On point: AI tools are assistance instruments with transparency gaps and hallucination risks, while low-code reduces complexity through structured, auditable components — both can work in a complementary manner.
AI tools like GitHub Copilot and Cursor are present in development but function as assistance tools, not as autonomous engines. Low-code platforms offer a structured counterpoint through auditable, preconfigured components for complex enterprise applications.
Artificial intelligence in software development is used daily today — for example through GitHub Copilot, Cursor, or assistants in ERP platforms. A key differentiating point remains, however: AI acts exclusively as powerful support and not as an autonomous development engine. Its strength lies in quickly producing syntactically and structurally plausible solutions — clean, readable code that is often immediately functional. However, as soon as complex business relationships beyond simple, isolated functions are required, AI systems reach their limits.
A significant challenge when using AI-generated solutions is the lack of transparency in the solution path. AI works based on probabilities, not on genuine understanding — its reasons for an implementation remain hidden. This makes it a black box in many respects. While human developers can explain their decisions even weeks later, this information is immediately lost with AI-generated code. When debugging, you often have to start from scratch because the intention behind the solution is not documented. There is also the risk of hallucinations: AI can produce results that appear superficially valid but can be completely incorrect in substance. For example, incorrectly calculated taxes in an ERP system might only be discovered very late and cause considerable financial damage.
Companies are liable themselves for compliance requirements such as GoBD or GDPR — this responsibility cannot be delegated to AI tools. Additionally, there is a threat of skill loss: developers who only approve AI results lose the ability to identify deeper errors or perform complex transfer tasks.
Low-code platforms take a more structured approach: they reduce complexity not through automation of logic, but through ready-made, auditable components that are already optimized for business-critical processes. Unlike AI, they provide transparency and traceability through preconfigured components. This does not mean skill loss; instead, it shifts engineering work to higher professional levels. The two approaches are therefore less competitors than complementary: AI can function within low-code environments as a local assistance tool without jeopardizing structured audit capability and maintainability.
Source: www.it-daily.net · Published June 10, 2026
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