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Legacy systems weigh companies down with costs, risks, and inefficiencies. AI-augmented modernization offers a faster, more practical path forward.
A 2024 McKinsey article found that as much as 70% of the software used by Fortune 500 companies was developed 20 or more years ago, and these outdated systems are showing their age. As technology evolves, many companies have found their legacy systems left behind.
Unfortunately, updating these systems can feel like a catch-22: Although traditional modernization is costly, time-consuming, and disruptive, delaying modernization efforts is high-risk.
The answer, in many cases, is AI-augmented legacy modernization. Modern software teams are leveraging cutting-edge AI frameworks to complete legacy modernization projects faster, cheaper, and more smoothly than traditional processes.
What is AI-Augmented Legacy Modernization?
AI-augmented legacy modernization, or AI-assisted legacy modernization, is the use of AI throughout the software modernization process. Software engineers employ techniques such as agentic swarm coding and AI-assisted code refactoring to update and migrate older enterprise systems into modern, scalable environments. Instead of relying only on manual work, tools powered by machine learning and generative AI help developers analyze large codebases, identify dependencies, and translate outdated programming languages into modern ones.
This approach makes it possible for businesses to modernize legacy platforms more efficiently while preserving the critical business logic they depend on.
Why Enterprises Are Turning to AI for Legacy Modernization
Legacy systems are, for many companies, the crucial foundation upon which their operations are built. However, because so much depends on these systems, the time and cost required to update them safely can deter many business owners. According to a 2023 article on tech debt, companies often pay an additional 10 to 20% to address tech debt—or the cost of redressing existing tech issues—on top of the costs of any project.
Fortunately, AI legacy modernization services make updating these systems much easier. Where traditional modernization requires months—or even years—of manual code review, rewriting, and testing, McKinsey found that AI-augmented modernization can accelerate timelines by 40 to 50%. The firm also estimates that using generative AI for modernization can lead to a 40% cut in technical debt–related costs while improving output quality.
How Different Industries Are Modernizing with AI
While legacy systems vary across different sectors, they often share a common problem: outdated technology that is costly to maintain and difficult to update. AI-powered legacy modernization services are becoming a practical way for companies in many industries to update their critical systems.
Financial Services
Financial institutions often struggle with aging mainframe systems that are slow to adapt to new demands. As a result, many are turning to AI legacy modernization services to speed migrations, reduce technical debt, and improve scalability.
In its article AI for IT modernization: Faster, cheaper, better, McKinsey & Co. shares a case study around a FinTech company that adopted AI legacy modernization services for banking to overcome long-standing mainframe challenges. The company needed to modernize 20,000 lines of code, which it estimated would take 700 to 800 hours to migrate properly. However, after deploying an extensive collection of genAI agents, the business successfully whittled that number by 40%.
McKinsey also shared an example from a top 15 global insurer. By implementing genAI modernization services for insurance companies, the organization improved its code modernization efficiency and testing by over 50%, while also seeing a greater than 50% acceleration of coding tasks.
Logistics
The logistics industry has consistently lagged in modernization, with over 75% of industry leaders acknowledging that their sector has been slow to embrace digital innovation. Many companies still rely on legacy transportation management systems, warehouse control software, and outdated ERP platforms that can’t easily scale to meet modern supply chain demands.
AI-driven legacy migration services can help bridge that gap. By automating code analysis, identifying hidden dependencies, and translating legacy applications into modern cloud-ready architectures, logistics firms can move away from brittle, maintenance-heavy systems.
For example, AI-assisted code refactoring tools can prepare decades-old warehouse management software for integration with IoT devices, while AI-powered testing can reduce risks when migrating scheduling systems to the cloud.
For an industry where real-time visibility and operational efficiency are crucial, AI legacy application modernization provides a means to reduce technical debt, enhance system reliability, and create platforms that can support future innovations, such as predictive analytics and autonomous delivery.
Healthcare
Healthcare organizations face unique modernization challenges, including managing sensitive patient data, integrating with clinical systems, and meeting stringent regulatory standards. One 2025 research article on AI-augmented legacy modernization touched on this, citing a case where a healthcare provider modernized its patient management system through AI modernization services with HIPAA compliance.
The provider used AI in several ways:
AI-assisted code translation converted about 65% of the legacy codebase, reducing manual effort while preserving critical healthcare workflows
Deep learning models mapped business processes and dependencies across clinical and administrative systems
AI-powered discovery uncovered hidden data relationships and potential compliance risks, which would have been difficult to detect through manual analysis
Synthetic data generation created safe, realistic testing environments without exposing patient information
According to the article, the results included:
A 50% faster timeline compared to traditional approaches
99.99% accuracy in migrating sensitive patient data
Roughly $12M in direct cost savings
An 85% reduction in post-implementation defects
A 60% drop in infrastructure costs after deployment
Choosing the Right AI Modernization Partner
MIT’s NANDA initiative found that AI projects built by specialized AI vendors or through external partnerships succeed about 67% of the time, compared to only one-third for in-house builds. With that said, however, not every vendor is equally equipped to handle complex modernization work.
Here are a few things to look for when comparing partners:
Legacy system expertise: Modernization projects vary depending on the system. Partners with a background in mainframes, COBOL applications, or ERP platforms are better prepared for the challenges these environments bring, which helps reduce surprises and keep projects on track.
Clear AI approach: Many firms advertise AI capabilities, but not all apply them in useful ways. A strong partner should be able to explain how AI fits into their process—for example, analyzing codebases, mapping dependencies, or automating testing—and why those steps matter for your project.
Transparent pricing and timelines: A reliable partner will be upfront about costs and schedules, giving you details you can actually plan around. If a vendor avoids specifics or changes estimates frequently, it’s a sign they may not have a clear handle on the work ahead.
Compliance knowledge: In regulated industries like healthcare or finance, compliance can’t be left to chance. A partner who understands requirements such as HIPAA or SOX will know how to build them into the project from the beginning rather than treating them as an afterthought.
Industry experience: Beyond compliance, it’s just as important that a partner understands the priorities and pressures of your industry. A team with vertical AI expertise can tailor modernization efforts to match your business’s needs—whether that means speeding up transactions in finance or improving data flows in healthcare.
Long-term support: Modernization doesn’t stop the day your new system goes live. Legacy transitions often require ongoing maintenance, updates, and even roadmap planning to stay aligned with business goals. A partner offering long-term support will help your system keep pace instead of falling behind again.
Modernization projects succeed more often when the right partner is involved. By choosing a team with technical expertise, practical AI skills, industry awareness, and a commitment beyond launch, you can avoid common pitfalls and build a system that’s ready for the future.
Modernizing legacy systems used to mean years of planning and disruption. With genAI, the future looks different: updates can be faster, more targeted, and less risky. Instead of delaying critical projects, enterprises can approach modernization as an ongoing part of their operations, saving them time, money, and stress moving forward.
At FullStack Labs, we help organizations apply these approaches in ways that fit their industry and long-term goals. From early assessments to ongoing support, our aim is to make modernization projects reliable and sustainable. If your team isn’t sure how genAI could play a role in future upgrades, we’d be glad to walk you through your next steps.
Does AI make legacy application modernization faster?
Yes, absolutely. The primary benefit of using AI is significant acceleration. By automating time-consuming and manual steps—such as deep analysis of old codebases, translating outdated programming languages, and generating thousands of test cases—AI tools can reduce the time required for a complex modernization project by 40% to 50%. This speed allows enterprises to move their critical systems into a modern, scalable environment much faster.
What is legacy system modernization?
Legacy system modernization is the process of updating or migrating older enterprise systems so they can operate in modern, scalable environments. Traditionally, this meant years of manual code review, rewriting, and testing. With AI legacy application modernization, many of these steps can now be automated, making the process faster and less disruptive.
Is replacing legacy software worth it?
Yes—though it often depends on the system and business priorities. Maintaining legacy platforms is costly, risky, and inefficient. Some banks, for example, spend up to 70% of their IT capacity just keeping legacy systems running. Modernization can reduce technical debt, improve performance, and free up resources for innovation. AI-assisted approaches make replacement or migration far more viable than in the past.
Does AI modernization reduce costs compared to traditional methods?
Yes, significantly. AI reduces costs both during the project and afterward. During modernization, automation slashes the manual effort required for code review and testing, leading to lower project costs and accelerating your return on investment (ROI). Post-modernization, the new, cleaner, and more efficient code architecture results in an estimated 40% reduction in technical debt, lowering long-term maintenance expenses, and freeing up specialized IT resources.
What are the problems with legacy systems?
Legacy systems often face slower performance, security vulnerabilities, and limited compatibility with new technologies. They also tend to have high running costs, are harder to maintain as skilled developers retire, and carry mounting technical debt. Over time, these issues limit agility and make it difficult for organizations to keep pace with competitors.
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