AI Product Manager

Error State Design for AI

Por qué: Lets you specify what the user sees when the model times out, hallucinates or answers with low confidence, so a failure during a customer demo becomes a recoverable message rather than a wrong answer.

Progressive AI Disclosure

Por qué: Lets you plan a path from a suggestion the user accepts to an action the AI takes alone, unlocking autonomy only after users have seen the feature behave well in their own context.

A/B Testing AI Features

Por qué: Lets you design an experiment on a feature whose output changes per request, choosing sample size and a success metric that survives the noise, so a win is not just variance.

Benchmark & Eval Set Management

Por qué: Lets you keep a versioned set of real cases that every prompt or model change must pass, so a vendor upgrade that quietly breaks a core scenario is caught before users see it.

Regulatory Compliance (EU AI Act)

Por qué: Lets you classify a feature by risk under the EU AI Act and know which transparency duties apply before launch, instead of learning about them from legal after the roadmap is committed.

AI Ethics Decision Frameworks

Por qué: Lets you bring a contested decision, such as using customer data to improve a model, to a structured review with a documented rationale, rather than settling it by whoever argues loudest.

Model Selection & Vendor Strategy

Por qué: Lets you compare vendors on capability, cost, latency and data terms and plan for portability, so a price rise or deprecation does not force an emergency rewrite.

Fine-Tuning Decisions

Por qué: Lets you answer a request to train a custom model by first checking whether better prompts or retrieval would do, and by pricing the data, training and maintenance the custom route needs.

AI-First Product Vision

Por qué: Lets you propose a product whose workflow only makes sense because of AI, rather than a chat box attached to an existing screen, and defend the change in value proposition to leadership.

Competitive Moat with AI

Por qué: Lets you ask what stays defensible when a competitor can call the same model, and design the product so usage feeds proprietary data that improves it, instead of relying on the model alone.