The excitement around AI has reached fever pitch. Every startup claims to be "AI-powered," every enterprise is launching "AI initiatives," and every product roadmap has an "AI feature" somewhere. Yet the vast majority of these efforts fail to deliver meaningful value.
After building AI products that actually work—and watching many others that don't—I've identified the patterns that separate successful AI implementations from expensive science projects.
The Demo Trap
The most seductive failure mode in AI product development is what I call the "demo trap." A team builds a compelling proof-of-concept that wows stakeholders. It works beautifully on curated examples. Leadership gets excited, funding flows, and the race to production begins.
Then reality hits.
The demo that worked perfectly on 50 hand-picked examples fails spectacularly on real-world data. Edge cases multiply. Users find creative ways to break the system. The 95% accuracy that seemed impressive becomes 95% of users having a frustrating experience at least once per session.
The gap between demo and production isn't a technical problem—it's a product problem. The best AI teams build for failure modes from day one.
What Actually Works
Successful AI products share a common trait: they're designed around AI's limitations, not just its capabilities. Here's the framework I use:
1. Design for Graceful Degradation
Every AI system will fail. The question is whether failure means a slightly suboptimal recommendation or a completely broken user experience. Build fallback paths that preserve user value even when the AI component fails.
2. Make AI Assistance, Not AI Automation
Users trust AI more when they feel in control. Instead of fully automated decisions, design systems where AI augments human judgment. Suggestions, not dictates. Recommendations, not requirements.
3. Build Feedback Loops Into the Product
AI systems improve with data, but only if you're collecting the right data. Design explicit feedback mechanisms that capture user corrections and preferences. Make the feedback loop part of the value proposition, not an afterthought.
The Competitive Moat
The companies building durable AI advantages aren't just using better models—they're building proprietary data flywheels that make their products better over time. Every user interaction generates data that improves the system, which attracts more users, which generates more data.
This is why I focus on AI systems that compound: products that get measurably better with every user, every interaction, every day.
Moving Forward
If you're building AI products, start with the failure modes. Map out every way the system could fail, and design the user experience around those scenarios. The demo is the easy part. Production is where products are won or lost.