enterprise AI trends. In modernisation, this signals a shift from reactive documentation to adaptive, self-updating systems, where AI continuously monitors for regulatory or dependency changes and proactively adjusts codebases.

Reducing Risk, Unlocking Agility

The business case for modernisation has moved beyond cost savings. It is now about resilience and agility — ensuring systems can adapt to regulatory changes, security threats, and shifting market conditions. AI lowers the risk profile not by removing complexity, but by making it transparent and automatable.

For technology leaders, the question is no longer whether AI belongs in the modernisation journey. The real risk lies in standing still while others move faster, safer, and with greater clarity.

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