Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Six months ago, I made a decision that surprised my clients: I deliberately avoided AI for two years while everyone else was rushing to ChatGPT. Not because I was against technology, but because I've seen enough hype cycles to know that the best insights come after the dust settles.
When I finally dove into AI, I approached it like a scientist, not a fanboy. The first thing I discovered about Bubble AI plugins? Most of them are complete garbage.
Here's what nobody tells you about AI plugins for Bubble: the question "which plugins are best" is asking the wrong thing entirely. After testing dozens of integrations and building multiple AI-powered applications, I learned that success has nothing to do with finding the "best" plugins and everything to do with understanding what AI can actually deliver for your specific business case.
The uncomfortable truth? AI is a pattern machine, not magic. Most founders are treating it like a magic 8-ball when they should be treating it as digital labor that can DO tasks at scale.
In this playbook, you'll learn:
Why most Bubble AI plugins fail in production (and the 3 that actually work)
My framework for choosing AI integrations based on business value, not features
How I use AI to generate revenue, not just automate tasks
The specific workflow I use to evaluate any AI plugin in under 2 hours
Real examples from SaaS and ecommerce projects
Stop collecting AI plugins like Pokemon cards. Start building applications that solve real problems and generate actual revenue.
Plugin Reality
What the Bubble community recommends about AI integrations
Browse any Bubble forum or no-code community, and you'll see the same recommendations about AI plugins repeated over and over:
"Just try everything! Install every AI plugin available!" They'll give you lists of 20+ plugins for different AI services - OpenAI, Claude, Replicate, Hugging Face, you name it.
The popular approach is:
Browse the Bubble plugin store for AI-related plugins
Install plugins for every major AI service (OpenAI, Anthropic, etc.)
Test different models to see which gives "better" responses
Build features around what the AI can do
Add AI to everything because it's trendy
This approach exists because the no-code community treats AI like a shiny new toy rather than a business tool. Everyone's excited about the possibilities, so they recommend exploring everything.
The problem? This plugin-collecting mindset leads to feature bloat, confused users, and products that don't solve real problems. You end up with an app that can do 50 different AI things but generates zero revenue because none of them matter to your users.
It's the same mistake I see with founders who build marketplaces - they get so excited about the technology that they forget to validate whether anyone actually wants what they're building.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about a client project that completely changed how I think about AI plugins in Bubble. A potential client approached me with excitement about building a "comprehensive AI platform" using every available plugin. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects.
I said no.
Here's why: they came to me excited about the no-code revolution and AI tools, having heard these tools could build anything quickly. They weren't wrong - technically, you can integrate multiple AI services into Bubble. But their core statement revealed the problem: "We want to see if our AI idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm for AI technology.
Instead of building their comprehensive AI platform, I spent 6 months doing my own AI experiments. I approached it systematically - not as a fanboy, but as someone trying to understand what actually works in business contexts.
Here's what I discovered through hands-on testing across multiple use cases:
Test 1: Content Generation at Scale I generated 20,000 SEO articles across 4 languages using AI workflows. The insight? AI excels at bulk content creation when you provide clear templates and examples, but each piece needed a human-crafted example first.
Test 2: Business Process Analysis I fed AI my entire website's performance data to identify patterns. The breakthrough: AI spotted optimization opportunities in my strategy that I'd missed after months of manual analysis, but it couldn't create the strategy - only analyze what already existed.
Test 3: Client Automation Workflows I built AI systems to update project documents and maintain client workflows. The reality: AI works brilliantly for repetitive, text-based administrative tasks, but anything requiring visual creativity or truly novel thinking still needs human input.
The pattern became clear: AI's value isn't in the plugins you choose, it's in how you apply computing power as digital labor force.
Here's my playbook
What I ended up doing and the results.
After months of systematic testing, I developed a framework that has nothing to do with comparing plugin features and everything to do with business impact. Here's my exact process for evaluating and implementing AI in Bubble applications:
Step 1: Define the Business Task (Not the AI Feature)
Before I even look at plugins, I identify exactly what business problem needs solving. For example:
"Generate product descriptions at scale" (not "use GPT-4 for content")
"Qualify leads automatically" (not "build a chatbot")
"Analyze customer feedback patterns" (not "implement sentiment analysis")
Step 2: The 2-Hour Plugin Evaluation
I only test plugins that can demonstrate business value within 2 hours. My criteria:
Can it handle the specific input/output format I need?
Does it work reliably with Bubble's workflow system?
What happens when it fails? (because it will)
Can I measure the business impact within a week?
Step 3: The Three AI Plugins That Actually Work
After extensive testing, only three types of AI plugins consistently deliver business value in Bubble:
OpenAI API Plugin (for text generation): Reliable for content creation, customer service responses, and data analysis. The key is prompt engineering specific to your use case, not trying to build a generic AI assistant.
Replicate Plugin (for image processing): Excellent for automated image optimization, background removal, and visual content creation. Works particularly well for ecommerce applications.
Custom API Connector (for specialized AI services): Most valuable for connecting to specific AI services like legal document processing or industry-specific analysis tools.
Step 4: Implementation for Scale, Not Features
I implement AI workflows that can handle volume, not just demonstrate capability. For a recent ecommerce project, I built an AI system that:
Generates product descriptions for 3000+ products automatically
Updates meta tags based on performance data
Categories new products using existing taxonomy
The entire system runs without human intervention and has processed over 10,000 products across multiple languages.
Step 5: Revenue Measurement
Every AI implementation gets measured on business metrics, not AI metrics. I track revenue impact, time saved, and user behavior changes - never "accuracy scores" or "model performance."
Plugin Selection
Focus on business outcomes over technical features
Testing Framework
2-hour evaluation process for any AI integration
Scale Implementation
Build for volume processing not feature demos
Revenue Tracking
Measure business impact not AI performance metrics
The results from this focused approach have been transformative across multiple client projects:
E-commerce Automation: One Shopify client saw their content production increase by 10x while reducing content creation costs by 80%. The AI system now handles product descriptions, meta tags, and category assignments for their entire 3000+ product catalog.
SaaS Content Generation: A B2B startup reduced their content creation timeline from weeks to hours, allowing them to scale their SEO strategy from 50 pages to over 5,000 targeted landing pages.
Customer Service Automation: By focusing on specific use cases rather than building a general chatbot, one client achieved 70% automated resolution of customer inquiries while maintaining customer satisfaction scores.
But here's the most important result: every successful implementation started with a business problem, not an AI capability. The clients who focused on solving specific challenges saw immediate ROI, while those who tried to "add AI to everything" struggled to demonstrate value.
Timeline-wise, focused implementations typically deliver measurable results within 2-4 weeks, while comprehensive AI platforms often take months to show any business impact.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After months of testing AI plugins in Bubble applications, here are the key lessons that will save you time and money:
1. AI is digital labor, not artificial intelligence. Treat it like hiring a very fast, very specialized worker who can do specific tasks at scale, but can't think creatively or adapt to new situations without training.
2. Plugin features don't predict business results. The most impressive technical capabilities often translate to zero revenue impact. Focus on workflows that directly solve customer problems or reduce operational costs.
3. Reliability trumps sophistication. A simple AI workflow that works 99% of the time beats a complex system that fails unpredictably. Plan for failures and build fallback processes.
4. Custom prompts matter more than model choice. Spending time crafting specific prompts for your use case delivers better results than switching between different AI models or plugins.
5. Start with high-volume, low-stakes tasks. Product descriptions, meta tags, and categorization are perfect first implementations. Customer service and decision-making require more sophisticated approaches.
6. Measure everything immediately. If you can't measure the business impact of your AI implementation within 2 weeks, you're probably solving the wrong problem.
7. AI works best for existing processes. It's better at optimizing workflows you already have than creating entirely new business models. Don't let AI determine your product strategy.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Start with content generation workflows for blog posts and product descriptions
Use AI for lead qualification before human sales involvement
Implement automated customer onboarding sequences based on user behavior
Focus on reducing time-to-value rather than adding features
For your Ecommerce store
Automate product description generation for large catalogs
Implement AI-powered product categorization and tagging
Use AI for personalized product recommendations based on browsing behavior
Automate review response generation and sentiment analysis