Our subject matter experts created this article to provide guidance on leveraging AI as an intelligent assistant, fostering trust through transparency, and ensuring sustainable modernisation outcomes. We will explore the transformative role of Artificial Intelligence in modernising legacy software systems. We will also examine the challenges posed by ageing systems built on outdated technologies, the opportunities AI presents to enhance understanding and transformation, and strategies for successful AI integration.

By addressing the practical application of AI in legacy code analysis, documentation generation, and incremental modernisation, the article provides actionable insights for organisations aiming to preserve critical domain knowledge while embracing modern software practices.

In software modernisation, Artificial Intelligence is a transformative force that, when applied strategically, can address the unique challenges faced by companies attempting to update their legacy systems. This is particularly true for manufacturing organisations dealing with systems that were often developed decades ago by domain experts who, while excellent in their field knowledge, weren't primarily software engineers, frequently resulting in legacy programming languages and outdated data storage systems that are difficult to integrate into contemporary architecture.

Understanding the Modernisation Challenge

Many organisations operate with systems that have evolved over several years, accumulating layers of business logic and operational knowledge. These systems, often developed by technology experts rather than software engineers, contain invaluable domain expertise embedded within dated technological frameworks.

The challenge isn't simply about updating technology — it's about preserving and enhancing this embedded knowledge while bringing the systems into the modern era.

The Role of AI in Software Modernisation

When we talk about AI in software modernisation, we're not discussing a single technology but rather a spectrum of capabilities that can be strategically deployed throughout the modernisation lifecycle. Let's explore how different types of AI can meaningfully contribute to the modernisation process.

1. Large Language Models in Code and Documentation Understanding

Large Language Models (LLMs) have revolutionised our ability to understand and work with legacy code. These models, trained on vast repositories of code and technical documentation, can serve as intelligent assistants in the critical phase of any modernisation project: code understanding, documentation analysis, requirements engineering, and knowledge synthesis.

Modern LLMs can analyse legacy systems to:

• Generate comprehensive documentation of existing functionality;

• Extract implicit requirements from legacy code and documentation;

• Identify and explain complex business rules embedded in the code;

• Detect potential issues and technical debt.

The key advantage here is the ability to bridge the knowledge gap between legacy systems and modern development practices. For instance, when analysing a legacy application-specific module, an LLM can not only document the existing business rules but also suggest how these same rules could be implemented using modern design patterns and architectures.

2. AI-Powered Code Transformation

The transformation spans multiple critical aspects of software development, including architectural patterns, system design, architectural patterns, code structure and data models. The actual transformation of legacy code into modern equivalents can be significantly enhanced through Artificial Intelligence. Modern AI tools can:

• Suggest modern architectural patterns that match existing functionality;

• Automatically refactor code while preserving business logic;

• Analyse existing database schemas and suggest normalisation improvements;

• Generate test cases to ensure behavioural equivalence;

• Create APIs that wrap legacy functionality during incremental modernisation.

While AI provides powerful transformation capabilities, successful modernisation remains a nuanced process:

• AI tools are intelligent assistants, not autonomous transformation engines;

• Human expertise is critical for validation, context understanding, and strategic decision-making;

• Transformations should be incremental, with continuous human oversight;

• Each transformation requires careful evaluation of the business context and technical constraints.

Implementation Strategy: Beyond the Buzzwords

The successful implementation of AI in modernisation projects requires a thoughtful, phased approach that builds confidence and demonstrates value incrementally. This is particularly important given the common resistance from both management and maintenance teams in manufacturing environments.

It's best to start with small, contained modules where AI can demonstrate clear value. For instance, begin with automated documentation generation and code analysis for a single system component. This allows stakeholders to see concrete benefits while limiting risk. As confidence gradually builds, we can expand the use of AI tools to more complex tasks and larger system components.

Building Trust Through Transparency

One key aspect of successful AI implementation is maintaining transparency in how AI tools make their decisions. When an AI system suggests a code transformation or identifies a potential issue, it should be able to explain its reasoning in terms that both developers and business stakeholders can understand. This transparency helps build trust and makes it easier for teams to validate and refine AI-generated suggestions.

Knowledge Transfer and Team Empowerment

Perhaps one of the most valuable aspects of AI in modernisation is its ability to facilitate knowledge transfer. AI tools can serve as a bridge between old and new technologies, helping maintenance teams understand how their existing knowledge applies to modern systems. This addresses one of the key concerns of maintenance teams, the fear of becoming obsolete, by showing how their domain knowledge remains valuable in the modernised system.

Measuring Success and Ensuring Value

The success of AI integration in modernisation projects should be measured not just in terms of code converted or documentation generated but in terms of meaningful business outcomes. This includes:

• Reduced time to understand and modify existing functionality;

• Improved system reliability and maintainability;

• Faster development cycles for new features;

• Better preservation and utilisation of domain knowledge;

• Increased confidence among maintenance teams in working with the modernised system.

Looking to the Future

As AI technologies continue to evolve, their role in software modernisation will only grow more significant. However, the key to success will remain the same: focusing on meaningful applications that add real value rather than implementing AI for its own sake. The future of manufacturing software modernisation lies in strategically combining AI capabilities with human expertise, creating systems that are not just technically advanced but also maintainable, reliable, and aligned with business needs.

The integration of AI in manufacturing software modernisation represents a powerful opportunity to address long-standing challenges in legacy system transformation. By taking a thoughtful, value-focused approach to AI implementation, organisations can modernise their systems while preserving crucial domain knowledge and bringing their maintenance teams along on the journey.

The key lies not in wholesale automation but in using AI as an intelligent assistant that amplifies human capabilities and facilitates knowledge transfer from old systems to new ones.

Remember that successful modernisation is a journey, not a destination. By starting small, demonstrating value, and gradually expanding the role of AI tools, organisations can transform their legacy systems while building confidence and capability within their teams. The result is not just modernised software but a more capable and confident organisation ready to face future technological challenges.

Our strong tech teams are ready to help your organisation transform their legacy systems sustainably and efficiently. Reach out and tell us about your project.