Building AI Products That Ship

Most AI features fail not because the technology is wrong, but because the teams building them are using frameworks designed for a different kind of software.

The most expensive AI features are the ones that pass every technical review and fail at the user need. Here's how to catch them before you build.

A value hypothesis is not a requirement. It's a bet with stated odds — and how you write it determines whether you'll know when you're wrong.

Users can't tell you whether they want an AI feature. They can tell you how they behave when the AI is wrong — and that's what you actually need to know.

When your competitor calls the same API you do, raw model capability is not a moat. Here's what actually is.

Story points assume the outcome is known and the effort is uncertain. AI development flips this: the effort is bounded but you often won't know the outcome until you're deep in it.

The instincts that make you good at working with software engineers don't fully transfer to ML engineers. The gaps are predictable, and so are the fixes.

For AI features, the evaluation dataset is the product spec. It's the concrete, testable artifact that defines what quality means — and who builds it matters.

An AI-native product gets to make AI the organizing principle. A retrofit has to make AI a good guest in a house it didn't design. These are different problems.

You don't need to write SQL to own AI data strategy. You need to ask the right questions before anyone writes a line of code — and know what the wrong answers mean.

Most software features cost almost nothing per additional user. AI features cost real money every time someone clicks. That changes everything about how you price them.

Traditional software fails loudly. AI features fail quietly — outputs degrade, users disengage, costs balloon — and by the time you notice, you've lost months of user trust.

AI features resist the commitments traditional roadmaps demand. The answer isn't looser deadlines — it's a different structure that's honest about what you're actually committing to.

A complete worked example: you're PM for a B2B SaaS product and the CEO wants an AI-powered support chatbot shipped in 90 days. Walk through every stage of the framework — with templates and deliverables at each step.

A worked example in pricing under cost pressure: your AI-powered document analysis feature costs $0.50 per document in API calls. You charge customers $29/month for unlimited use. Walk through the cost analysis, usage modeling, tier design, stakeholder communication, and A/B test design needed to make the right call.

A worked example in kill discipline: a 6-month AI pilot showed 'promising results' but the team wants another 6 months. Walk through the kill framework, sunk cost analysis, upward communication strategy, and how to redirect the team and salvage learnings.

A worked example in executive communication: the board wants a 30-minute presentation on your AI product strategy. Walk through narrative structure, the metrics that matter to directors, handling the workforce replacement question, and a one-page AI strategy template.