Small and mid-sized enterprises (SMEs) often carry a heavy burden of legacy technology. Products developed over decades – with thousands of lines of code in ageing languages like Pascal, LabVIEW, Fortran, COBOL, or early C/C++ – become increasingly difficult to maintain as time passes. The code is often undocumented and was developed by one or two engineers (typically the founders) who are now nearing retirement. As these baby-boomer experts exit the workforce, their hard-won tribal knowledge leaves with them, and the company is left with software or hardware that few can understand or update. This technical debt isn’t just an IT inconvenience; it’s a barrier to growth that “erodes structural confidence” in the business[1]. In fact, McKinsey has noted that 71% of an organisation’s transformation value depends on technology, meaning unresolved tech debt puts the entire business strategy at risk[2].
The Legacy Burden in SMEs: Why do so many SMEs end up in this situation? The reality is that many smaller companies start by “building software in a state of survival,” prioritising quick fixes and feature delivery over documentation or architecture[3]. Over the years, layer upon layer of new features and patches accumulate without ever refactoring the core. Common issues include poor documentation, lost internal knowledge through staff turnover, years of bolting on new requirements without cleanup, and a lack of skills to handle archaic languages and tools[4]. The result is often what software architects call a “big ball of mud” – an entangled codebase where small changes feel risky and no one fully understands the whole system[5][6]. For example, legacy COBOL systems continue to run critical operations in banking and government, but nearly 60% of COBOL experts are expected to retire within the next five years, creating a looming talent crisis[7]. SME product lines built on ageing tech face the same “grey tsunami” of retiring experts and vanishing support.
When it’s time to develop a new product or update an existing one, SMEs have an unenviable choice: continue evolving the legacy system (fast in the short term but potentially brittle), or break from the past and rewrite everything from scratch in a modern platform (slower upfront, but future-proof). Neither path is easy. Evolving tangled legacy code can be like building on a cracked foundation – making quick progress, but with a risk of collapse. A full rewrite in a modern language (e.g. C#, Java, Rust, Python) promises a clean slate, but it’s expensive, time-consuming, and risks losing hidden “features” of the old system that customers rely on. In domains like operating systems, device drivers, or real-time embedded controls, high-level languages might not meet strict performance requirements, forcing developers back to C/C++ or Rust for low-level efficiency. On the hardware side, legacy printed circuit boards (PCBs) or custom mechanical parts pose a similar dilemma: you might not have original CAD files or documentation, and the old components may be obsolete. Re-engineering hardware without drawings or test data means essentially reverse-engineering your own past product, with all the uncertainty that entails. It’s no wonder many SME owners cite legacy tech as the thing that “keeps them awake at night.”
So, how can we overcome this barrier? The good news is that modern advancements in Artificial Intelligence (AI) – especially in machine learning and Generative AI – are opening up new avenues to tackle legacy technical debt. AI won’t press an instant “rewrite” button for your 20-year-old codebase or magically regenerate lost schematics. What AI can do is act as a supercharged assistant, helping your team extract buried knowledge, automate tedious refactoring, and mitigate the risks associated with modernisation. In this article, we’ll explore three major approaches (supported by recent research and real case studies) by which AI is helping SMEs modernise legacy software and hardware. These approaches range from AI-driven reverse engineering to automated code migration to intelligent testing via digital twins. Each can play a role in transforming legacy systems from roadblocks into springboards for future growth.
AI-Powered Reverse Engineering and Documentation
One of the biggest hurdles in dealing with legacy systems is simply understanding what you have. Often, the original architects are no longer available, documentation (if it ever existed) is hopelessly outdated, and the code or schematics have evolved in an organic, ad-hoc manner. Before you can replace or update a legacy system, you need to know how it works – its architecture, business logic, interfaces, and quirks. This is where AI excels: digesting large amounts of unstructured information and producing coherent knowledge.
For software codebases, generative AI (like large language models) can analyse old source code and generate human-readable explanations, diagrams, and even specifications. This essentially creates documentation after the fact. A recent Thoughtworks case study described using generative AI to help a client reverse engineer a 15-million-line legacy codebase, whose modernisation program was running nine months late due to documentation bottlenecks. By leveraging AI, they were able to generate documentation much more quickly and in a form tailored to the needs of business analysts, cutting the reverse-engineering time by two-thirds[8][9]. This freed up the client’s domain experts to spend more time designing the “to-be” system architecture instead of tediously documenting the old “as-is” system[10]. In other words, AI acted as a force multiplier for understanding the legacy system, bridging the knowledge gap so that modernisation could proceed faster.
Modern AI coding assistants can also work at a more granular level to explain code to developers. Tools like GitHub Copilot, ChatGPT, or GitLab Duo can ingest a chunk of obscure legacy code and produce summaries or comments that clarify what the code is doing[11]. This is incredibly useful when you’re dealing with a 20-year-old module written in, say, COBOL or classic ASP – languages that few on the current team may read fluently. In fact, a GitLab survey found that 34% of developers using AI in software development are doing it to modernise legacy code (this figure was even higher – 46% – in the financial services sector, which relies heavily on legacy systems)[12]. Even if AI’s explanation isn’t perfect, it provides a starting point for engineers to orient themselves in the legacy codebase. Think of it as an AI junior developer who tirelessly reads through ancient code and writes the missing “README” for it.
In the hardware realm, AI-driven reverse engineering is equally transformative. We now see AI “agents” that can take physical parts or old blueprints and reconstruct digital models. For example, DemandBlue has developed a Reverse Engineer Agent that uses computer vision and multi-angle photos to convert real-world mechanical parts into full 3D CAD models and assembly diagrams[13][14]. Using a series of AI modules (for vision, perspective selection, relational reasoning, etc.), the system can extract the geometry and relationships of components from photographs or scans and output a CAD file as if the part had been designed in modern software[14][15]. One case study involved an old CNC machine chuck assembly for which no drawings were available. Technicians took eight photos of the disassembled parts, and the AI agent generated a parametric 3D assembly model with a complete bill of materials in just six hours [16][17]. The resulting digital model included an exploded view and a reassembly guide for the legacy part[18]. This kind of AI tool effectively captures “tribal knowledge” about legacy hardware and makes it explicit. An SME facing a legacy PCB with no schematics, or a custom mechanical widget from 1995 with no CAD files, can also utilise vision-based AI to accelerate reverse engineering. The AI won’t know a priori what the part does, but it can precisely map out form, fit and function, producing schematics or 3D models that engineers can then refine and validate.
By leveraging AI for reverse engineering, SMEs can rapidly build an intermediate specification of their legacy product – whether it’s software architecture diagrams or hardware CAD drawings. This specification serves as the Rosetta Stone, enabling the new generation of engineers to understand the old system. Crucially, keeping this process in-house (with AI as an assistive tool) ensures that your company captures the knowledge internally rather than relying on external consultants to decipher your legacy (and potentially walking away with that knowledge). In summary, AI helps turn tacit knowledge into documented knowledge, which is the first critical step to any modernisation effort.
AI-Assisted Code and Design Modernisation (Refactoring & Migration)
Understanding a legacy system is half the battle – the next step is actually upgrading it. This could mean refactoring the existing codebase in place (improving its structure without changing its external behaviour), or translating it into a modern language/platform, or even designing a new system that replaces the old. Traditionally, all of these options were labour-intensive and prone to errors. Here again, AI is acting as a catalyst, enabling teams to modernise legacy code and hardware faster and more safely than before.
For software, a powerful approach is to utilise AI for automated code refactoring and translation. Modern AI models can ingest legacy source code and generate equivalent code in a different language or a cleaner style, all while preserving the original functionality. This goes beyond basic transpilers because AI can infer the intent of the code. For example, Large Language Models (LLMs) can perform what one VP of Technology calls “LLM-supported rewrites” – automatically applying structural changes across a codebase, such as updating APIs, removing obsolete patterns, or breaking down a monolith into modular layers [19]. Unlike simple search-and-replace scripts, AI-based codemods incorporate context; they strive to preserve business logic and behaviour while making the improvement[19]. In practice, this has enabled features such as middleware and adapter generation, where AI helps create shims that connect old systems to new platforms [20]. This is an attractive middle path between a complete rewrite and doing nothing: AI can wrap the legacy system with modern interfaces or gradually replace components, so that you don’t have to “rip and replace” all at once[20].
Several real-world examples highlight massive gains from AI-assisted code modernisation. Techolution reports that modern tools (including their in-house platform) can automate dependency mapping and code conversion with up to 80% reduction in manual effort for legacy modernisation projects[21]. In one case, a healthcare company collaborated with an AI-driven service to convert 8.5 million lines of COBOL code to Java with 99.5% accuracy, completing a migration that would have taken years by hand in a matter of months [22]. The AI was able to handle the bulk of rote translation, while humans oversaw the process and focused on edge cases – a pattern that’s emerging as a best practice. Similarly, JetRuby’s engineering team used GitHub Copilot to help modernise a Java Struts-based system and reported that manual coding work dropped by roughly 70% on average for each module they upgraded[23][24]. Tedious tasks, such as converting legacy UI tags or refactoring classes, were accelerated from hours to minutes with AI assistance [25]. In one module example, 51 hours of manual work were reduced to 16 hours (a 74% time savings) after introducing AI, with Copilot even generating unit tests to cover 85–90% of the critical logic[26][27]. These numbers are game-changing for SMEs with lean teams – it’s like adding a dozen junior developers who work tirelessly on your legacy code transformations, guided by the expertise of your senior engineers.
AI-assisted modernisation isn’t limited to just translating code syntax. It also helps with optimising and improving the code during the migration. AI can identify dead code, detect duplicated logic, or suggest more efficient algorithms as it refactors. For instance, an AI analysis at Commonwealth Bank identified redundant COBOL processing routines that had been running (and wasting resources) for decades[28]. By stripping out such bloat during the modernisation, the new system became leaner and cheaper to maintain than the legacy ever was. In essence, AI not only ports your code to a modern form but also often cleans and polishes it in the process – something that would be extraordinarily hard to do manually without compromising functionality.
Modernisation of hardware and embedded systems also benefits from AI, albeit in different ways. One approach is using generative design AI tools for hardware re-engineering. Engineers can input the requirements or constraints of a legacy component, and generative AI will explore thousands of design permutations to propose an optimal modern replacement[29]. For example, given a mechanical part that needs to fit an existing machine and meet certain strength specs, AI-driven design software can produce a new CAD model (perhaps using lighter materials or a more efficient geometry) that satisfies those constraints[29]. The engineer gains a head start on redesigning legacy hardware, with AI handling the computationally intensive search for design alternatives. AI is also helping at the chip and PCB level: several tools can generate hardware description code or PCB layouts optimised for new components, which is useful when you need to redesign an old circuit using newer, available chips[29]. Essentially, AI can automate parts of the electronic design process, ensuring that the modern equivalent of a legacy board is correct and optimised, reducing manual engineering effort.
Another practical strategy for legacy hardware is a hybrid retrofit, where you replace some parts of the old system with modern equivalents while keeping the rest. While not purely an “AI solution,” this approach is informed by the analysis that AI provides. For example, an SME might use AI analysis to determine which parts of a legacy machine controller are causing performance bottlenecks or are at the highest risk of failure. Those parts (say an old microcontroller board) could then be redesigned or replaced with a modern microcontroller, while the surrounding systems (sensors, actuators, mechanical framework) are retained. ADUK, an engineering firm, describes a case where they updated an outdated industrial control board by redesigning its core modules with a modern microcontroller, preserving the rest of the architecture and extending the system’s life by another decade[30]. We can imagine AI’s role here being to verify compatibility and help generate the interface logic so the new microcontroller can emulate the old board’s behaviour. This task might be assisted by AI-based code generation if the old firmware needs to be ported. Indeed, specialised generative AI “agents” are emerging that can manage end-to-end legacy software migration (analysing code, converting it, and even optimising it)[31]. These AI agents enable incremental modernisation, where you gradually replace pieces of the legacy system with new components, rather than undertaking a risky big bang approach. The AI ensures that each piece you swap can communicate with the remaining components of the old system.
It’s worth emphasising that incremental modernisation is often the safest route for SMEs, and AI makes it far more feasible. Generative AI can help preserve the business logic and user experience during a transition[32]. Instead of throwing away decades of proven functionality (and forcing your customers to accept a totally new system overnight), AI lets you rejuvenate the legacy system step by step. For instance, you might start by using AI to create a modern API layer on top of a legacy database, then gradually replace the back-end logic behind that API with a new service – throughout, the API ensures continuity. In the past, writing such adapters and shims for compatibility was a painstaking manual effort; however, with LLMs that can generate code, these interfaces can be semi-automatically created [20]. One blog aptly noted that GenAI is transforming legacy app modernisation by allowing enterprises to modernise incrementally while extending the lifespan of critical legacy systems, rather than a disruptive rip-and-replace[32].
In summary, AI gives SMEs a toolbox to future-proof their software and hardware. It can translate legacy codebases into modern languages with high accuracy, refactor and optimise as it goes, and even suggest architecture improvements. It can assist in redesigning legacy hardware or integrating new hardware with old systems. The result is that modernisation projects, which previously seemed insurmountable (or unaffordable), are now within reach for a small team. As one Deloitte study put it, AI and automation can dramatically reduce the time and cost required, turning even a massive legacy overhaul into a series of manageable steps[33][34]. By harnessing these AI capabilities, SMEs can update their tech stack to current standards – faster than their competitors, and with minimal disruption to their existing customers.
Intelligent Testing, Validation, and Digital Twins to De-risk Modernisation
Even with a good understanding of the legacy system and AI-assisted development of the new system, one question looms large: How do we ensure the new or refactored system works as intended? When you’ve spent decades refining a legacy product, it accumulates countless bug fixes and corner-case behaviours. Users have come to rely on its quirks. Reimplementing or changing it risks breaking something essential or disappointing long-time customers. This is why testing and validation are absolutely critical in any legacy modernisation. Here, AI offers innovative solutions through smart testing tools and “digital twin” simulations that can significantly reduce the risk of modernisation.
AI-driven testing can address the gaps left by years of minimal documentation and sparse test coverage in legacy projects. Traditional test creation for a legacy system is daunting – how do you write tests for behaviour that’s not even well-documented? AI can help by analysing legacy code and user logs to identify scenarios and edge cases that require testing. For example, AI can perform “test gap detection,” scanning through code changes or running traces of the old system to identify logic paths that lack corresponding tests [35]. Instead of just aiming for blanket code coverage, these AI tools prioritise high-risk and critical behaviours – such as edge cases known to correlate with past production bugs[35]. They can then generate test cases (with plausible inputs and expected outputs) to cover those paths[36]. In one scenario, an AI test generator increased code coverage by 33 percentage points on a legacy service and reduced escaped defects by 40–60%[37][38]. Similarly, JetRuby’s use of Copilot not only sped up coding but also generated regression tests; in their case, Copilot was able to generate unit tests covering 85% of the critical logic in the refactored modules, compared to roughly half that coverage earlier [26]. These AI-generated tests act as a safety net, ensuring that the modernised code hasn’t unintentionally deviated from the legacy behaviour.
Beyond code testing, AI-powered monitoring and quality gates can be embedded in the development pipeline. As developers write new code (or AI writes it for them), AI assistants can flag inconsistencies or risky changes in real-time. For instance, AI code review tools can highlight whether a proposed change affects a historically unstable module or if it diverges from the system’s architectural patterns [39][40]. They provide context, such as, “This function you’re modifying has caused 80% of past incidents in module X,” giving developers immediate situational awareness. This kind of operational AI governance – where an AI continuously watches the codebase for anomalies or “drift” from the intended design – helps maintain code quality as the system evolves[41]. It’s like having a guardian that remembers the lessons of thousands of past code reviews and production incidents, and gently reminds your team before they introduce a new bug. The outcome, as reported in practice, is significant: escaped defects can be cut by half when such AI-driven guardrails are in place[39][42]. For an SME, this means fewer nasty surprises when the new system goes live, even if the team modernising it is relatively junior or unfamiliar with the legacy intricacies (since the AI is helping catch their mistakes).
Perhaps the most exciting development in risk mitigation is the use of Digital Twins for legacy systems. A digital twin is a virtual model that replicates the behaviour of a physical system or a piece of software. In the context of legacy modernisation, imagine having a high-fidelity simulator of your current legacy system. You could then test your new replacement system against this simulator to see if it behaves the same way under all kinds of scenarios – before you deploy it to real users. AI makes creating and utilising such digital twins much more practical. Generative AI, for example, can learn from historical data and logs to mimic how the legacy system responds to inputs. One article notes that AI-powered simulations of legacy systems allow businesses to test modernisation approaches safely before applying them in production[43]. For instance, if you are replacing a legacy warehouse management software, an AI-driven digital twin can simulate transactions, inventory updates, and queries exactly as the old system would, allowing you to verify that the new software yields identical results. Deloitte’s research highlights companies starting to use digital twin simulations to understand the impact of “pulling the plug” on a legacy system – essentially rehearsing a system switchover in a virtual environment to spot any issues in advance[44][45]. This technique dramatically reduces the fear of the unknown. It’s like performing surgery in a detailed VR simulator over and over until you’re confident the real procedure will succeed.
Digital twins aren’t limited to software behaviour; they’re equally valuable for hardware. In industrial settings, you might create a digital twin of a legacy machine – its vibrations, outputs, heat generation, etc. – and then test how a new control system or a new component would perform by plugging it into the twin. AI comes into play by handling the complex modelling of these systems. In manufacturing, for example, AI-driven plant simulations can model a legacy production line and predict how changes (new equipment, updated settings) will affect throughput and quality[46]. By doing this, SMEs can avoid costly downtime; they can iterate on the “virtual legacy system” until the upgrade method is perfected, and only then apply it in real-world settings.
Another facet of AI in validation is predictive analytics during the modernisation process. As you modernise, AI can monitor the health of both the old and new systems and alert you to anomalies. If the legacy system is still running in parallel (a common strategy during phased migrations), AI can detect unusual patterns that might indicate a looming fault – allowing you to address it proactively rather than in crisis mode[43]. AI can also ensure that security and compliance are maintained throughout modernisation. Legacy systems often have hidden security holes; AI tools can scan both legacy and new code for vulnerabilities and even auto-generate patches for known issues[47]. This is crucial because you don’t want to introduce new security risks while trying to modernise – or leave old ones lurking.
Putting it all together, a combination of smart testing and digital twinning gives SMEs a robust safety harness for modernisation. You begin by building confidence in understanding the legacy (Approach #1), then execute the upgrade with AI assistance (Approach #2), and finally, thoroughly validate the results with AI-driven tests and simulations (Approach #3). For example, you might use an AI to generate a suite of regression tests from your legacy system’s behaviour, then have those tests run against the new system being built. Any deviations get caught and fixed early. Meanwhile, a digital twin might be used to simulate a full day’s workload on the new system, ensuring it can handle the performance and edge cases – all before any real user is switched over. This approach minimises disruption for the end-users. Ideally, your customers shouldn’t even notice that behind the scenes, the technology was overhauled entirely; if they do notice, it should only be in the form of faster performance or new features, never lost functionality. By mitigating risk in this way, AI enables SMEs to take bold steps in pursuing modernisation, where previously a fear of failure might have led to paralysis.
Conclusion: From Technical Debt to Technical Advantage
AI is revolutionising the landscape for SMEs burdened by legacy systems. What used to be a painful dilemma – stick with the status quo and slowly decline or attempt a risky overhaul – is now more of an opportunity. With AI, technical debt can be transformed from a hidden liability into a strategic advantage[48][49]. The journey isn’t easy, but it’s faster and safer than it’s ever been. An SME can leverage AI to capture all the implicit knowledge in their legacy products, distil it into a form the next generation of engineers can work with, and then systematically modernise piece by piece without blowing the whole operation on a single bet.
It’s important to note that AI doesn’t remove the need for human expertise – rather, it augments your team. You still need experienced engineers to guide the AI, review its outputs, and make architectural decisions. But AI can handle the heavy lifting of code conversion, documentation, and testing at a scale and speed that humans alone could never match. This hybrid approach is key to success. One successful strategy observed in the industry is to use AI to draft an intermediate specification from the legacy system, have human experts validate and refine that specification, and then use AI again to generate new code or designs based on the approved specification. Intellectual ownership remains with your company (you’re not outsourcing the core knowledge), and AI serves as an accelerator to bridge skill and manpower gaps. In effect, AI can democratize legacy modernisation – you don’t need a team of rare COBOL gurus if you have AI tools that a competent modern developer can leverage to interpret COBOL for them.
We explored three major pillars (understanding, modernisation, validation) where AI contributes, but there are undoubtedly other emerging techniques. For instance, AI can optimise project planning by analysing your legacy portfolio and predicting which components are the best candidates for modernisation (providing an effort vs. impact heatmap[50][51]). It can also assist in training by turning legacy code intricacies into learning modules for new hires. And in some cases, AI might even enable entirely new business models – for example, transforming a legacy software application that ran on local servers into a cloud-based SaaS offering by automatically wrapping it in APIs and adding AI-driven analytics on top.
For SMEs in manufacturing, scientific instrumentation, or any technology-heavy field, the message is clear: you no longer have to be afraid of your legacy. The longer you let legacy debt fester, the more it “lingers in ambiguity” and drags down your velocity[52]. Now is the time to tackle it head-on using the best tools available. Start with a pilot project – maybe use an AI code assistant to refactor one troublesome module or try an AI-driven reverse engineering on one old piece of hardware. Learn from that experience and scale up. Build internal capability around these AI tools; train your team (perhaps establish a Centre of Excellence for AI in development, as some have done[53][54]) so that AI isn’t a one-off experiment but a core part of your engineering culture.
Modernising legacy systems is undoubtedly still a complex undertaking, but with AI, it’s no longer an impossible one. By combining your team’s hard-earned domain knowledge with AI’s speed and pattern recognition, you can overcome the weight of legacy designs and even turn them into a foundation for future innovation. In a way, AI lets you carry forward the best of your past (all the proven logic and quality that is embedded in your legacy products) while shedding the inefficiencies that accumulated around it. SMEs that adopt this approach will likely sleep a lot easier at night – and position themselves as agile, tech-savvy leaders in their respective markets. The path to growth is no longer blocked by decades-old code or hardware; with AI’s help, that path can be cleared, paved, and illuminated for the road ahead.
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