How AI is changing the rules of legacy modernisation + why it's more urgent than ever
Legacy systems have been the backbone of enterprise IT for decades, often powering critical business functions behind the scenes. But these systems, many of which were built in the pre-cloud, pre-mobile, pre-AI era, are now holding organisations back. They’re expensive, difficult to maintain, poorly documented, and dangerously brittle. Legacy platforms often rely on outdated technologies that require hard-to-find specialists and are expensive to maintain, especially when customisations pile up over time. They typically lack modern security protocols, making them easy targets for cyber threats. Legacy applications often can't communicate with newer systems, leading to disjointed processes and fragmented data.Despite these issues, many organisations still hesitate to modernise because they fear losing critical functionality, underestimating complexity, or overrunning budgets.
The truth is that modernising legacy platforms using traditional methods continues to be slow, high-risk, and resource-intensive.The good news? AI and low-code are eliminating many of these roadblocks.
AI + low-code are changing the game
With low-code platforms like OutSystems now integrating AI-driven tools, the modernisation process can be dramatically accelerated and de-risked.Here’s how:
Automated Discovery
AI can analyse legacy systems to identify data structures, business logic, dependencies, and validations - even if there’s no existing documentation. This automated reverse engineering lifts the veil on the system’s inner workings, helping teams start modernisation with clarity and confidence.
Requirements Generation
Once a system is understood, AI can convert that analysis into structured, usable requirements documentation. What once took weeks or months of workshops, interviews, and manual mapping can now be generated automatically.
Accelerated Development
AI-enabled platforms like OutSystems allow developers to spin up new applications from simple prompts, URLs, or existing codebases. This dramatically reduces the build time. In one example, a foundational prototype of a complex case management system was generated from an existing codebase in just a few hours. This then enabled the development team to focus on high-value tasks for the modernisation sooner.
Improved Code Quality and Maintenance
AI continues to work in the background post-launch, monitoring and improving code quality. It flags issues, suggests optimisations, and recommends security and integration best practices, all while learning from each release.
Reduced Project Risk
AI-driven discovery, documentation, and development help eliminate the biggest risk factors in legacy modernisation - scope creep, undocumented functionality, and slow delivery. By automating core phases of the SDLC, AI brings predictability and speed to even the most complex transformations.
The rise of agentic AI - and why it raises the stakes
Agentic AI refers to systems that can act on their own: initiating tasks, making decisions, coordinating workflows, and interacting with other systems autonomously. These digital agents are already starting to reshape enterprise IT - from customer service to internal operations.However, there's a catch: legacy systems aren’t ready to leverage the power of Agentic AI.To take advantage of Agentic AI, organisations need agile, interoperable, and adaptable platforms. They need modern architectures that can handle dynamic workflows, integrate data seamlessly, and enable new types of interactions.And critically, they need a clear understanding of the processes that exist in their current legacy systems, in order to make confident and responsible decisions on where AI agents can add value, and how best to govern the responsibility that comes with that.It’s more urgent than ever for companies to bite the bullet and replace their legacy systems.
Real-world impact: core legacy system replaced in 12 months
An equipment hire and industrial solutions company successfully replaced its legacy system in under a year using OutSystems. The result? A 35% reduction in maintenance and operational costs in the first year alone. Using OutSystems' low-code platform, the organisation moved quickly, reduced risk, and freed up budget for future innovation. The company’s technology ecosystem is now fully equipped to leverage Agentic AI.
From laggards to leaders
Legacy modernisation can be as simple as a "lift and shift" of functionality for some companies. Today, it means opportunity. With AI in the mix, organisations can:
Break large systems into modular components
Cleanly separate logic from presentation and data layers
Create reusable digital building blocks for future applications
Migrate incrementally with less risk and more control.
AI modernisation isn’t about rebuilding everything at once. It’s about doing the smart things first - and doing them fast.
Getting Started
Some teams begin with a proof of concept: modernising a single app or workflow, often using OutSystems AI tools to prototype in days, not months. Others start by encapsulating their legacy systems with new front ends, gradually replacing functionality over time.Whichever path you choose, the message is clear:The longer legacy systems linger, the harder it becomes to embrace what’s next.AI and Agentic AI are not future trends - they’re already reshaping how software gets made, managed, evolved and differentiates businesses.Modernise now, or risk being left behind - Talk to us.
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