Building an AI feature involves design decisions that are easy to underestimate. Here are five patterns worth considering, and the factors that should shape how you apply them. 1. AI Input Design: Format, Scope, and Guidance The input pattern covers three distinct decisions: format, scope, and guidance. What modality the user interacts through. A text field,…
Most product teams designing AI features start with capability: what can this model do? That question shapes what you build, but three others shape whether it succeeds: What are users ready to accept? How much of the work should be automated? And where does a human need to stay in the picture? The Overton Window: Know…
Product teams have shipped dozens of features. They know how to scope, estimate, design, validate, and ship. So when an AI feature lands on the roadmap, they treat it like any other backlog item. That assumption is risky. AI features need to be treated differently. The seven areas where teams most often get this wrong are…
An AI PRD needs to account for behavior you can't fully predict and quality criteria that are subjective and evolve with real usage. That's because AI is fundamentally different from traditional software: it's probabilistic, not deterministic. That uncertainty runs through every section of the PRD: how you scope the work, what you require, how you measure…
AI products are measured differently from the products you've managed before. This glossary covers the metrics you need to evaluate performance, spot problems, and make decisions with your team. Not every metric applies to every product. Use this as a reference for the terms you encounter, not a list of everything you need to track. Many…
Building AI into a product is easy to get excited about and hard to get right. Most teams either start with the technology and work backward to find a use for it, or they identify real problems but generate ideas without any grounding in what AI can actually do today. Neither produces a roadmap worth building.…
The previous post in this series ends with a prioritized list of pain points your team has aligned on. What it does not tell you is whether those pain points are worth solving. These five questions give you a consistent way to stress-test any pain point before you commit resources to it. 1. Magnitude: How many…
Before you decide what to build with AI, you need to understand where it will actually make a difference. User experience mapping gives your team a picture of the user's existing reality: what they do today, where they hit friction, and which problems are worth solving. The output is a prioritized set of pain points your…
The cost of running an AI model is easy to measure. It shows up in cloud bills, API invoices, and unit economics spreadsheets, and that visibility makes it a natural anchor for pricing decisions. It shouldn't be. The Floor and the Ceiling For any AI product, you're working between two numbers. The floor is your full…
AI products introduce a layer of complexity that standard discovery methods are not designed to handle. The system makes probabilistic decisions. It will be wrong sometimes. Users will have emotional reactions to those mistakes that they cannot accurately predict. And what users tell you they will do when confronted with a faulty output is rarely what…
Most organizations are now using AI in some capacity. That alone tells you something important: access to AI tools is not a competitive advantage. Everyone can call the same APIs and run the same models. The organizations actually getting value from AI are not winning because they found a better tool. They are winning because they…
AI features are now standard product work. Recommendation engines, fraud detection, content moderation, and intelligent search ship on the same schedule, with the same stakeholder expectations, and under the same PM ownership as any other feature. What is different is how you build them. The lifecycle that follows covers eight steps. They build on each other,…
Artificial intelligence is no longer a single thing. It's a collection of distinct capabilities, each solving a different class of problem. That distinction matters for product leaders. Knowing what each capability does, and what it can't do on its own, is what allows you to move from "we should use AI" to a clear answer about…
Anyone who has led organizational change has seen a predictable pattern. The change is introduced. Resistance follows. Performance drops. Teams struggle through uncertainty before things stabilize at a new normal, if they get there at all. The initiative is sound. The case for change is clear. And yet people resist and what looked straightforward on paper…
Supervised learning is the branch of machine learning behind spam filters, fraud detection systems, medical imaging tools, and large language models like ChatGPT. Understanding how it works, and where it reaches its limits, matters for anyone making decisions about building or buying AI-powered products. What Supervised Learning Actually Is Supervised learning is a method where a…