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You need data to train your AIYou need users to get that dataYou need working AI to attract usersYou need... well, you get it 🔄
Small but meaningful → One problem, solved wellQuick to implement → From idea to users fastEasy to measure → Clear success signals (or not)🎨 The human touch of design⚙️ The technical rigor of machine learning engineering
The Problem
"Let's add AI to this! 🔥🔥"
^ Every product manager's favorite phrase in 2025. Most teams excel at building AI or designing experiences—but not both. The data tells the story:
2022 →
54% of AI projects reach production
2024 →
Only 18-36% achieve expected benefits
2025 →
Yet 67% plan to increase AI investment
The result? Features users abandon, because great AI needs both. Yet despite these challenges, AI investment continues to surge:
The Data Paradox in AI
With generative AI exploding, every industry is reimagining their processes—from marketing to product development to IT. But there's a catch that most teams discover too late.
Imagine building an AI recommendation system. Sounds straightforward? Here's the paradox:
This isn't theoretical—70% of leading AI companies identify data as their biggest roadblock.
Pro Tip: Start with ✨ minimum viable experience ✨ first, then let data guide your AI evolution
Introducing ✨ MVE ✨
✨ Minimum Viable Experience (MVE) means beginning with the simplest AI setup that still offers real benefits. It's like the “Hello World” of AI products—just enough to get feedback from actual users.
Three key principles:
The key? A framework that bridges both worlds:
Ready to bridge the gap between UX and ML?
Checkout the next blog post as we compare the two workflows
Next →
🚀 Let's Connect
I'm always excited to discuss the intersection of AI and user experience, or explore potential collaborations.
✉️
🌎 Citizenship: US, Canada
📍 Location: Toronto, ON, Canada
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