Our new blog post features how to implement legacy system modernization with AI agents to increase enterprise efficiencies, speed up scalability, enhance security, and slash maintenance costs.
Nowadays, companies that still rely on legacy software in their daily operations face a number of problems. Thus, according to the stats from ServiceNow, such systems cost enterprises about $40,000 a year in maintenance, and 44% of experts experience difficulties in incorporating new techs due to the legacy drain.
With this in mind, stakeholders start taking the legacy modernization side, and here’s the proof. In 2025, the legacy application modernization market size was estimated at $15.14 billion, and by 2029, this number is expected to reach $27.3 billion, growing at a compound annual growth rate (CAGR) of 15.9%.
And before getting down to the grain, describing major strategies of legacy modernization and explaining how you can accelerate the process with AI agents, let’s first cover the essentials.
What is legacy system modernization?
Legacy system modernization is the process of refining, updating, transforming, or even replacing obsolete software within an enterprise to align with modern techs and tailored business needs. As a result of such transformation, you get more scalable, agile, and secure systems, at the same time eliminating technical debt and maximizing operational efficiencies.
The reasons for modernization of legacy systems differ depending on the legacy type:
- Technology-based legacy software that usually runs on COBOL, Fortran, or old versions of Java/Python (that are no longer supported) is prone to attacks, as there are no more security patches for such software or developers with the needed expertise.
- Architecture-based type presupposes that the “bones” of the solution are no longer agile, i.e. monolithic architectures where the UI and logic are glued together and slight changes like changing a button might lead to the system failure.
- Business-based legacy systems create situations when specific business logic was implemented by experts who left the company years ago, and new product launches or pricing model changes are difficult to introduce.
- Integration-based legacy means your outdated systems don’t have APIs and rely on manual data entry, which creates data silos and in turn errors, delays, and a fragmentation vision of your business.
Why do you need to implement legacy system modernization? Four key reasons
The modernization of legacy systems will give a number of perks for your enterprise, and we have the statistical proof.
Cost reduction
Although costly at first sight, legacy application modernization will bring you more bang for your buck. By replacing fragile, outdated foundations with modular, standard-compliant architectures, you eliminate the technical debt, as modern software is easier to maintain and troubleshoot. For example, IBM reports 15-35% yearly savings on infrastructure as well as 74% lower costs on hardware, software, and staff.
Enhanced efficiency
By eliminating code smells and complex logic, your teams start spending less time on manual fixes, thus concentrating on high-value feature development. Besides, modern architectures and languages are greatly optimized for speed, so tasks that took hours can be completed in minutes or even seconds. Resource optimization is another benefit. Instead of running expensive servers at 20% capacity just in case, you can automatically scale up and down cloud resources, reaching maximum output with minimum waste.
Rock-solid security and compliance
Statista says that 58% of enterprises cite improved security as the number-one driver for updating obsolete software. And for a reason. Outdated technology lacks support for modern encryption and multi-factor authentication, making it prone to malicious attacks. For example, a massive data breach at the UK Electoral Commission resulted in disclosing about 40 million individuals’ personal data, and the vulnerabilities stemmed from unsupported, unpatched software. On top of that, by modernizing legacy solutions, you make it a way easier to comply with regulations like GDPR, HIPAA, or PCI-DSS.
Business agility and innovation
According to Forbes, 88% of respondents say they’re preoccupied with the fact that their obsolete software makes them lagging behind more innovative rivals. Another 68% report that old techs prevent them from adopting innovations. In turn, by moving to modern codebases, you can easily connect your solutions with AI, machine learning, big data analytics, and other trailblazing techs, rapidly deploying gestures that will make you stand out from the crowd.
Top-5 legacy application modernization strategies
Introduced by Gartner as 5 Rs, legacy application modernization strategies range from some slight changes to deep transaction and transformations.
Rehosting
Also called as lift-and-shift, this legacy modernization strategy is the fastest and the least expensive. It usually comprises moving the outdated software to a new environment, mostly from on-premises to the cloud — with minimal changes to the app and its architecture. This method is perfectly fitted for:
- Urgent deadlines, when you need to move a data center quickly.
- Cost management, when you want to make a transition from high capital expenditure (CapEx) for hardware to a pay-as-you-go operational expense (OpEx) model.
- Stable legacy that works well and doesn’t need new features.
Pros:
- High transition speed
- Low initial cost as there’s no code rewriting
- Low risks of introducing bugs
- Immediate scalability
Cons:
- Technical debt (messy code) will move to a new environment.
- Missed perks: you won’t benefit from cloud-native features like auto-scaling and serverless computing.
- Cost inefficiency, as your poorly designed solution might consume more cloud resources than necessary.
Use case:
- Netflix started its cloud endeavor with a lift-and-shift to Amazon Web Services (AWS) before later refactoring its entire system into microservices.
Replatforming
Replatforming, or Lift-and-Reshape, is a strategy that includes making a few key adjustments to the legacy software to optimize it for the new environment. This method is typically used when the solution works fine, but there are problems with infrastructure management or licensing costs:
- Managed databases. You can eliminate manual backups and patching by moving from a self-installed SQL Server on a VM to a managed service like AWS RDS or Azure SQL.
- Containerization is another use case. By packing the legacy solution into Docker containers, you make it easy to manage, without changing the actual code logic.
- Middleware upgrades presupposes moving from heavy and costly servers such as WebLogic to more lightweight versions like Apache Tomcat.
Pros:
- Enhanced automation as you eliminate manual backups, OS patching, and hardware scaling.
- Cost effectiveness as managed services are optimized for price-performance.
- Minimal risk because you don’t break the app functionality.
Cons:
- Scope creep that might turn a quick project into a massive one.
- Technical debt still stays.
Use case:
- In the years of its early growth, Pinterest replatformed their data layer to Amazon S3 and managed NoSQL databases, sparing engineers the need of manually managing server maintenance.
Refactoring
This method includes restructuring and optimizing the existing code without altering its external behavior. The code becomes more maintainable to better suit a cloud environment. Here are the use cases, when refactoring is the best choice:
- High technical debt. The code is messy, and it’s difficult and expensive to add new functionality to the app.
- Performance issues, i.e. legacy software is slow or inefficient despite having powerful hardware.
- Long-term value. When the app is critical to your business, refactoring is the optimal investment in code quality.
Pros:
- Increased agility, because the code becomes cleaner
- Enhanced performance due to resource optimization
- Reduced technical debt and easier software maintenance
Cons:
- Time- and effort-consuming
- Risk of regression
- Testing intensive
Use case:
- Etsy refactored its messy PHP code to isolate search logic and implement modern API-first patterns, which resulted in increased solution scalability with no need to rewrite the entire marketplace.
Rearchitecting
The re-architect strategy comprises changing the internal structure and code of the legacy software to move it to a new architecture. This is a fundamental shift that often includes moving from a monolithic structure to a microservices-based / serverless architecture. The situations when minor updates are not enough are the following:
- Scalability barriers. When the outdated software isn’t able to cope with traffic spikes, even when extra hardware is available.
- Incompatibility, namely when legacy apps fail to pair up with modern AI tools, IoT solutions, or advanced data analytics systems.
- Increased technical debt, meaning the cost of maintaining the old system and finding specialized talent outweighs the cost of transformation.
Pros:
- Auto-scaling and high availability
- Resilience (if one server fails, the entire app won’t go down)
- Future proofing (enhanced compatibility)
Cons:
- Quite high cost
- Time-consuming
- Operations complexity
Use case:
- Airbnb moved their Ruby on Rails monolith to a microservices-based infrastructure to reach immense scalability and as a result revenue growth.
Rebuilding
The most radical legacy system modernization strategy, rebuilding presupposes entirely discarding the existing code and developing the software from scratch on a new architecture and tech stack. At the same time, the previous business requirements are maintained.
- Total obsolescence is the code that is impossible to maintain (for example, COBOL or Delphi).
- Logic pollution (impossible to understand or modify) due to many-year messy code fixes.
- Strategic pivot. This includes the necessity to rebuild, when the outdated platform can’t do the things your business needs, for example, real-time AI processing.
- High costs, i.e. when it’s cheaper to build a new system than to keep maintaining the old one.
Pros:
- Zero technical debt
- Optimized system performance
- Competitive edge performance
Cons:
- High risks
- Scope creep
- Large upfront investment
Use case:
- The Capital One bank relied on legacy code and instead of moving old apps to the cloud, they rebuilt the core infrastructure, using modern languages and APIs. It allowed the traditional bank to better compete with trailblazing fintech startups.
Implement modernization of legacy systems
How agentic AI accelerates legacy software modernization
The agentic AI has enhanced a number of business operations, and it also finds its feet in legacy system modernization. To wit, AI agents act as digital teammates, accompanying human developers at every stage of the migration / rebuilding and even executing complex transformation workflows independently.
According to McKinsey, multi-agent systems for modernization can bring tangible benefits, such as a 40-50% acceleration in tech modernization timelines and a 40% reduction in costs (after the elimination of technology debt). Such measurable business impact is possible through automation in the following areas.
Reverse engineering
AI agents don’t just analyze the legacy code, they reverse engineer it to comprehend the core business logic. Special crews of agents also go through millions of lines of code to generate up-to-date specifications, dependencies, and architecture diagrams. Besides, you can translate legacy languages as COBOL to Java with AI agents, thus allowing developers and stakeholders to validate rules before new code is generated.
Code conversion and refactoring
Legacy software modernization is often split between specialized agents. An Architect Agent deals with the code structure, a BA Agent manages business rules, and a Developer Agent implements the actual migration.
Context-aware rewrites are also performed by agentic AI. By understanding the target environment, agents refactor the code with the best practices like turning synchronous calls to asynchronous or updating legacy APIs. If the code fails to compile, they autonomously spot the error, trace it back to the root cause, and fix it.
Automated testing
Considering the fact that testing is often the most time- and resource-consuming phase of modernization, AI agents are here to help. QA agents are able to autonomously generate a huge number of test cases, perform regression tests, and validate transactional integrity after the migration is completed. What is particularly important, they create safe and realistic testing environments with synthetic data not to expose sensitive production data.
How Aetsoft can help with legacy modernization
To effectively operate in a competitive business environment, where legacy application modernization is becoming a necessity, you can’t dispense with the implementation of agentic AI. By leveraging advanced technology in this context, you can speed up the process and significantly increase efficiency.
However, to do it right, you’ll certainly need an expert in artificial intelligence and machine learning like Aetsoft. From overarching consulting to full agentic AI system integration into your modernization process, we’re here to assist, while delivering utmost security, resilience, and cost effectiveness. Rely on our competence in ChatGPT development and related spheres to bring your business to a brand new level. Schedule a call with our engineers to discuss your legacy system modernization endeavors.
FAQ
What is the difference between RPA and Agentic AI in modernization?
Robotic process automation (RPA) leverages a specific pre-described script to perform repetitive tasks. In software legacy modernization, it’s usually used as a temporary bridge to connect old systems that lack APIs to new ones. Conversely, AI agents are goal-oriented and reasoning-based. They use large learning models (LLM) to analyze the intent of legacy code and automatically rewrite it, while troubleshooting its own errors.
How long does a typical AI-driven legacy modernization take?
This process typically takes from 6 months to 2 years, depending on the modernization scope. Still, agentic-fueled legacy modernization gives a 30%-50% acceleration as compared to traditional methods.
What are the risks of using AI agents for code transformation?
Although AI agents have proved to be effective in legacy application modernization, still they’re certain risks like semantic drift (app logic loss), security issues (as AI is usually trained on public code), and compliance problems (in highly regulated industries). To prevent these challenges, partner up with an experienced AI development company.
