Refactoring Legacy Systems with the Help of AI

Many organizations find themselves tethered to legacy systems—complex, fragile, and often poorly documented. The mere thought of modernizing them can be overwhelming, with traditional refactoring projects promising months, if not years, of high-risk, high-cost effort. But what if there was a way to navigate this complexity with greater speed and precision?

This is where generative AI integration can help. It automates code analysis, identifying dependencies and even suggesting optimized code. AI tools are now transforming a manual, error-prone process into a streamlined, intelligent one. 

This article will explore how AI is not just making modernization possible but also practical.

The Burden of Legacy Code and How it Affects Enterprises

Virtually every large organization has some form of legacy code, which is old software that has undergone years of patches, accumulated technical debt, and developed broken shortcuts. These aging systems are usually anchored deep within business processes, but as time passes, they become increasingly complicated to maintain. Adding one simple feature could become a new engineer's worst nightmare as they try to untangle a web of complex systems. These could take programmers five repositories deep to add a single validation to a decade-old code. This phenomenon has been deemed the legacy code trap. 

This is when, despite the possibility of an organization innovating and allocating the required resources, they still feel the need to support old code that "kept the lights on" during the past. Recent evidence suggests that as much as 60-80% of the IT budget is allocated to maintaining legacy systems, as opposed to new projects. 

Note: The prospect of maintaining legacy software is often referred to as keeping the lights on (KTLO). It becomes a significant issue for the organization, encompassing inflated maintenance costs, security risks, and a decline in developer productivity. Instead of pondering the possibility that legacy systems are silently reducing competitiveness, businesses' focus shifts to keeping them running.

Why Businesses Need to Refactor Their Legacy Systems

One of the primary drivers to update outdated software is that supporting old systems drains budgets. As per IBM, successful modernization can lower maintenance and operating costs by 30-50% or even more. For example, an IBM study found that modernizing apps led to a 74% decrease in hardware, software, and staffing costs. Cost savings are the ultimate goal for most developing companies, as funds are necessary for strategic business moves.

Another consideration that pushes many to refactor legacy systems is security. Older systems and platforms often contain unaddressed vulnerabilities and fail to comply with current rules and regulations. These systems often operate on outdated languages and frameworks, thereby putting the entire organization at a security risk. Also, legacy systems struggle with the newer compliance requirements (GDPR, SOC2, etc.). 

The most pressing issue, perhaps, is that having legacy systems in place will hold the business back from embracing new capabilities. Many outdated systems are pre-internet or pre-cloud and thus designed without easy integration with modern APIs, cloud services, or comprehensive analytics tools. Untangling legacy code and modularizing it to cloud-native infrastructure can unleash the cloud, AI, and real-time data services that were previously incompatible. 

What Is AI-Powered Legacy Modernization?

AI-powered legacy modernization focuses on the application of generative AI models, which assist or automate the process of reengineering legacy codebases. The process of refactoring large systems traditionally requires several months, if not years, of meticulous code analysis and rewriting, which, in turn, is time-consuming and complex. With generative AI systems, however, AI-powered tools rely on large language models. These models analyze complex systems and thousands of code files, deducing patterns in architecture and even producing code that supersedes legacy systems. 

In essence, LLMs are hyper-advanced virtual engineers that can analyze and reprogram the architecture of complex systems while preserving existing business rules.

Generative AI models are capable of working on legacy codebases while employing their advanced AI capabilities. For example, they can transform COBOL modules into Java. Also, they can locate all copies of obsolete encryption algorithms and replace them. Many modern AI advanced code refactoring tools use a context engine or a knowledge graph that provides AI models with a comprehensive insight into the system by giving context to code beyond just a string of characters. This means recognizing interdependencies between system parts, as well as data flows and known interaction patterns. 

Note: This context is particularly important when trying to replace a system, especially a legacy system. Understanding why certain parts of the code are implemented in a particular way is equally important as understanding what the code is intended to do.

Incremental Modernization of Existing Systems

Generative AI technology can change the traditional "lift-and-shift" or "rip-and-replace" strategies into something entirely new. It now allows for gradual evolution or incremental modernization of existing systems. This means AI now translates in sequence what previously required core refactoring steps. For example, an AI system can analyze the code and determine a collection of safe transformations. This transformation may include removing dead code, updating an outdated library, and then organizing the remaining code for software engineers to implement. 

AI-powered modernization tools with generative AI capture the essence of the work of a whole team of senior engineers. Boston Consulting Group (BCG) report states that their generative AI agents dissect legacy code (the "what" and "why") at breakneck speed, revealing the business logic and dependencies within old systems. This ability allows organizations to accelerate and de-risk the modernization journey. Once cumbersome tasks that required months of manual effort can be accomplished in a matter of days, all without prolonged downtimes. 

Most importantly, AI-powered refactoring facilitates legacy system modernization without compromising the value of business logic legacy code. Thereby, it provides enterprises with the best of both worlds: continuity of core logic and the advantages that the current technology stack can offer.

Critical Risks of Unsupervised Generative AI Modernization

While the potential for generative AI to assist in modernization is tremendous, a controlled approach to AI use is necessary, which also entails supervision. Let's discuss some of the most concerning risks AI carries.

Lack of Context Leading to Errors

One of the challenges of generative AI is that it needs adequate context pertaining to a system. Otherwise, it may suggest changes that, on the surface, would appear to work in silos, but at the same time, would break key functionality. For example, an AI code assistant might remove a piece of code that is perceived to be redundant when, in fact, it is a critical part of the system. This can happen when AI is not fed with the historical context as to why the code or part was built in the first place. With no oversight, AI can remove defensive patterns or intricate business logic, resulting in a setback that a human maintainer would, ideally, not allow to happen.

Security Vulnerabilities and Bugs

OpenAI's decision to restrict access to ChatGPT and similar platforms stems from immaturity and extreme perplexity of the technology. The manner in which AI systems are accessed over the web is a substantial threat and damaging to intellectual property and security. Models like ChatGPT are delving into sensitive areas without stern limitations. The element of hallucination is one of the major generative AI security risks, creating fictitious codes that inevitably make the generated program insecure and vulnerable. 

AI systems can exploit WCF vulnerabilities, introduce weak cryptography, and logic errors that compromise the original system's design. So you shouldn't rely on it without professional human assistance. Supervised testing of novel, autonomous changes is key.  

Compliance and IP Concerns

AI models raise concerns about data governance. Using generative AI indiscriminately, such as third-party APIs or cloud AI services, could unintentionally leak sensitive data and proprietary code. Consider the case where developers want AI to refactor a portion of internal source code and paste it into a public AI tool. That code could be retained in AI's logs or training data. It's a patent and compliance risk nightmare, especially in highly regulated domains. 

Furthermore, there is a danger in machine learning where models can generate code fragments resembling parts of their training dataset. These fragments may be open source code compliant with certain licenses. They could pose compliance and copyright concerns if ever integrated into your code. Thus, the unsupervised AI in your possession could be breaching license and compliance obligations of certain proprietary data.

Lack of Accountability or Explainability

AI can make sweeping changes to a codebase, all the while keeping the reasoning behind the changes to itself. This phenomenon has come to be known as the black box phenomenon. It is far more probable that AI would be the one to make changes to code that a developer is least likely to want to touch. This can bring development to a standstill, introduce critical bugs, maintainability disasters, and security breaches.

Building Transparency Into AI-Driven Legacy Modernization

When implemented correctly, AI-driven modernization can enhance transparency far more than manual methods. A generative AI tool can uncover hidden complexities and create a clear blueprint of a legacy system's architecture. In contrast, a human-led rewrite might overlook documenting these crucial insights. 

Experts from BCG have observed that the use of GenAI agents introduces "hyper-transparency" into their modernization projects, leading to a more robust design and delivery process compared to traditional approaches. The combination of AI's diligence and human supervision offers the best of both worlds: accelerated refactoring with complete visibility and accountability. Essentially, transparency mitigates the risks associated with AI. By embedding it from the outset, organizations can enjoy the efficiency gains of generative AI while maintaining full control and confidence in their software.

Establish Human-in-the-Loop Governance

Create a distinct governance structure that clearly outlines how AI will be utilized and who will be responsible for its oversight. A practical step is to assemble a technical steering committee or task force to supervise the AI-driven modernization initiatives. For instance, lead engineers and architects can determine which modules to prioritize and establish rules for the AI, such as coding standards and verification procedures. DevOps leaders can integrate AI recommendations into the CI/CD pipeline with appropriate checks, while security specialists can review AI-generated code for compliance.

By clearly defining roles, such as who approves AI-driven modifications, who has veto power, and so on, you can prevent the AI from becoming an uncontrolled process that makes unverified changes. In practice, this often means AI suggests changes, and humans approve them. The AI tool might generate refactoring proposals, but these must undergo the same code review and testing processes as human-written code. Keeping experienced developers involved for validation ensures that human expertise guides the AI, preserving code quality and consistency.

Ensure Explainable and Documented Changes

Demand full transparency in all AI operations. The AI platform must generate logs or reports detailing its actions, such as a "blast radius" analysis that shows which files and functions a proposed change will affect. Some sophisticated AI refactoring tools can automatically comment on pull requests with explanations of the changes or produce side-by-side comparisons that clarify each modification. Making the AI's reasoning accessible, even at a high level, helps the team develop trust in its suggestions.

Furthermore, producing comprehensive documentation as a result of AI analysis is incredibly valuable. AI platforms can sift through the documentation of a 15-year-old system's architecture and behavior during code analysis, making the entire modernization process "transparent and stress-free." This type of AI-generated documentation can help fill the knowledge gaps left by outdated manuals, providing a clearer understanding of the evolving codebase to everyone.

Implement Traceability and Version Control

Manage AI-driven modifications with the same level of traceability as any other code change. Every refactoring action performed by the AI should be committed to version control with clear messages (which the AI can help draft) and linked to a specific requirement or ticket. If an AI transforms thousands of lines of code, the changes should be broken down into logical commits that a human can easily review and understand. This creates a detailed audit trail, allowing you to answer what changed, who or what made the change, and why.

Some companies integrate AI outputs directly into their existing CI/CD pipelines. For example, when an AI submits a pull request to refactor code, automated tests and static analysis tools are run, and the team can discuss the changes just as they would with a human contributor. This not only builds confidence but also helps catch potential issues early. You can also add policy gates, requiring that AI-generated code meet certain test coverage standards or pass all linters before it can be merged. Applying the same DevOps discipline to AI contributions demystifies the process.

Foster a Culture of Education and Openness

Finally, cultivate a culture of transparency around AI within the development team. Encourage engineers to view the AI as a collaborator whose work needs to be understood, not as an infallible black box. This may involve training the team on how the AI tool functions, its limitations, and how to interpret its outputs.

Some modern AI platforms even allow developers to query the AI about the legacy code, asking questions like, "Why did you suggest this change?" or "Explain the function of this legacy module." For example, BCG's GenAI agents were designed to answer specific questions about the codebase, ensuring alignment with modernization goals and making the process more interactive and transparent. When developers can get clear, plain-language answers from the AI about code behavior, it bridges the gap between opaque AI decisions and human comprehension.

Why AI-Powered Modernization Is Better Than Traditional Methods

AI-assisted modernization of legacy systems is certainly better than doing it manually. The table below illustrates the difference between the two methods.

Table: Manual Modernization vs Modernization Through AI. AI has higher speed, lower cost, reduced risk, better code quality, higher agility, and more cost efficiency than doing it manually.
 

AI-driven business transformation significantly improves the results of legacy system refactoring. A financial organization that would need 18 months just to analyze a complex legacy landscape could do it within a few weeks with an AI context engine. AI permits a smoother transformation, rather than large rewrites that typically halt significant business progress.

AI and Large-Scale Automation for Strategic Business Impact

Aside from tactical enhancements, AI legacy refactoring can provide strategic benefits to the business. McKinsey found that companies using GenAI agents for IT modernization notice that the process has become faster and teams are more productive.

Other benefits include:

• Swift time-to-market: Companies can leverage modernization with AI to enhance legacy code for new features, resulting in faster deployments. Automation results in a drastic reduction of months to weeks for complex development and testing cycles, resulting in timely business responses to market demands.
• Significant cost savings at scale: AI-assisted modernization automates tasks that would otherwise require a substantial amount of time and effort. These savings can be spent on other strategic goals of the company development process, like creating new digital products, targeting new regions, or aiming to increase customer satisfaction. 
• Improved system performance and reliability: AI increases performance by predicting system and component failures. Better performance means lower maintenance expenses. Meanwhile, increased reliability and performance reduce customer frustration and outage perception, leading to a stronger brand image.
• Enterprise-wide digital transformation: Perhaps the biggest opportunity brought about by AI is the ability to undergo deep and wide transformations. Most enterprises have a lingering modernization backlog that is so overwhelming that it gets postponed year after year. Companies that utilize AI for business process automation can transform their platforms from being reactive, crisis-initiated upgrades to proactive, foundational layers for rapid innovation. Thus, swift, wholesale transformation becomes attainable.
• Aligning IT with business innovations: Organizations can advance their human capital to more innovative tasks by automating the mundane work of migrating code. Engineers and architects can focus on creating new value by developing new differentiating features, enhancing the user experience, or integrating and experimenting with modernized systems' AI and advanced analytics. 
   

Aligning Tech and Business Goals in Modernization

For any modernization project to succeed, especially those enhanced by AI, it's crucial to align technology initiatives with business objectives. Refactoring legacy systems shouldn't be an isolated technical task; it must be directly linked to the organization's AI opportunity discovery process, visualization of strategic goals, and the generation of tangible business value.

Here's a guide for businesses to maintain this tech-business alignment during an AI-driven modernization process.

Establish Clear Business Outcomes

Before writing a single line of code, leadership must define what the modernization aims to achieve from a business perspective. Are you looking to lower operational expenses, enhance customer satisfaction with quicker service, or create new revenue channels through digital offerings?

By establishing specific targets, you create a clear direction for the project. For instance, if the primary goal is to release features more rapidly, you might prioritize modernizing systems that are creating bottlenecks in the development cycle. As experts from IBM suggest, effective application modernization should boost efficiency, performance, and create new ways to serve customers. In fact, a well-executed modernization can increase annual revenue by as much as 14% through the introduction of new functionalities. Keeping these goals front and center ensures that everyone understands the business relevance of the initiative.

Prioritize Based on Business Value

Not all legacy systems hold the same importance. Adopting a portfolio management approach to modernization allows you to address the systems that carry the greatest risk or present the largest opportunity first. An outdated billing system causing frequent revenue leakage or compliance problems, for example, would be a top priority. Conversely, a legacy application with minimal usage might be scheduled for modernization later or even be retired completely.

Prioritizing by impact helps deliver early victories that demonstrate the project's worth. This strategy is also effective for gaining stakeholder support, as they can see direct improvements to major pain points. 

Implement Incremental Delivery and Feedback Loops

Aligning with business goals requires continuous validation that the modernization is meeting them. Instead of a "big-bang" deployment, an agile and incremental approach (often made more efficient by AI) is far more effective. Changes should be delivered in iterative cycles, such as modernizing one service or module at a time, followed by collecting feedback from business users or product owners.

This method helps confirm that the refactored system is fulfilling its intended business purpose, like actually making a workflow faster. If a particular change isn't delivering the expected results, you can make corrections early in the process. Integrating feedback from business stakeholders into each cycle ensures the modernization stays on track to produce the desired value.

Measure and Report on Value

To track the progress of modernization, develop metrics that are meaningful to business stakeholders. Technical metrics like code coverage or the number of refactored modules are important for the development team. For enterprises as a whole, it's essential to translate these into business-focused metrics. Examples include reductions in IT maintenance costs, decreases in system downtime, faster time-to-market for new features, and improved customer Net Promoter Scores (NPS).

A practical approach is to establish a baseline for these metrics before the modernization begins and then measure them periodically after each significant refactoring phase. It's crucial to communicate these results broadly. If AI-assisted modernization leads to a 30% reduction in critical incidents or shortens a process from five days to one, share this success across the organization. This not only justifies the investment but also reinforces the connection between technical efforts and business improvements. 

Foster Cross-Functional Collaboration

True alignment between technology and business requires breaking down organizational silos. It's vital to involve business domain experts and end-users directly with development teams. AI-driven refactoring can decipher much of the logic within legacy code. Meanwhile, the business experts can validate whether that logic remains relevant or if certain old features can be discarded.

Regular meetings between IT and business teams throughout the project keep everyone aligned. For instance, when AI analysis uncovers a specific business rule embedded in old code, business analysts should be there to verify if that rule is still applicable in the current business environment. 

Intelligent Process Automation tools for Refactoring

Several large technology companies, as well as startups, are rapidly developing AI for code modernization. For example, cloud distributors such as AWS provide services that apply machine learning to the code-analysis process. Similarly, Google Cloud is embedding generative AI into its application modernization services. Static code analysis and migration tools are starting to incorporate AI to increase precision and automate processes.

While Augment Code and Niruntara aim for high automation, IBM's Code Assistant is a lower automation tool and more of a facilitator of developer productivity. Using these tools, enterprises have the chance to finally escape the legacy code quagmire and accelerate the journey to modern, agile software systems.

To sum up, before choosing your AI assistant and starting your legacy modernization process, consider the following:
 

1. the type of legacy system (language, platform)
2. the target modernization (e.g., microservices, cloud, new language)
3. the desired level of autonomy
4. the necesary level of control.