• Automating Code Generation One of the most exciting uses of AI in low-code platforms is the ability to automate aspects of code generation. By utilizing AI models trained on massive datasets, low-code platforms can help developers generate code snippets, suggest improvements, or even write entire blocks of code based on high-level user inputs.

For example, platforms like OutSystems and Mendix are integrating AI to help developers generate complex workflows, database structures, and even UI components with minimal effort. This significantly speeds up the development process and lowers the barrier to entry for less experienced developers.

Implication: AI-powered code generation allows developers to work faster and more efficiently, reducing the manual effort required and lowering the potential for human error. For organizations, this means delivering products faster and at a lower cost.  

• Enhancing Drag-and-Drop Functionality Low-code platforms are designed to simplify the development process, but integrating AI into the drag-and-drop functionality takes this to the next level. AI can now suggest components, layouts, or even entire page structures based on the type of app being built or the developer’s past behaviour.

For example, if a developer is building a CRM app, the AI may recommend specific widgets (like contact forms or sales pipelines) that have been commonly used in similar projects.

Implication: AI-driven suggestions make the development process even more intuitive, especially for “citizen developers” with minimal coding experience. It helps streamline the process of building complex applications without the need for deep technical expertise.  

• Intelligent Testing and Quality Assurance Quality assurance (QA) is an essential step in the development process, but it can also be time-consuming and tedious. AI can help automate the testing of low-code applications, identifying bugs, performance issues, and even security vulnerabilities more quickly and accurately than traditional manual methods.

For example, AI tools can predict where bugs are likely to occur based on patterns in the code, or they can automatically run thousands of tests to ensure that the app performs optimally under various conditions.

Implication : AI in QA reduces testing time and increases the reliability of the final product. It also ensures that apps built using low-code platforms meet the highest quality standards without requiring extensive human intervention.    

• Dynamic Workflow Automation AI can also help automate workflows within low-code platforms, allowing businesses to design applications that adjust based on user interactions or external data inputs.
  
  For example, AI can detect patterns in customer data and adjust workflows automatically to optimize customer engagement or internal processes.

Implication: AI-powered workflow automation makes low-code apps more intelligent and responsive. Businesses can create highly dynamic applications that adjust to changing business needs without having to write custom code for every scenario.