AI in legacy modernization: transforming the past into a strategic asset

The modernization of legacy systems is one of the most persistent and complex challenges in global corporations. Companies often find themselves stuck with software that has old code, obsolete technologies, and a complete lack of documentation, creating a critical barrier to innovation and scalability.

It is estimated that 70% of the systems in operation at Fortune 500 companies are legacy systems over 20 years old, and that 80% of the IT budget is consumed just to keep them running.

Generative artificial intelligence is changing the equation in legacy modernization, enabling companies to transform this operational and financial bottleneck into a foundation for strategic innovation.

What defines a system as legacy?

Legacy systems are, by definition, solutions that no longer keep up with current business demands, but still support critical operations. It is not necessarily a concept connected only to the time of existence, but rather to the inability to adapt to the inevitable changes in the corporate landscape.

The main symptoms of a legacy system include:

• Accumulated complexity: years of technical "patches" without a clear architectural vision, with unstructured data models and business rules misaligned with the company's current demands;

• Lost knowledge: the departure of technical personnel who held domain knowledge of business rules, often undocumented;

• Obsolete technologies: software running on old languages, not natively integrating with modern cloud and AI ecosystems, and not keeping up with business demands;

• Barrier to Innovation: difficulty in scaling new functionalities due to the fragility and architecture of old code;

• User-unfocused usability: the interface does not consider modern design thinking approaches, focusing on the user and attention to diverse needs, making the legacy system difficult to handle.

For a company seeking to innovate and grow, having an efficient, scalable, adaptable, and secure system is paramount, and legacy systems end up limiting this evolution.

AI as an accelerator for legacy modernization

Artificial intelligence has proven to be a game-changer when it comes to legacy systems. Here at SQUADRA, modernization has always been driven, for over three decades, by traditional approaches to digital transformation, solution architecture, and strategy, a process that has yielded several success stories. Now, with Genius, our multidisciplinary AI-powered platform, this modernization has become much more efficient and secure.

One of Genius's purposes is precisely to transform legacy systems into strategic assets for the business. According to Rômulo Cioffi, Chief AI and Innovation Officer at SQUADRA:

"AI has allowed us to modernize much more safely, quickly, and economically. Genius is like a co-pilot that understands the legacy system and designs a new future from it."

To transform legacy into a foundation for the new, Genius uses intelligent agents that understand complex code, dependencies, and contexts to design a scalable digital future. Unlike approaches that position AI as an end in itself, Genius uses it as an enabler to achieve productivity, security, and scalability results.

The application of AI through Genius covers the entire modernization process pipeline, generating tangible gains compared to traditional approaches.

In addition to bringing hyper-productivity gains and delivering a system ready for use in much less time, artificial intelligence is capable of learning from the context of business rules, which drastically reduces the dependency and time dedicated by the company's business areas.

Jack, the agent behind legacy modernization by Genius

One of Genius's pillars is the specialized agent Jack, a turing bot created by Squadra, which acts as an AI orchestrator to scale and speed up code generation. Jack is not just a code generator; it operates on a robust architecture based on three pillars:

• Foundation (LLM): utilizes generative linguistic capabilities of cutting-edge models (such as OpenAI, Anthropic, Meta, and Google) and trained data to understand code syntax and business rules;

• Core (RAG, Retrieval-Augmented Generation): a living, long-term knowledge base that ensures responses are based on real company facts and documents, not guesswork;

• Operation (agent network): acts as a team of virtual specialists (developer, architect, and technical lead) to ensure that every AI decision is auditable and accurate.

Jack is the tireless executor that analyzes, automates, and validates the transformation of the plan into functional code, but the entire modernization journey is mediated by people, in addition to being secure, auditable, and highly accurate.

• Secure because only the data necessary to interpret business rules and code is shared;

• Auditable because it is possible to track and review every AI decision;

• Highly accurate because responses are based on real company facts and documents.

Case Study: Modernizing National Rail Logistics

One of Brazil's largest logistics and railway operators had a mission-critical system for optimizing high-traffic rail and maritime transport that was no longer keeping up with business demands. The challenge was:

• Over 300,000 lines of C++ code;

• Lack of supporting documentation;

• System over a decade old with no technical personnel with deep knowledge of the legacy available.

Legacy modernization became imperative to eliminate risks and build a new, solid technological foundation for scalability. Starting with technological modernization, which maintains the system's functional characteristics, the execution was divided into four main stages:

• Discovery: use of LLMs for static analysis and clustering by domain to identify complexity points and extract hidden business rules from the 300,000 lines of C++ code;

• Design and modernization pipeline: creation of a new modern architecture, with the system divided into modules for gradual migration;

• Build: Jack assisted the development team in the secure conversion of the C++ legacy to modern technologies such as C# and React;

• Comparative QA: identical scenarios were configured in both systems, legacy and modernized, to compare train schedules, routes, and compositions side-by-side, with any divergence being classified and corrected iteratively until the diagrams converged perfectly;

• Deploy: each module is being implemented in a hybrid operation with the legacy system, allowing for gradual migration as we advance in the system's overall functional evolution.

The legacy modernization process through Genius allowed the operator to build a solid and scalable technological foundation, eliminating risks and preparing the business for continuous functional evolution starting in 2026. Furthermore, the use of Genius unlocked four fundamental benefits:

• Hyper-productivity and operational efficiency: automation of repetitive tasks and intelligent use of resources, drastically reducing the time dedicated by business areas and the time to implement the modernized system;

• Greater decision accuracy: improved precision in processes and traceability of AI decisions enhance the final software quality;

• Innovation and competitive advantage: transformation of a legacy system into a strategic asset, generating new business capabilities;

• Scalable digital transformation: real technological integration with business processes, allowing for rapid market adaptation.

Legacy modernization with AI is not just about modernizing; it's about regaining the ability to innovate. With Genius, what was once a bottleneck becomes the foundation for sustainable growth, quickly, securely, and scalably.

If your company still operates systems that hinder its growth, it's time to ask yourself: what is the cost of not modernizing? Is your company ready to transform the past into future value? Discover Genius or talk to one of our specialists.