TL;DR: GrowthBook 5.0 ships with an AI Visual Editor that lets anyone build and launch a live experiment on their website from a plain-language prompt, no engineering ticket required. 

Growth and marketing teams rarely suffer from a shortage of ideas. 

There is always another headline to test, another landing page to improve, another audience that may respond to different messaging, or another campaign that could convert more effectively.

The problem is getting those ideas into production. The reality for many marketing and growth teams is that testing capacity is capped by engineering bandwidth. 

The GrowthBook AI Visual Editor unlocks this entire process. For the first time, anyone can vibe code a new home page or product page and deploy a rigorous experiment in minutes without engineering expertise. The AI Visual Editor allows users to move sections, change text and colors, and even swap out images with just a series of prompts. Once a new variation is built, these same users can kick off rigorous experiments using the same metrics and templates created by your data science team.  So anyone can run experiments you can trust.

See the visual editor in action here:

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Run experiments without waiting for engineering

The most immediate benefit of a visual experimentation system is straightforward: growth and marketing teams can move from an idea to a live experiment without waiting for an engineer to implement every variation.

Using GrowthBook’s AI Visual Editor, teams can describe a change in plain English or make it directly through a WYSIWYG interface. They can update text, modify styles, rearrange content, replace imagery, hide elements, or create more substantial page variations.

The editor then turns those changes into an experiment that can be previewed, reviewed, and launched through GrowthBook.

This removes a significant source of friction from the experimentation process.

Engineering teams no longer need to spend time implementing every headline test, campaign-specific landing page, image variation, or call-to-action adjustment. Growth teams no longer need to wait for an open sprint before learning whether an idea works.

And GrowthBook’s AI Visual Editor keeps engineering teams happy. The AI Visual Editor, like all of GrowthBook, is built on transparency so the technical teams can still review exactly what the experiment is doing, audit the results, look at the metrics, etc. Visual experiments also run on the same SDK used for other parts of GrowthBook, and even be flicker-free. 

The difference is that engineering is no longer required for every step of every experiment.

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What you can do with the AI Visual Editor

Many visual editors fall short and break on modern sites, and quietly push you back into writing CSS or HTML. We fully rebuilt our new AI Visual Editor from scratch to fix these issues. 

The AI Visual Editor lets anyone describe the change they want in plain language and get a working variation without writing code. Use manual mode or enter a prompt to do things like:

• Generate images with AI: Use AI to generate and modify hero images, product photos, background visuals, and more.
  

• Change headlines and copy: Manually update copy to test different messaging.
• Generate new copy ideas: Prompt the AI to write different headlines, CTAs, or copy variations to test.
• Run multi-arm bandits: Use the AI Visual Editor to make changes to your site and run it as a multi-arm bandit to dynamically allocate traffic to the highest performing variant.
• Update designs and layouts: Adjust fonts, padding, button styles, restructure layouts, and more.

• Import Figma frames or mockups: Bring in a design straight from Figma to test actual user interaction without rebuilding it from scratch, not just as a static image.

• ‍Import image files: Pull in your own images or brand assets.

The variation is built directly in the editor so you can see exactly how all your changes will look to your end users.

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Test more ideas and learn faster

Reducing implementation work changes more than test speed. It changes which ideas are worth testing at all.

When every experiment requires engineering time, teams naturally reserve experimentation for larger ideas. Smaller questions remain unanswered because the expected value of the result does not justify the cost of implementation. A visual editor lowers the incremental cost of answering them, resulting in teams running more experiments.

The real advantage is that teams can explore more ideas, test smaller assumptions, iterate on promising concepts, and build a clearer understanding of what customers respond to.

Instead of spending weeks debating which message should become the new default, teams can put several credible alternatives in front of real users and measure the result. You can even run a multi-arm bandit directly from the AI Visual Editor. The new workflow becomes:

1. Identify an opportunity.
2. Create a high-quality variation.
3. Launch it safely.
4. Measure its effect.
5. Use the result to inform the next decision.

The faster that loop becomes, the faster a team can improve.

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Personalize messaging for different audiences

Most websites present a single version of the company to every visitor.

But not every visitor arrives with the same problem, the same level of familiarity, or the same reason for evaluating the product.

Someone arriving from an AI-focused campaign may care about evaluating nondeterministic product experiences. An enterprise buyer may care more about security, governance, and deployment flexibility. A developer may care about SDK performance and implementation details. A marketing leader may care about conversion rates and how quickly their team can launch tests.

Sending all of these visitors to the same generic page often means presenting each of them with a diluted version of the message they actually need.

Client-side experimentation gives growth and marketing teams a practical way to test more relevant experiences for different audiences. The objective is not personalization for its own sake. Every additional experience creates complexity, and not every audience needs its own version of a website.

The value comes from being able to test whether a more relevant message actually improves the outcome.

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Validate ideas before investing in permanent development

Some ideas require substantial engineering work to implement correctly.

A redesigned pricing page may need new components. A different onboarding flow may require backend changes. A personalized experience may eventually need to be integrated deeply into the application.

But teams do not always need to build the complete version before learning whether the underlying idea has value.

A visual experiment can serve as a lightweight production prototype.

A growth team can create a realistic variation, expose it to a controlled audience, and measure whether it changes customer behavior. If the experiment performs poorly, the company avoids investing in a larger implementation. If it performs well, the team has evidence that can justify and guide the permanent build.

Not every experiment can or should be implemented through a visual editor. Changes involving application logic, backend systems, authentication, pricing calculations, or complex product behavior will still require engineering.

But even in those cases, a client-side test may help validate the customer-facing premise before the company commits to the full investment.

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Turn experimentation into a fun part of your work

Growth and marketing teams should not need to choose between moving quickly and running rigorous experiments. But more than that, GrowthBook’s AI Visual Editor allows your team to express their creativity while measuring quantifiable results. 

Because the goal is not simply to change the website faster.

It is to learn what works, and have fun while doing it. The new AI Visual Editor is a Chrome extension. You can add it to your browser, open up the extension, connect to your GrowthBook account, and create experiments. Read the Visual Editor docs to see how it fits your setup.

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