AI Product Fundamentals
Non-determinism, trust, failure modes, UX for AI features
1
Progressive Trust Architecture
Constrain first. Observe. Expand. Log everything.
When you build an AI feature, don't give it full control immediately. Start by having the AI make suggestions that a human approves before anything happens. As you confirm the AI is getting it right, let it do more on its own. This prevents bad outputs from destroying user trust before you've proven the feature works.
2
Graceful Degradation by Design
Intercept before delivery — fluent is not correct, and no answer beats a wrong answer.
Unlike a traditional app that crashes with an error code, an AI can fail silently by producing a convincing but completely wrong answer. Good AI product design means building checkpoints that catch bad outputs before users see them, and always having a backup plan — like showing a 'we're working on it' state or routing to a human — instead of letting the AI guess its way into a disaster.
3
Fault-Tolerant Expectation Design
Design for the model you have, not the model you want.
AI doesn't always give the same answer twice, and it's sometimes wrong. If you design your product assuming it's always right, users will be blindsided when it fails. Instead, design so that when the AI is wrong, the user can easily correct course or try again without losing trust.
4
Two-Layer AI Measurement
Engagement proves activity. Two layers prove value.
With normal software, if users click a button a lot, that usually means it's working. With AI, a user can click a lot because they're confused and getting wrong answers. So you need to measure two things separately: whether users actually finished their goal, and whether the AI's outputs were accurate and trustworthy. Measuring only one without the other gives you a false picture.
5
Deterministic-AI Decision Boundary
Rules for correctness, AI for judgment — and always design the handoff.
Traditional software follows a fixed script — click this button, get that result, every time. AI is more like a very capable but occasionally unpredictable expert: great for messy, open-ended tasks, but unreliable for anything where being wrong even once is unacceptable. As a PM, your job is deciding which parts of your product need the script and which parts need the expert.