AI in Software Development:
Balancing the Advantages and Risks

Artificial Intelligence (AI) is rapidly transforming the software development landscape – and low-code platforms like Mendix, OutSystems, and PowerApps, are at the forefront of this shift. By using Mendix AI Assist (MAIA), CoPilot, etc. to generate data models, workflows, and even entire applications, organizations can unlock new levels of speed and efficiency. However, the use of AI in software development is not without its risks.

This article outlines the key benefits and potential pitfalls of using AI in software environments, offering a balanced perspective for teams embracing this technology.

Advantages of AI in Software Development

1. Accelerated Development – Based on natural language inputs, AI can now generate data models, user interfaces, workflows, logic, and code snippets. This can reduce development time and allows teams to prototype and iterate faster than ever before.

2. Increased Productivity – Developers can offload routine or repetitive tasks (like CRUD generation, validations, or simple automation) to AI, allowing them to focus on more complex business logic and user experience.

3. Guided Development – AI empowers non-developers – such as business analysts or domain experts – to contribute directly to app creation, without deep technical knowledge. This democratizes development and bridges skill gaps.

4. Consistency and Standardization – AI can enforce naming conventions, UI patterns, and logic structures across applications, helping maintain coherence in large or distributed teams.

5. Support for Testing and Documentation – Advanced tools can generate test scenarios, flow descriptions, or inline documentation automatically, improving maintainability and knowledge transfer.

Risks of AI in Software Development

1. Functional Misalignment – AI may generate components that superficially meet requirements but fail to reflect business rules or user needs accurately – especially if requirements are vague.

2. Traceability Gaps – It can be difficult to trace generated components back to user stories or requirements, making audits, testing, and change management more difficult.

3. Security and Compliance Issues – AI-generated logic may overlook key access controls, data validation, or regulatory constraints, such as GDPR, or use outdated best practices, leading to vulnerabilities or legal non-compliance.

4. Maintainability Challenges – Generated logic may be inconsistent, overly complex, or poorly named, especially when used without governance. This increases technical debt and onboarding time.

5. Over-Reliance and Skill Degradation – Developers and citizen users may trust AI-generated code without review, weakening critical thinking and introducing undetected flaws into production systems. Over time, this may even reduce the team’s ability to troubleshoot or innovate without using AI.

6. Intellectual Property Concerns – AI may generate content based on patterns learned from copyrighted material or platform-specific conventions, raising legal and vendor lock-in concerns.

7. Accountability Issues – If a bug causes damage, who is responsible: the developer, the AI provider, the owner of the data set with which the AI was trained, or …?

Conclusion: Use AI as an accelerator, not as an autopilot

AI is a powerful accelerator for software development, but it must be used responsibly. AI can be useful to boost productivity and innovation, but it is not a substitute for human oversight, review, and traceability.

Establishing governance policies, traceability frameworks, and validation checkpoints ensures AI-generated components align with business goals, comply with regulations, and remain maintainable over time.