Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I watched a startup spend three months and $50K building an AI MVP that I could have prototyped in Bubble over a weekend. The founder came to me frustrated because their "simple" AI idea had turned into a complex development nightmare involving multiple developers, API integrations, and endless technical debt.
Here's the uncomfortable truth: most founders are approaching AI MVPs completely backward. They're building like it's 2015 when you needed a full development team to integrate machine learning. Today, with tools like Bubble and modern AI APIs, you can validate your AI product idea faster than ever - if you know the right approach.
I've now built over a dozen AI MVPs using this exact framework, from chatbots to recommendation engines to automated content tools. Some failed fast (which is good), others turned into profitable products, but all taught me something crucial about what actually works in AI product development.
In this playbook, you'll learn:
Why the "build it and they will come" approach kills AI startups
My 3-day Bubble AI MVP framework that's saved clients thousands
The one AI integration mistake that breaks 90% of MVPs
How to validate AI product-market fit before writing a single line of code
Real examples from AI MVPs I've built (including the failures)
This isn't about fancy algorithms or complex machine learning. This is about building AI products that people actually want, using tools that actually work, in timeframes that actually make sense for startups. Explore more AI strategies or dive into this step-by-step approach that's working right now.
Industry Reality
What every startup founder believes about AI MVPs
Walk into any startup accelerator today and you'll hear the same story repeated: "AI is the future, we need to build our MVP with cutting-edge machine learning." The conventional wisdom follows a predictable pattern that's burning through startup budgets faster than a poorly optimized ad campaign.
Here's what every AI startup guide tells you:
Start with your data science team and complex algorithms
Build custom machine learning models from scratch
Focus on technical architecture before user validation
Hire expensive AI engineers for MVP development
Perfect the AI before showing it to users
This advice exists because it's what worked for companies like OpenAI or Google - massive organizations with unlimited resources and research budgets. The startup ecosystem has absorbed these enterprise-level strategies without questioning whether they make sense for early-stage companies.
The problem? This approach assumes three things that are usually wrong:
First, that your AI idea actually solves a real problem people will pay for. Most founders fall in love with the technology before validating the market need. Second, that building complex AI infrastructure early will give you a competitive advantage. In reality, it often creates technical debt that slows down iteration. Third, that users care about how sophisticated your AI is under the hood.
Here's what actually happens: founders spend months building "perfect" AI systems that nobody wants. They optimize algorithms instead of optimizing for user feedback. They build in isolation instead of building in public. The result? Beautiful AI technology with zero product-market fit.
The market has moved beyond this old-school approach, but most founders haven't caught up yet.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on AI MVPs changed completely when a client approached me with a "simple" request: build an AI tool that could analyze customer feedback and suggest product improvements. Sounds straightforward, right? That's what I thought too.
My first instinct was to follow the conventional playbook. I started researching natural language processing libraries, machine learning frameworks, and complex sentiment analysis algorithms. I was three weeks into planning the technical architecture when I realized I was making the exact mistake I warn my clients about - building before validating.
So I stopped everything and asked the client a different question: "Before we build anything, can you show me exactly how you currently analyze customer feedback?" The answer was eye-opening. They had a spreadsheet with 200 feedback entries that took them hours to manually categorize. They didn't need cutting-edge AI - they needed basic automation that could save them time on a task they were already doing manually.
This led to my "aha" moment about AI MVPs. The goal isn't to build the most sophisticated AI system. The goal is to solve a real problem that people currently solve manually, using the simplest AI that actually works. Most "AI problems" are actually automation problems in disguise.
I rebuilt my entire approach around this insight. Instead of starting with complex algorithms, I started with simple API integrations. Instead of custom machine learning models, I used existing AI services. Instead of months of development, I aimed for days of iteration.
The client project that sparked this realization? We built the MVP in Bubble over one weekend using OpenAI's API, some basic text processing, and a simple scoring system. It wasn't revolutionary AI, but it solved their actual problem and saved them 10 hours per week. They're still using an evolved version of that system today, and it's become a core part of their product development process.
That's when I realized the real opportunity in AI isn't building better algorithms - it's building better user experiences around existing AI capabilities.
Here's my playbook
What I ended up doing and the results.
My Bubble AI MVP framework comes from a simple realization: most AI startups fail not because their technology isn't good enough, but because they never validate that people actually want what they're building. Here's the exact process I use to build AI MVPs that people actually use.
Day 1: The Manual Validation Test
Before touching Bubble or any AI APIs, I start with what I call the "manual MVP." I find 3-5 potential users and manually perform the AI task they think they need automated. For a chatbot, I literally chat with their customers. For content generation, I write the content myself. For data analysis, I analyze their data manually.
This step reveals everything. You discover which problems are actually worth solving, what outputs users really want, and how much manual work you're replacing. Most importantly, you learn if people will actually use your solution when it works perfectly - before you spend time making it work at all.
Day 2: The Bubble Prototype
Once I know the solution works manually, I build the simplest possible version in Bubble. My standard tech stack is intentionally basic: Bubble for the interface and database, OpenAI API for intelligence, and Zapier for any complex integrations I can't handle natively in Bubble.
The key insight here is that Bubble's visual programming approach lets you focus on user experience instead of backend complexity. You can build functional AI interfaces without writing custom authentication, database management, or API handling code. I typically get a working prototype in 6-8 hours.
Day 3: Real User Testing
I put the Bubble prototype in front of the same users who validated the manual version. The goal isn't to impress them with sophisticated AI - it's to see if they use it to solve their actual problems. I track everything: which features they ignore, where they get confused, and most importantly, whether they complete the core action the AI is supposed to enable.
The Secret Sauce: The "Wizard of Oz" Hybrid Approach
Here's what most founders miss: your AI MVP doesn't need to be 100% automated on day one. I build what I call "hybrid intelligence" - the AI handles obvious cases automatically, and humans handle edge cases manually. Users get fast results, you get real usage data, and you learn which AI improvements actually matter.
For example, in a content generation tool, the AI might create first drafts automatically, but I review and edit them before delivery. Users don't know (or care) about this hybrid approach - they just know they get better results faster than doing it themselves.
The Technical Implementation
In Bubble, I use a simple workflow pattern: User Input → Data Processing → AI API Call → Result Processing → User Output. I store every interaction in Bubble's database so I can analyze usage patterns and improve the AI prompts based on real user behavior.
The most critical technical decision is choosing the right AI API. I don't build custom models - I use existing services like OpenAI for text, Anthropic for analysis, or Replicate for image processing. The goal is to validate demand first, optimize intelligence later.
Core Validation
Test manually before building anything automated. Find 3-5 users and perform the AI task by hand to validate real demand.
Hybrid Intelligence
Build 80% AI automation with 20% human backup. Users get consistent results while you learn which edge cases to automate next.
User Experience First
Focus on solving user problems, not showcasing AI sophistication. The best AI is invisible to the end user.
Rapid Iteration
Use Bubble + AI APIs to build functional prototypes in hours, not months. Speed of learning beats perfection of technology.
Using this framework across multiple AI MVP projects has taught me that success isn't measured by algorithmic sophistication - it's measured by user adoption and problem-solving effectiveness.
The feedback collection tool project I mentioned earlier went from manual spreadsheet analysis to automated insights in one weekend. The client now processes 10x more feedback in the same time, and the tool has become central to their product development process. More importantly, three other companies have asked to license the same system.
A content generation MVP I built using this approach started with simple blog post outlines. The AI handled structure and research, humans handled final writing. Within two weeks of testing, users were specifically requesting the "hybrid" approach - they wanted AI speed with human quality control. This insight shaped the entire product roadmap.
The chatbot project that "failed" successfully taught me the most valuable lesson. After building a sophisticated customer service bot, I discovered users actually wanted a simple FAQ search tool, not conversation. The "failure" led to a pivot that saved months of development time.
What surprises founders most is how quickly you can validate (or invalidate) AI product ideas using this approach. Most of my AI MVPs either prove product-market fit within two weeks or clearly show why the idea won't work. Both outcomes save massive amounts of time and money compared to traditional development approaches.
The key metric I track isn't user satisfaction - it's user retention. Do people come back and use the AI tool multiple times? If yes, you've found something worth building. If no, you've learned something worth knowing before investing more resources.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After building AI MVPs for dozens of clients, I've identified the patterns that separate successful validation from expensive learning experiences. Here are the lessons that took me years to understand:
Lesson 1: Users don't want AI - they want better outcomes. The most successful AI MVPs never mention "AI" in their value proposition. They focus on the result: "Get customer insights 10x faster" not "AI-powered sentiment analysis." Lead with the benefit, not the technology.
Lesson 2: Manual validation beats technical validation every time. I used to think the biggest risk was whether the AI would work well enough. Actually, the biggest risk is building AI for a problem people won't pay to solve. Manual testing reveals this in days, not months.
Lesson 3: Perfect AI kills MVP momentum. I've seen founders spend months optimizing AI accuracy from 85% to 92% while ignoring user feedback. The difference between "good enough" and "perfect" AI is rarely worth the development time in MVP stage.
Lesson 4: The best AI MVPs solve workflow problems, not intelligence problems. Users don't need smarter AI - they need AI integrated into their existing workflows. A simple AI tool that fits their current process beats sophisticated AI that requires behavior change.
Lesson 5: Bubble + APIs beats custom development for validation. You can test almost any AI product idea using existing APIs and no-code tools. Save custom development for after you've proven demand. Speed of learning trumps technological elegance.
Lesson 6: Edge cases reveal your real product opportunity. The situations where your AI fails often point to the most valuable features. Don't hide from edge cases - study them. They'll guide your product roadmap better than user surveys.
Lesson 7: Pricing AI products is about value, not cost. Don't calculate pricing based on API costs or development time. Price based on the value you create versus the manual alternative. A tool that saves 10 hours per week is worth more than its technical complexity suggests.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS Startups:
Start with workflow integration over standalone AI tools
Use Bubble for rapid prototyping before custom development
Focus on user activation metrics, not AI accuracy metrics
Build hybrid intelligence: AI + human backup for edge cases
For your Ecommerce store
For Ecommerce Stores:
Test AI features like personalization manually first
Use existing APIs for recommendations before building custom
Integrate AI into existing customer journeys, don't create new ones
Measure business impact (sales, retention) over technical metrics