AI as a driver for software modernisation - AI makes every step more efficient

Software modernisation is always a topical issue, as all technologies have an expiry date. Updating outdated systems is an unavoidable expense, but it is also a strategic opportunity to improve efficiency and competitiveness. One of the key questions is whether to replace the system with a new one or to upgrade the old one. As business logic has evolved over the years to meet different customer needs and industry developments, building a completely new system is both costly and slow. In the case of modernisation, the situation has never been simpler. This is where AI comes in. It allows modernisation to be faster, more accurate and more cost-effective.

In this blog, we look at how AI can be used at different stages of software modernisation.

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Taking over an old system - AI boosts initial analysis

Modernisation projects often start with a status quo: what the system actually does, what it's all about and how well documented it is. In legacy systems, information may be scattered or only behind certain key people.

AI can already be used at this stage, for example in the following ways:

Analysis of code structure and repetitions
Automation of documentation creation
Structuring user interfaces and operational logic

This helps to quickly build up an overview of the system and to prioritise development work correctly.

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Code conversion - AI speeds up the rewriting of repetitive structures

One of the most effective ways to use AI is to convert massive code bases containing tens of thousands of lines of code. The AI-assisted approach enables fast and consistent translation.

AI is particularly well suited:

To rewrite a repetitive structure without errors
For preliminary sketching of long components
Automating monotonous and time-consuming work

In this way, AI frees up expert time for more challenging tasks, such as designing the architecture and refactoring.

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Streamlining the development process - AI supports documentation, testing and quality assurance

The role of AI is not limited to just writing code. It can support the entire software lifecycle:

Documentation: AI can automatically generate and maintain technical documentation as part of the development process, allowing developers to focus more on the content and functionality of the documentation.
Quality assurance: AI can be used to identify potential vulnerabilities in the code and suggest improvements at an early stage.
Testing: AI can be used to generate test cases and help build integration and unit tests. The developer can then focus more on designing test coverage. Comprehensive integration tests also provide a good basis for refactoring the system in a safe and iterative way.

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Summary: AI in software modernisation

Properly managed, AI offers a number of accelerating and even enabling benefits for system modernisation.

Faster progress: automatic code conversion and analysis significantly reduces project time.
Fewer mistakes: AI processes repetitive structures quickly and consistently, while preventing human error.
More complete and up-to-date documentation: AI can maintain and complete documentation that may be partially or completely missed in a hurry. AI also speeds up the process of updating documentation, as it is easy for a developer to spend a lot of time on detailed refinement.
More flexible development: AI speeds up testing and quality assurance, allowing faster iteration, which in Finnish means that, again, more can be achieved in less time.
Cost-efficiency: less manual work and a better focus on expert work.

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The role of AI at different stages of software modernisation

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Among other things, we have modernised the financial industry's accounting applications. The modernisation has been done in an AI-hosted way, so that the technical modernisation from a programming language to the most modern programming language and the architecture upgrade have been implemented using AI tools. Fluentia's AI toolkit significantly helped to manage the risk of modernisation when upgrading business-critical computing applications. Compared to a more traditional modernization project, the project was able to avoid manual coding errors and expand the amount of automation testing for quality assurance.

Contact us to see how AI-powered software modernisation could strengthen your business!

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