With the rise of AI, one question keeps coming up: where do low-code developers fit into this new wave? Will AI replace low-code developers?
(btw, screenshot above is from from Mentor — Generative AI App Creator)
Having transitioned from traditional coding to low-code myself (for context, I joined Outsystems 12 years ago), I see a different story. It’s not about one technology replacing another — it’s about how they complement each other to unlock new possibilities.
Understanding Low-Code Development and AI
Before diving into how these two can work together, let me break down what makes them unique.
Low-Code Development Platforms
These platforms allow enterprise companies to quickly build and adapt applications using drag-and-drop widgets and templates, requiring minimal coding. This approach speeds up development and makes it accessible to a broader range of users. There are several “types” of low-code, ranging to target from small and very specific use cases like airtable, to addressing full stack/full lifecycle software delivery (like OutSystems, Mendix, Appian, etc.)
Artificial Intelligence (AI)
AI refers to various technologies, including machine learning, natural language processing (NLP), cognitive computing and the popular generative AI (Gen AI). These technologies enable machines to perform tasks that typically require human intelligence. By automating complex processes, AI can create personalized and adaptive software that learns from data and user interactions.
The Impact of Gen AI on Low-Code Platforms
Low-code platforms are evolving to become more intelligent and adaptable, integrating seamlessly into the software development lifecycle. A significant shift will involve these platforms leveraging generative AI to understand user intent, automate repetitive tasks, and generate complex code snippets. According to Gartner, by 2026, over 80% of independent software vendors will embed GenAI capabilities in their enterprise applications, up from less than 5% today.
The combination of low-code and generative AI will increase productivity by automating routine tasks. This allows developers to focus on complex logic and creative problem-solving. In essence, GenAI acts as a productivity booster, optimizing development processes across the board. But doesn’t this sound familiar? Wasn’t this the promise of low-code itself? So, is the integration of AI with low-code an additional step in boosting productivity? What does this truly signify?
How AI Will Impact the Lives of Low-Code Developers
While AI will definitely boost productivity for low-code developers, it will also introduce new challenges and areas that require attention:
Complex Logic Still Dependent on Human Input
GenAI will automate tasks such as data modeling and scaffolding, speeding up the development process. However, while AI can quickly handle up to 80% of a project, the final 20% — which involves customizations, complex logic, and problem-solving — will still require significant human input.
“Humans” as Code Validators
Even though AI will reduce the amount of routine coding, experienced developers who understand software architecture and security will become even more valuable. AI can make mistakes, so human expertise will be crucial for validating and correcting AI-generated code. Senior developers will play a key role in ensuring that AI-generated solutions meet quality standards.
Challenges for Junior Developers
The rise of AI does pose challenges, particularly for junior developers. Some developers using tools like Copilot have expressed concerns about the long-term impact on newcomers. If AI handles most basic coding tasks, junior developers may struggle to gain the foundational skills that senior developers acquire through hands-on experience. This shift could lead to a situation where junior developers mainly learn by debugging AI-generated code, which might hinder their overall skill development.
Conclusion
I believe the future for low-code developers lies in adaptation. GenAI introduces another layer of abstraction, and as we move forward, developers will focus more on strategic design, problem-solving, and aligning AI-generated solutions with business goals. Those who can bridge the gap between business requirements and AI outputs will be essential for successful software development.
While AI offers tremendous potential, it also brings risks, especially concerning governance and over-reliance on automation. Ultimately, the role of low-code developers is far from over; it is evolving into a more specialized and strategic role. Understanding AI and its limitations will be crucial for thriving in this new digital age.
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