AI-Supported Software Development for Legacy Systems

Old systems slow down innovation and cause high costs. With AI-supported software development, legacy applications can be documented and modernized - efficiently, with low risk and without functional loss.

In many companies, business-critical processes run on systems that have long since passed the end of their technological life cycle. They work - yes. But they tie up enormous resources, can hardly be expanded and can only be maintained with considerable effort.

What is often overlooked: The true value of these systems lies not in their technology, but in the knowledge they contain. Business logic, processes and data models have been written into them over years - often decades. This implicit knowledge makes legacy systems a risk - if it is lost.

With every expert who leaves the company, the understanding of these legacy systems decreases. In many cases, the documentation is incomplete or outdated, and new development teams are faced with code that is difficult to understand. Maintenance becomes a black box - changes become a risk.

In addition, there are rising operating costs and growing regulatory pressure. Business as usual is no longer an option for many companies in the medium term.

Instead of relying on expensive complete migrations or standard software, more and more organizations are using AI-supported software development to strategically modernize their legacy systems.

Existing source code is analyzed, documented and specifically converted into modern languages such as Java or C#. In contrast to classic automation tools, generative AI not only recognizes syntax, but also the underlying logic. This means that functional knowledge is retained - while the technological basis is updated.

The key lies in contextual understanding. Modern AI models identify patterns, abstract functions and create code proposals that are based on current architectural principles. This means that even heterogeneous legacy landscapes can be developed more efficiently - without the known weaknesses of rigid converters.

In addition, readable documentation is created in parallel, which is not only helpful for developers, but also for external service providers, compliance officers or new team members. This makes technical knowledge accessible again - and permanently secure.

AI-supported software development unfolds its potential especially when it is combined with human expertise. The modernization process is not understood as a one-off transformation, but as an iterative procedure:

• KI generates initial suggestions based on the alt code
• Technical experts review and refine the results
• Feedback flows back into the AI control system - for example through optimized prompting or data formats

This creates maintainable, testable and comprehensible target code in several loops - including accompanying documentation and test cases.

The maturity of modern AI models and the availability of tried-and-tested methods make it possible to modernize even complex legacy systems efficiently and sustainably. Companies that take this step now will benefit from:

• Better decision-making ability through regained understanding of the system
• Lower migration risks thanks to step-by-step, valid transformation paths
• sustainable knowledge building for operation, compliance and further development

Many IT decision-makers are faced with a growing number of challenges: rising operating costs, tight budgets, a shortage of skilled workers, new regulatory requirements and, at the same time, enormous pressure to innovate. In this situation, there is no need for more one-off tools - strategic empowerment is required.

AI-supported software development fulfills precisely this requirement: it not only helps to technically renew legacy systems, but also puts companies back in a position to make independent decisions about their IT infrastructure. If you understand the code, you can change it. Those who document can scale. Those who have secured knowledge can implement projects in a predictable manner - independently of individuals or service providers.

In this way, AI does not become a black box, but a translator between the past and the future. The company regains control over systems that were previously considered unchangeable.

Many companies have already had initial experience with code analysis tools or conversion tools - often with mixed results. The differences to generative AI are clear:

The most important difference lies in the understanding of context and meaning. While conventional tools "translate", AI establishes relationships, evaluates structures and prioritizes relevant components. The result is not just code, but real decision support for development and architecture.

Another key aspect: modernization is also cultural change. Especially with legacy systems, which have often grown over decades, technology meets organizational structure, historical processes - and evolved responsibility.

AI-supported software development can facilitate this change. It makes it possible to design modernization in a step-by-step, comprehensible and documented manner. This creates trust - both in the development team and at management level.

Typical effects in customer projects:

• Less resistance from IT teams because the established logic is not lost
• Faster onboarding of new employees through comprehensible documentation
• Better communication with specialist departments because processes remain transparent

This is how technical modernization becomes a catalyst for organizational renewal.

Naturally, many IT managers ask themselves the question: Is the use of generative AI worthwhile? The answer: Yes - measurably.

According to analyses by McKinsey & Co, AI-supported modernization processes can:

• Shorten project runtimes by up to 50 %
• reduce the cost of technical debt by up to 40 %
• Significantly improve the time-to-productivity of new teams

This can make a decisive difference, especially in complex, fragmented IT landscapes. In addition, there is the often not directly quantifiable value of secured know-how, regulatory protection and better predictability. (Source: McKinsey & Company - The State of AI in 2023)

Getting started with AI-supported software development does not have to be a major project. On the contrary: many companies start with a pilot application or a system section to gain experience.

Typical process

1. System selection & Target definition: Identification of a relevant but manageable legacy application
2. Analysis phase: inventory code and available information are merged
3. Initial AI processing: First proposals for modernization, documentation, tests
4. Validation & Feedback: Review by development team - optimization of AI results
5. Rollout & Transfer: Transfer to production or expansion to other systems

This iterative approach is scalable and low-risk - and can be adapted to individual IT strategies. This means that AI is not a panacea, but a tailor-made component of your modernization.