We've shipped AI products for over a decade. Some succeeded wildly. Some failed. Most taught us something. This playbook distills those lessons into a practical guide for building AI products that actually launch.
Start with the problem, not the technology
The projects that fail start with 'We should use AI for something.' The projects that succeed start with 'We have this specific problem that AI might solve.' The difference sounds subtle but it's everything.
Scope ruthlessly
AI projects have a tendency to expand. The model could do this, and this, and this. Resist. Ship the smallest useful thing first. You can always add more. You can't get back the months you spent building features nobody needed.
- Define the single most important capability
- Build that first, ship it, learn
- Add capabilities based on real user feedback
- Kill features that don't get used
Plan for imperfection
AI systems make mistakes. Plan for it from day one. What happens when the model is wrong? How do users correct it? How do you catch and learn from errors? The best AI products aren't the ones that never fail; they're the ones that fail gracefully.
Users will forgive AI that makes mistakes. They won't forgive AI that makes mistakes and can't be corrected.
Human-in-the-loop by default
Unless you have a compelling reason otherwise, keep humans in the loop. AI should augment human decision-making, not replace it entirely. This is especially true for high-stakes decisions. The goal is to make humans more effective, not to remove them.
Measure what matters
Define success metrics before you build. Not 'AI accuracy' but business metrics: time saved, revenue generated, errors prevented, customer satisfaction improved. If you can't measure the impact, you can't justify the investment.