Unit 1: The AI product landscape: types, patterns, and opportunities

Learning objectives

Classify different types of AI products. Recognize common AI product patterns across industries. Identify AI opportunities in your own product.

Video script

Reading material

Pattern analysis

Each AI product pattern has specific product management implications.

Intelligence layer. You're adding AI-driven insights to data users already have. The challenge: users need to trust the insights enough to act on them, but not trust them blindly. Design for informed decision-making, not automation.

Automation. You're replacing manual tasks with AI-driven processes. The challenge: understanding which tasks users want automated and which they want to keep control over. Automate the tedious, keep the meaningful.

Generation. You're using AI to create new content. The challenge: quality consistency and user ownership. If the AI generates a report, whose name goes on it? Quality needs to be high enough that users are comfortable taking ownership.

Prediction. You're forecasting outcomes to help users plan. The challenge: handling uncertainty. A prediction with 80% confidence needs to be communicated differently than one with 95% confidence.

Personalization. You're tailoring experiences to individual users. The challenge: the "creepy vs. helpful" line. Personalization that feels responsive is valued. Personalization that feels like surveillance is rejected.

Practical exercise

Exercise: Pattern identification

Choose three AI-powered products you use or are familiar with. For each one, identify the AI product type (assisted, native, or infrastructure) and the primary AI pattern (intelligence, automation, generation, prediction, or personalization).

Then analyze: what's the biggest product challenge for this specific combination? How well is the company addressing it? What would you do differently?

Write up your analysis as a brief comparison (one paragraph per product).