Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
I remember staring at a potential client's brief last year. They wanted an AI-powered platform for their marketplace business. The budget was substantial - one of my biggest potential projects to date. But something felt off about their approach.
They came to me excited about the no-code revolution and new AI tools. They'd heard these tools could build anything quickly and cheaply. Technically, they weren't wrong - you can build a complex platform with these tools.
But their core statement revealed the fundamental problem: "We want to see if our idea is worth pursuing." They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm.
That's when I made a decision that initially shocked them - and changed how I think about AI MVPs forever. I said no to the project. Not because it wasn't technically feasible, but because they were asking the wrong question entirely.
If you're wondering whether you can build an AI MVP without coding, you're probably asking the wrong question too. Here's what you'll learn from my experience:
Why the "can I build it?" question misses the point entirely
The real constraint isn't building - it's knowing what to build and for whom
My framework for testing AI ideas before touching any tools
The specific no-code tools that actually work for AI MVPs
Why most "AI MVPs" fail before they even launch
Ready to rethink everything you thought you knew about AI development? Let's dive in.
Conventional Wisdom
What every entrepreneur thinks about AI MVPs
Walk into any startup accelerator or scroll through Twitter, and you'll hear the same advice repeated endlessly. The conventional wisdom around AI MVPs goes something like this:
"Just ship it fast with no-code tools." The story always follows the same pattern: Use Bubble for the frontend, integrate some OpenAI APIs, maybe throw in some Zapier automation, and boom - you've got an AI startup. The focus is entirely on speed and technical execution.
Here's what most "experts" recommend:
Start with the tool selection - Pick your no-code platform first, then figure out what to build
Build the full product - Create a complete AI-powered solution with all the bells and whistles
Launch and iterate - Get it out there and see what happens
Scale with funding - Raise money based on the "AI" angle and growth potential
Worry about monetization later - Focus on user acquisition first, revenue will follow
This approach exists because it feels productive. Building things is tangible. You can show progress, demo features, and impress investors with slick interfaces. The tools themselves have become incredibly powerful - platforms like Bubble and Zapier really can help you build complex systems without coding.
But here's where this conventional wisdom falls apart: It assumes the constraint is technical, when it's actually strategic. In 2025, the constraint isn't "Can I build this?" The constraint is "Should I build this, and for whom?"
Most entrepreneurs get so excited about AI's possibilities that they skip the boring work of validating product-market fit. They build first, ask questions later. And that's exactly why 90% of AI startups fail.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the exact moment this hit me. The client I mentioned earlier - let's call them a "two-sided marketplace platform" - had done their homework on the technical side. They'd researched no-code tools, mapped out user flows, even created mockups.
They wanted to connect service providers with customers using AI to optimize matching and pricing. Sounds solid, right? The technical execution would have involved Bubble for the main platform, integrated AI APIs for the matching algorithm, payment processing, user management - the works.
But when I dug deeper, red flags started appearing everywhere:
No existing customer relationships - They'd never run a marketplace, didn't understand the chicken-and-egg problem
No understanding of unit economics - They had no idea what customers would pay or what it would cost to acquire them
No validation of the AI component - They assumed AI would make matching "better" but had never tested this assumption
No go-to-market strategy - Their plan was literally "build it and they will come"
They wanted to spend three months and a significant budget building something to "test if the idea works." That's when I realized the fundamental flaw in how most people approach AI MVPs.
I told them something that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build - not three months."
Their reaction was immediate: "But how can we test an AI marketplace in one day?" That question revealed everything. They were conflating testing the idea with testing the technology. They thought they needed to build the AI to validate the concept.
This is the trap most entrepreneurs fall into. They think "AI MVP" means "minimum viable AI product." But what they really need is "minimum viable validation" - the smallest possible test to validate whether anyone actually wants what they're planning to build.
I've seen this pattern repeat across dozens of client conversations. Everyone wants to jump straight to building because that's the "fun" part. But the real work - the boring, unsexy work of finding customers and understanding their problems - gets skipped entirely.
Here's my playbook
What I ended up doing and the results.
After turning down that project, I developed a completely different approach to AI MVPs. It's based on a simple principle: Your MVP should be your marketing and sales process, not your product.
Here's the exact framework I now use with every client who comes to me with an "AI idea":
Day 1: Manual Value Test
Before touching any tools, I make them prove value manually. For the marketplace client, this meant:
Create a simple landing page explaining the value proposition
Manually reach out to 50 potential service providers
Manually reach out to 50 potential customers
Try to match them via email/WhatsApp for 2 weeks
No AI. No automation. Just pure manual validation of whether the core value proposition works.
Week 2-4: Pattern Recognition
If the manual matching works, you start seeing patterns:
What type of matches work best?
What information do you need to make good matches?
Where does the process break down?
What would customers pay for this service?
This is where you discover whether AI would actually add value, or if it's just a shiny distraction.
Month 2: Automation Only After Validation
Only after proving demand do you start building. And even then, you don't build "AI" - you build the smallest possible automation that solves the validated problem.
For most businesses, this means:
Simple database - Use Airtable or Google Sheets to track matches
Basic automation - Use Zapier to automate notification emails
Payment processing - Integrate Stripe for transactions
Simple interface - Use Bubble or Webflow for a basic booking system
Notice what's missing? The AI. In most cases, you don't need AI to validate the core business model. You need it to scale and optimize later.
Month 3+: Strategic AI Integration
Only after proving the business model do you add AI strategically. But now you know exactly what problem you're solving:
Automating manual matching processes that you've proven work
Improving match quality based on data you've collected
Scaling processes that are already profitable
This is completely different from "let's build AI and see what happens." You're using AI to scale something that already works, not to figure out what might work.
The tools I actually recommend for this approach:
Validation phase: Notion, Google Forms, manual outreach
MVP phase: Bubble + Zapier + Airtable + Stripe
AI integration: OpenAI API + custom workflows in Bubble
The result? You end up with an AI product that solves a real problem for real customers, instead of a cool demo that nobody wants to pay for.
Validation First
Prove value manually before building anything. Most AI ideas fail at this stage - better to learn early.
Minimal Automation
Start with simple tools like Zapier and Airtable. Add complexity only when you understand the problem deeply.
Strategic AI
Integrate AI to scale proven processes, not to validate untested assumptions about what customers want.
Distribution Focus
Build your audience and sales process first. The best AI in the world is useless without customers.
Here's what happened when I started applying this framework with clients:
The marketplace client took my advice and spent two weeks manually matching service providers with customers. Within 10 days, they discovered their core assumption was wrong. Customers didn't want "AI-optimized matching" - they wanted faster response times and better communication. No AI required.
They pivoted to a simple booking platform with automated notifications. Revenue in month one. They saved 3 months of development time and thousands in budget by testing manually first.
A SaaS client wanted to build "AI-powered customer support." Instead of building an AI chatbot, we had them manually respond to customer inquiries for two weeks while documenting common questions. They realized 80% of inquiries were about the same 5 topics. Solution? A simple FAQ page and better onboarding, not AI.
An e-commerce client wanted "AI product recommendations." We manually curated product bundles for their top customers for one month. The manual curation worked so well it increased average order value by 40%. Only then did we automate the process with simple rules-based logic (not even machine learning).
The pattern is consistent: Manual validation reveals whether you need AI at all. In most cases, the answer is "not yet" or "not for that problem."
This approach has a 90% success rate in my experience, compared to the 10% success rate of "build AI first, validate later" approaches I've observed in the market.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this framework across dozens of client projects, here are the key lessons that changed how I think about AI MVPs:
AI is a scaling tool, not a validation tool - Use it to optimize proven processes, not to test unproven assumptions
Manual processes reveal AI opportunities - You can't automate what you don't understand manually
Distribution trumps technology - The best AI in the world is worthless without customers
Most "AI problems" aren't AI problems - They're process, communication, or UX problems in disguise
Start with the outcome, not the technology - Define success metrics before choosing tools
No-code tools are perfect for validation - But only after you know what you're validating
AI adds complexity, not simplicity - Only add it when the complexity is worth the benefit
The biggest mistake I see entrepreneurs make is treating AI as the solution to everything. In reality, AI is just another tool - and like any tool, it's only valuable when applied to the right problem at the right time.
If I had to give you one piece of advice about building AI MVPs without coding, it would be this: Stop asking "Can I build this?" and start asking "Should I build this?" The first question is about technology. The second is about business.
And in 2025, technology is the easy part.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Test AI features manually with your existing customers first
Use Bubble + OpenAI API for rapid prototyping
Focus on automating support and onboarding processes
Measure time-to-value improvement, not just engagement
For your Ecommerce store
For e-commerce specifically:
Start with manual product curation before building recommendation engines
Use Shopify apps for AI features before custom development
Focus on inventory and pricing optimization over customer-facing AI
Test AI-generated product descriptions with A/B testing