Don’t underestimate low-code in an AI world

Estimated reading time: 4 minutes

Generative AI has the potential to automate 70% of the work done by software engineers. However, the remaining 30% of the development lifecycle is critical.

It requires aligning software with organizational best practices, security standards, and long-term maintainability. This gap raises a pivotal question for engineering leaders: Can AI-generated code truly replace human engineering expertise, or does it introduce new maintenance burdens that slow us down?

The reality is that while AI can churn out code at record speed, it often lacks context. Without oversight, AI-generated code may diverge from your team’s quality standards and create hidden technical debt. Engineering leaders report that speeding up coding alone doesn’t guarantee faster shipping or sustainable systems. More code, in fact, can mean more problems if technical debt isn’t kept in check​

Low-Code + AI = Fast and governed development 

Low-code platforms have long offered a standardized, visual development environment that can extend the capabilities of AI coding assistants. Instead of dropping raw, unstructured AI outputs into your stack, low-code tools can integrate AI-driven outputs into a manageable framework. 

Engineers can quickly refine and iterate on AI-created prototypes using drag-and-drop components and pre-built blocks, ensuring maintainable results that adhere to architectural standards. In practice, this means teams can confidently build robust, scalable internal applications without getting bogged down by fragile, spaghetti-code prototypes. 

A low-code platform can provide a structured layer of quality control with standardized, well-maintained components for UI, logic, and automation. AI builds on top of this to help developers quickly assemble functional apps from natural language prompts, making development dramatically faster.

AI in software development: Speed vs. maintainability

AI-powered development tools excel at automating repetitive coding tasks and can take a project from idea to prototype faster than ever. Teams leveraging tools like coding assistants or generative models have seen immediate boosts in output. But speed isn’t everything in engineering. AI-generated code is not always production-ready – it often isn’t optimized for maintainability, security, or scalability out of the box.

This presents an important trade-off: rapid development vs. long-term maintainability. AI can rapidly generate application logic and even entire features, but it frequently produces code in a vacuum, lacking alignment with an organization’s conventions and tech stack. Without human intervention, AI-written code can introduce inconsistencies or vulnerabilities that become costly to fix later. 

Engineering leaders must still apply human oversight to validate and refine AI outputs. Code reviews, architecture alignment, and rigorous testing remain non-negotiable. 

How low-code platforms complement AI’s capabilities

Low-code platforms act as the governance layer for AI-driven development. Rather than having AI generate swathes of code that developers must untangle, low-code tools channel AI’s output into a structured environment. This has several concrete advantages:

• Built-in best practices: Low-code platforms enforce standardized components, consistent layouts, and proven action blocks. Any UI or logic that AI helps produce is immediately fitted into well-structured, legible patterns. This keeps the codebase clean and in line with engineering best practices.
• Intelligent automation and human control: Many modern low-code tools come with intelligent AI agents that can automate steps in the app-building process (from form design to database integration), but always within guardrails set by the platform. Developers still maintain control – they can adjust, override, or extend the AI’s suggestions using visual editors or custom code when needed.
• Pre-built components: Instead of reinventing the wheel, engineers using low-code have libraries of pre-built, enterprise-ready components at their disposal. AI can assemble these like Lego blocks to draft an application, and developers can then fine-tune the assembly. This dramatically reduces manual coding for boilerplate functionality while ensuring the output is secure and scalable by default.
• Agentic, structured approach: In a well-integrated setup, the AI does the heavy lifting under the hood, but the low-code platform provides an agentic (assistive) approach. Engineers get to work at a higher level of abstraction – reviewing business logic and user experience – rather than wrestling with low-level code quality issues. The result is software that’s built faster but also remains enterprise-ready and maintainable.

By leveraging low-code alongside AI, teams ensure their quickly-generated applications aren’t brittle prototypes but sound, extensible systems. In short, low-code turns rapid AI prototyping into real, production-grade software. But the real advantage isn’t just in what low-code and AI can do, it’s in how engineering leaders choose to apply it.

---

London • June 16 & 17, 2025

Speakers Gergely Orosz, Camille Fournier and Lara Hogan confirmed.

See who else is speaking

What engineering leaders should focus on

AI is moving fast. Low-code gives that speed structure. But what does this mean for engineering leaders making real-world decisions? Start where engineers are blocked, not where AI is trending. If your teams are drowning in boilerplate code, building one-off internal tools, or stuck waiting on frontend bandwidth, that’s where low-code combined with AI assistance can deliver immediate leverage. 

Stop asking “can this replace a developer?” and start asking “what can this unblock?”

The best teams don’t shrink their engineering orgs. They redesign how work flows. Low-code lets senior engineers focus on architectural thinking while offloading repeatable logic, UI scaffolding, or integration plumbing.

Build a culture of abstraction, not shortcuts. Low-code isn’t about skipping steps. It’s about leveling up: operating with reusable, governed components and bringing AI into a system that supports long-term maintainability.

AI-generated code vs. AI-powered low-code

It’s worth explicitly highlighting the difference between using general AI coding tools and adopting an AI-powered low-code platform. Both approaches involve AI in software development, but their outcomes for your team can be vastly different. The table below provides a concise side-by-side comparison:

Final thoughts

General AI coding tools are great for boosting individual developer efficiency on a task-by-task basis, but AI-powered low-code platforms accelerate the entire software delivery process. The latter not only speeds up coding, but also handles the scaffolding, enforcement of standards, and deployment-ready structure that are often missing.