AI is writing code for marketers now. In HubSpot, that means spinning up HubL modules, automating tasks, and tweaking workflows — all without a developer on hand. AI tools have opened the door for non-developers to engage with coding like never before, accelerating how teams build and experiment inside the platform.
But with that acceleration comes risk — especially when users aren’t equipped to validate what AI produces. This post explores both the strengths and limitations of using AI for coding within HubSpot, particularly for marketers, admins, and operations teams. If that’s you, read on to learn where AI shines, where it falls short, and how to use it to your advantage.
Quick Starts for Common Tasks
AI tools can generate boilerplate HubL, JavaScript, or React code snippets quickly — helping users avoid starting from scratch and saving time on repetitive tasks.
Idea Generation & Problem Solving
AI can assist with creative problem-solving by suggesting new ways to approach integrations, custom modules, or data handling inside HubSpot.
Faster Documentation
AI excels at generating code comments and explanations, which can help teams maintain clearer documentation, especially in collaborative environments.
Skill Bridging
For marketers or admins who want to learn more about the technical side of HubSpot, AI can act as a tutor — translating prompts into basic code and breaking down complex logic into something more understandable.
Lack of Context for HubSpot-Specific Features
AI doesn't always understand how HubSpot structures work — like pipeline logic, custom properties, or module fields. This can lead to broken or incompatible code.
Overconfidence in Inaccurate Code
AI may generate incorrect or insecure code, and present it confidently. Without a developer's eye, mistakes can go unnoticed until they cause issues in production.
Misalignment with Business Logic
Even when technically correct, AI-generated code may not reflect the unique workflows, naming conventions, or rules specific to a business.
False Sense of Readiness
AI can encourage a “copy-paste and go” mentality. But even AI-assisted code needs rigorous QA, testing, and — ideally — review by someone with coding experience.
Real-World Example: When Working Code Isn’t the Right Code
A solutions consultant used AI to write SQL for a Hightouch sync between a client’s data warehouse and HubSpot. The code ran without errors, the client was happy, and data flowed smoothly between systems.
A few weeks later, the client was blindsided by a massive Hightouch usage bill. After digging in, a developer discovered the issue: the AI-generated SQL was technically correct, but it lacked operational safeguards. Without frequency limits in place, the query triggered continuous syncs — essentially running in a loop — and racking up unnecessary usage.
The fix was just one line of SQL to limit sync frequency. A simple tweak, but it dramatically reduced costs.
The lesson: AI can get you close, but it doesn’t know your billing model, sync triggers, or platform usage limits. Without that context, even “correct” code can create expensive problems.
AI is helping democratize access to coding tasks in HubSpot, but it’s not a magic wand. Without a solid understanding of the platform — or the ability to evaluate code quality — non-developers risk creating more problems than they solve.
Used responsibly, AI can be an incredible accelerator. But it should always be paired with thoughtful review, testing, and the right level of technical support.
The future of coding in the HubSpot ecosystem won’t be just AI or just developers — it’ll be smart collaboration between the two to bridge the gap.