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Based on Nobel Prize–winning research on decision-making under uncertainty
A product manager combines fast and slow thinking.
They quickly select directions from a pre-prepared, prioritized list of user problems, then explore them in depth with the help of AI. In this process, AI acts as a “devil’s advocate,” helping them deeply understand the underlying user problem.
Through this process, the product leader quickly separates what works from what doesn’t—and takes full ownership of the decisions that follow.
80% rule
A product manager applies fast thinking. The goal is to quickly select possible directions for further development with minimal cognitive effort (System 1). The only criterion for selection is alignment with the company’s goals.
The product manager automatically receives a prioritized list of user pain points from AI.
Based on experience and tacit knowledge of the product domain, they quickly select what to further explore. We call this idea shopping—the creation of a narrowed-down list of potentially interesting ideas.
20% rule
A product manager applies slow thinking. The goal is to develop a deep understanding of user pain points, which requires cognitive effort (System 2).
The product manager “chats” with pain points to deeply understand the user problem and evaluate its traction. In this process, they often find that some pain points are not worth solving.
Our AI chat is pre-loaded with context for each specific pain point, including whether and how it has been addressed before. It acts as a “devil’s advocate” for the product manager—challenging assumptions and helping ensure a deep understanding of the user problem.
Taking ownership of the decision
The final step is presenting the selected pain points for development to stakeholders. The goal is to take ownership of the decision, test the messaging, and create alignment across the company.
The product manager uses AI to prepare a presentation that clearly shows why the pain point matters and how solving it supports the company’s goals.
In very flat management structures, instead of a presentation, the product manager adds the item directly to the roadmap, giving the decision real weight.
Daniel Kahneman, decision-making under uncertainty. Link: https://www.nobelprize.org/prizes/economic-sciences/2002/kahneman/facts/
Human product decision-making using AI is like online shopping
Identifying pain points that are strong enough to drive a company's product goals is a time-consuming and complex task in product strategy.
Continuum Tracker automatically links pain points to specific development opportunities and organizes them in a structured backlog, allowing you to focus on product strategy.
Objevování problémů
AI automaticky nachází uživatelské pain pointy ve firemních datech a strukturuje je podle důležitosti.
Výběr příležitostí
AI automaticky ukazuje trakci uživatelských problémů.
Hlubší porozumění a rozhodnutí
AI funguje jako „ďáblův advokát“ – pomáhá rozporovat produktové hypotézy, rozkrývat kontext a pochopit uživatelský problém do hloubky
Product context for AI agents
AI development agents operate without product context, making it hard for them to decide whether to implement a new feature or escalate it for human review.
Our AI agent embeds product context into the software development process, enabling accurate autonomous decision-making.