AI in Legacy Modernization: How to Accelerate System Upgrade?

Somewhere deep in your architecture, there’s a system no one fully owns. It operates quietly — sometimes too quietly — patched over the years by developers who left more questions than answers. The original engineers? Long gone. Documentation? Scattered or missing entirely. What remains is an aging monolith: undocumented, fragile, and dangerously opaque.

Many of these systems were designed when innovation meant ditching filing cabinets for spreadsheets. Whether it’s COBOL, Java 6, or early .NET, they reflect the tools and assumptions of their time. Back then, they served their purpose. Today, they absorb resources and introduce risk with every update. But waiting comes at a price. 

Delaying modernization only drives up the cost — in downtime, in risk, in missed opportunities. This is where AI comes in as a precision tool that makes modernization faster, smarter and far less painful.

In this article, you’ll learn how Devox Software uses AI to accelerate the modernization of legacy systems. We’ll show you how we use intelligent code analysis, automated architecture mapping, and intelligent refactoring to breathe new life into old systems with less risk and more control.

The True Cost of Outdated Systems

What is legacy technology? It’s not just “old tech”. Legacy technology refers to systems, software or infrastructure that were developed for a different era — often rigid, monolithic and deeply embedded in business-critical workflows. These platforms are usually older than modern cloud environments, APIs and cybersecurity protocols. They are not scalable, can hardly be integrated and are often based on outdated programming languages or outdated hardware. But because they still “work”,” companies keep them alive.

But inheritance is not always about age. It’s also about relevance. You can still write legacy code today — you just have to use frameworks or architectures that don’t scale, integrate or adapt. In a world where technology is evolving by the month, software is becoming obsolete faster than ever before. Systems that were developed just a few years ago can already feel ancient if they weren’t designed with flexibility in mind. That’s the harsh reality of modern IT: legacy is a moving target.

The first thing you must face with a legacy system is a lack of talent. Disappearing skills: COBOL, Fortran, and even older versions of Java and .NET are disappearing faster than organizations can find replacements. When the engineers who developed your legacy systems retire, they take decades of undocumented logic with them. The few experts that do exist charge top dollar. It comes as no surprise that in 2025, statistics show that companies, as a result, spend up to 60-80% of their IT budgets on maintaining legacy systems.

Secondly, product development cycles are no longer measured in years — they’re measured in weeks. However, companies tied to legacy systems operate on a different timeline. Without modern DevOps pipelines, every new feature requires painstaking testing, manual deployments, and extensive regression analysis. Every duct-taped workaround and “temporary” solution that became a permanent fix has only led to one thing: a system that is more expensive to maintain than build from scratch.

Next, legacy platforms often lack secure API frameworks and still rely on hard-coded credentials, outdated encryption, and long-outdated authentication protocols. This makes compliance nearly impossible. Updating or integrating new features requires navigating a maze of dependencies, outdated libraries, and undocumented code. Simple changes that should take days can drag on for weeks. Worse, many legacy systems are monolithic, meaning a single failure can impact the entire platform.

Finally, the burden of technical debt not only slows down development teams, but also dictates business decisions. The actual cost of outdated software is not just in the maintenance but also in what it prevents you from accomplishing. Every day you spend with an obsolete infrastructure is a day your competitors are one step ahead. Embedded finance, real-time analytics, and personalized customer experiences require modern architectures that legacy platforms can’t support.

For organizations that are ready to act, AI-powered modernization offers a faster, smarter, and more cost-effective path forward.

Why Traditional Methods Can No Longer Keep Up

The modernization of legacy systems has long been treated as a high-stakes relay race: review system, document code, plan rewrite, test, deploy. On paper, the process is structured and even predictable. But the reality is far more chaotic, especially when you’re dealing with an undocumented, business-critical system that generates millions in revenue every month.

Let’s start with the obvious: traditional approaches assume that someone still understands the system. That there is a map. But in practice, every function you touch feels like disarming a mine without a schematic.

Reverse engineering involves this kind of code and takes a lot of time. Entire teams can spend weeks deciphering the intent from the syntax. Why was it written this way? What happens if we remove this block? Who decided that this method should be executed at startup? These questions lead to long sessions, cautious commitments, and halting progress. And that’s before you hit the external dependencies: Libraries that are no longer maintained, frameworks whose lifespan has expired, and third-party integrations that no one wants to touch. And then there’s the human factor. Skilled legacy developers are a limited and shrinking resource.  

In the meantime, the system is still in operation. It creates invoices, runs reports, and manages user logins. You can’t simply put the business on hold while you modernize. The tension between stability and progress makes legacy projects so uniquely tricky. If you move too fast, you break something critical. Move too slowly, and the technical debt will continue to grow.

And let’s not forget economics. Traditional modernization is resource-intensive. It requires time, talent, and budget — all at once. At some point, someone in the boardroom wonders if it wouldn’t be easier to “put it off a bit longer.”

This hesitation is the real risk. While you wait, systems become more fragile; compliance gaps widen, and innovation stalls. Legacy issues become a tax — not just on technology but on growth itself.

Traditional approaches are suffering under this burden. They were not designed for undocumented complexity, vulnerable dependencies, and the reality of rebuilding at full operation. And that’s exactly why AI isn’t just a nice-to-have — it’s a necessity.

Overcoming Modernization Challenges with AI

Legacy modernization projects are difficult. Сompanies must first untangle layers of technical debt and opaque system logic before meaningful progress can be made. Traditionally, the discovery process, which involves reverse-engineering thousands or even millions of lines of poorly documented or undocumented code, has taken months or even years.

But AI-driven tools are changing the equation.

Business Logic Discovery Engine

Before you can modernize, you must understand. That’s the paradox of legacy systems: the older they get, the less visible their logic becomes. Before you touch a single line of legacy code, you need to understand the business logic behind it. Not just how the system works, but why.  In the past, this meant months of manual code analysis, but AI is fundamentally changing the way teams approach this phase.

AI-supported analysis uncovers patterns, identifies dependencies and reconstructs decision rules that are hidden deep in the system. This gives your team a clear overview of what the software really does and why it is important. Using AI tools, teams can hypothesize products, align with market dynamics in real time and quickly build business cases based on data-driven insights, not assumptions.

In this phase, modernization moves from a reactive clean-up to a proactive design sprint. You’re not just updating systems, you’re designing the next generation of your digital product in consultation with the business from the start.

AI-Driven Legacy Unpacking

In the past, this meant months of manual code analysis, but AI is fundamentally changing the way teams approach this phase.

Artificial intelligence recognizes the complex patterns and hidden business rules deep within legacy systems. Where teams of engineers used to spend endless hours manually reviewing code, an AI solution such as GitHub Copilot, Amazon CodeWhisperer, or custom solutions (such as CodeConcise from Thoughtworks) is more efficient. The latest versions of artificial intelligence can scan, map, and document existing logic quickly and with remarkable clarity. The speed and precision of these AI solutions give teams the clear overview they need to make strategic architecture decisions, turning months of manual analysis into just a few weeks.

By transforming raw code into human-readable insights, these tools give architects and analysts an unprecedented understanding of “what’s really going on” Instead of searching for undocumented logic scattered across hundreds of modules, teams get contextual overviews, logical flows and clean documentation within days, not next quarter.

What used to be a black box is now a readable blueprint — thanks to AI.

NLP-Powered System Mapping

Advanced AI tools use natural language processing (NLP) and large language models (LLMs) to analyze legacy code bases and turn opaque, outdated code into structured insights. This automated documentation enables engineers to gain a clear understanding of technical dependencies quickly. At Devox Software, we have used such tools effectively, allowing organizations to decode their complex legacy systems rapidly. Once the logic is clear, AI can further analyze the code to pinpoint redundant processes, inefficient structures, and potential performance bottlenecks.

Legacy-to-Cloud Migration

Another major obstacle to modernizing legacy systems is translating older languages, such as COBOL or outdated Java frameworks, into modern, cloud-ready stacks.Traditional migration or refactoring projects are usually costly, and often lead to errors from the past being repeated or, worse still, new errors being created.

AI-driven refactoring reduces these risks. Using generative AI models trained on large code bases, teams can automatically transition outdated legacy applications into modern architectures with minimal human intervention. The AI not only translates the syntax line by line but also intelligently restructures the code to align it with current best practices.  

GenAI Refactoring Engine

In terms of reducing strategic risk, AI also gives companies a better overview of the progress of modernization projects. Predictive analytics and AI-generated documentation ensure stakeholders have an accurate, real-time overview of the status of each modernization project. This allows management to proactively address potential issues and have a precise overview of the status of each modernization project. Projects stay on budget, on schedule, and track. 

Built-In Risk Mitigation

Finally, introducing AI into legacy modernization is fundamentally changing how organizations manage project risk and resource allocation. Human error, subjective interpretations, and inconsistencies are inevitable. However, AI mitigates these problems by providing consistency, precision, and a strategic overview. The AI-based approach introduces robust safety nets through automated tests, comprehensive code reviews, and constant monitoring. 

Ultimately, AI not only facilitates modernization, it completely redefines it.

The AI-powered Modernization Roadmap

Companies already know that their outdated architecture is slowing them down, but they don’t know how to fix it without destroying everything else. With AI-driven methods, the journey from monolithic legacy applications to agile, cloud-native platforms is measurable and highly efficient.

At Devox Software, we’ve distilled this approach into a four-step, AI-powered modernization roadmap that helps our customers transform faster than they thought possible.

Phase 1. AI-Supported Business Analysis

Modernization begins long before the first line of code. For organizations working with legacy systems, the first challenge is to understand all the business logic encoded in outdated software.

At Devox Software, we approach the transformation of legacy systems by first reconstructing the business intent of the system. AI-powered analysis uncovers what the system does, why it was designed that way, and how it needs to evolve to support new goals.

Our generative AI agents work on multiple levels: codebases, documentation archives, user workflows and stakeholder input. By analyzing these sources in parallel, they uncover hidden dependencies, conflicting requirements and structural inefficiencies that human discovery teams often overlook or misinterpret.

The result is a comprehensive business map that reveals:

• Core functionalities that should be retained and improved
• Redundant functions that need to be eliminated
• Bottlenecks and risks in the current architecture
• Strategic opportunities for redesign

In result, instead of months of isolated stakeholder interviews and fragmented documentation reviews, our clients receive a consolidated, AI-generated blueprint of their legacy system — in a matter of days. This artifact becomes a strategic anchor that aligns product, technical and business stakeholders around a single understanding of the system’s current role.

Phase 2: Understanding code

Most legacy systems have outlived their architects and what remains is a jumble of tribal knowledge pieced together over the years with quick fixes. Teams spend months reverse-engineering an unwieldy codebase.

We break this cycle. Using AI tools trained to dissect old codebases, we uncover everything that matters — business rules, logic chains, and invisible dependencies. Platforms like GitHub Copilot or CodeConcise from ThoughtWorks recognize the syntax and go deep into the business logic, rules, and data flow. Imagine having a brilliant translator that instantly decodes the DNA of your old system into precise, actionable insights.

Phase 3: Migration зlanning

Once you’ve figured out how your system works, the next challenge is migration planning. AI systems simulate migration paths and highlight risks early — before they are reflected in budgets or schedules. Complexity is mapped. Interdependencies are uncovered, not in a vacuum, but based on actual architecture and performance figures.

With real-time analytics, you can simulate migration scenarios, identify hidden problem areas, and set realistic budgets. More importantly, you’ll have the necessary information to gain executive buy-in.

Phase 4: Automated refactoring and testing

Now comes the hard work: transforming the legacy code base into a modern, agile architecture. The old way is labor-intensive, expensive, and prone to human error. Our approach combines AI-driven refactoring with automated testing to keep the timeline short and the risks low. We automate the heavy lifting — translating outdated code into modern frameworks while preserving the functional logic that still has value. The result is tested, verified, and continuously compared with the business results.

Tools like GitHub Copilot, OpenRewrite, and BrillioOne.ai can independently rewrite outdated syntax, reorganize clunky structures, and adapt your app to modern standards. At the same time, AI-driven tests validate that your business logic and functional performance stay intact after refactoring. As a result, refactoring does not slow us down. It speeds up delivery — with AI-driven test coverage and error rates so low that the QA team no longer has to wait for the impact.

Phase 5: Deployment & Optimization

The moment of truth.

Deployment in a modern infrastructure presents several stumbling blocks, but AI mitigates the situation. We integrate intelligent CI/CD workflows and monitoring tools that report metrics and understand them.

Even with a revamped codebase, final deployment is often fraught with integration issues and performance uncertainties, especially if you’re moving towards cloud or hybrid environments. But AI-driven frameworks tackle these issues head-on, diagnosing potential problems before they derail your project. Bottlenecks are detected before users feel them. Systems adapt without waiting for a ticket. Optimization is not planned — it is continuous.

This AI-driven roadmap unlocks new opportunities. By shifting routine work to intelligent systems, the development team gains the freedom to innovate.

Is Your System a Candidate for AI-Powered Modernization?

Legacy systems rarely announce their obsolescence. There’s no flashing warning light, no urgent alert. Instead, the signs appear gradually — in slower deployment cycles, unexplained outages, and the quiet avoidance of even minor updates. Left unchecked, these warning signs evolve into structural weaknesses undermining resilience and stall innovation.

At Devox Software, we’ve learned to recognize the early indicators that signal when AI should be part of the modernization strategy. These aren’t theoretical markers. They’re practical red flags we’ve seen inside real businesses across industries.

Here’s what we watch for: 

1. The system has outlived its creators.
   When no one on the current team can explain the internal logic of the system, the operational risk increases. Without context, even routine maintenance becomes speculative. AI-driven code analysis helps restore the lost logic by uncovering previously just guesswork structures.
2. Documentation is incomplete, outdated or missing altogether.
   Many legacy systems are surrounded by layers of notes lost in old email threads. Attempts to document them retrospectively often fail due to the complexity. AI tools trained on large code bases can automatically create architecture diagrams, uncover hidden dependencies and identify critical business logic.
3. Small changes lead to unexpected consequences.
   If a small update in a module leads to regressions in seemingly unrelated areas, the system is no longer predictable. This indicates tightly coupled components and unclear boundaries — a perfect use case for AI-powered refactoring and modularization.
4. Integration with modern systems is becoming increasingly difficult.
   APIs are rigid, data formats are custom, and external systems require manual workarounds. These friction points show that the system was not designed for today’s ecosystem. AI can speed up the mapping of interfaces and support the creation of new levels of integration without completely rewriting the system.
5. Technical debt is growing faster than it can be reduced.
   Each sprint leads to more workarounds, duplication, and complexity. Engineers avoid the legacy module because they “don’t want to break it.” This hesitation signals stagnation. AI can help sort through the debt, identify risk areas, and initiate targeted remediation.
6. There is no visibility into what the system is doing.
   Logging is inconsistent, monitoring is fragile, and troubleshooting requires a deep dive into unknown code. These blind spots are more than just a technical nuisance. They are a potential compliance and security risk. AI can help uncover hidden behaviors and map data flows that are important to corporate governance.
7. Important business processes are locked in outdated code.
   Often, legacy systems are not just infrastructure. They are the business. Billing modules, pricing logic, and user authorizations — all deeply embedded, undocumented, and business-critical. Extracting and preserving this logic with precision is one of AI’s most important contributions to secure modernization.
8. Your development team treats the system like a black box.
   It speaks volumes when developers actively avoid the codebase or develop new features around the system instead of working within it. It shows a lack of trust and a system that is no longer evolving.

AI opens up the code base, but insight alone is not enough. You need a partner who knows what you can do with it. We combine intelligent tools with in-depth technical expertise to help you move forward quickly — without neglecting the essentials.

Sum Up

Some systems were never built to last decades. Yet many of them are still around, quietly executing important workflows and integrating them into business operations’ foundations. Not out of strength but out of habit. Not because they inspire trust but because no one dares to touch them.

These systems were not designed for today’s digital environment. They resist integration, slow down change, and make teams navigate around them rather than through them. AI will not eradicate this complexity. But it does offer something powerful: leverage. What used to take weeks of trial and error can now lead to informed decisions in hours.

At Devox Software, we’ve seen what happens when companies stop tiptoeing around their legacy technology and start working with it — strategically and intentionally. The results are unmistakable: faster release cycles, greater confidence in delivery, fewer surprises, and systems that finally keep up with the pace of business.

The systems that got you here won’t get you where you want to go next. But they don’t have to stop you either. Legacy systems become more than old code with the right tools and partners. It becomes a launch pad for what’s next.

That transformation begins the moment you stop seeing legacy as a problem and start seeing it as a basis for decision-making.

Let’s build from here.