Why AI Won’t Replace Low-Code (And Why That’s Good News)

There’s a growing belief that AI will make low-code platforms redundant. With AI code assistants, agents and “vibe-coding” tools promising to build software from a simple prompt, it’s easy to assume platforms are no longer needed.

Gartner’s latest research tells a very different story.

AI is changing how software is built, but low-code platforms are becoming more important, not less. The future is not AI or low-code. It’s AI working inside low-code.

Here’s what that really means in practice.

AI is great at generating code. That’s also the problem.

AI tools are incredibly good at producing working software quickly. Ask for a feature, get logic, screens and flows in seconds.

But speed creates a new challenge.

As AI-generated code scales, teams start to see:

• inconsistent patterns and duplicated logic

• bloated codebases

• unclear ownership and explainability

• growing security and compliance risks

AI tends to generate more code, not less. And more code means more surface area to maintain, secure and evolve over time.

This is where low-code still plays a critical role.

Low-code isn’t about typing less. It’s about engineering better.

One of Gartner’s most important distinctions is this:

Vibe coding accelerates typing.
Low-code accelerates engineering.

Low-code platforms are designed to:

• reduce the total amount of code that exists

• enforce architectural standards by default

• promote reuse through visual models and components

• abstract boilerplate and plumbing away from teams

• limit technical debt over the life of a system

AI can help you build faster.
Low-code helps you build something that still works properly in two, five or ten years.

They solve different problems.

AI-only development introduces real risk

Used without guardrails, generative AI introduces risks that most teams underestimate:

• Security: AI-generated logic can be opaque and hard to audit

• Compliance: explainability matters, especially in regulated industries

• Maintainability: debugging AI-generated code at scale is painful

• Technical debt: fast starts often lead to messy futures

Low-code platforms already address many of these issues through built-in governance: access controls, audit logs, validation rules and controlled extensibility.

When AI operates inside that environment, the risk profile changes completely.

The winning approach: AI + low-code, on purpose

The most effective teams are not choosing between AI and low-code. They are deliberately combining them.

The pattern looks like this:

• Use low-code platforms as the default foundation for enterprise-grade and customer-facing systems

• Limit vibe coding to scoped, supervised use cases, such as prototyping or non-critical internal tools

• Enable platform-native AI assistants so generation happens within a governed environment

• Let AI handle repetitive setup, while people focus on architecture, integrations and edge cases

Modern low-code platforms like OutSystems and Microsoft Power Apps already follow this model, using AI to generate data models, screens and logic that teams then refine visually.

From “prompt to deploy”, not “prompt to chaos”

A practical delivery model is emerging:

1. Start with a natural-language prompt to generate an app scaffold

2. Refine it visually in the low-code environment

3. Hand over to experienced developers for performance, security and scaling

This widens who can contribute, speeds up delivery and keeps quality high. Business users get involved earlier, engineers focus on high-value work, and teams avoid the chaos of unmanaged AI output.

AI agents make this even more powerful

Beyond single prompts, AI agents are now being used to orchestrate multi-step workflows: generating components, wiring integrations, producing dashboards or automating repetitive build tasks.

When those agents connect into low-code platforms rather than bypassing them, teams get:

• consistency

• reuse

• traceability

• far lower long-term risk

Speed with structure always beats speed alone.

The bottom line

AI isn’t replacing low-code. It’s making it better.

Low-code platforms are evolving into the governed backbone for AI-assisted delivery, giving teams the freedom to move fast without losing control. Gartner’s view is clear: AI-assisted development will become standard. The teams that succeed will be the ones that pair AI with the right foundations.

At Doddle, this matches exactly what we see on real projects.

AI for acceleration.
Low-code for longevity.

If you’re figuring out how to combine the two without creating future headaches, that’s the problem space we love working in.