SIGNAL™
Intelligent Product Discovery
Universal discovery framework that transforms noise into clear product decisions. Works for any product, with or without AI, using intelligence and structured sense-making to reduce uncertainty and guide confident decisions.
“Discovery is not about collecting opinions. Discovery is about detecting signals.”
Overview & Purpose
SIGNAL™ is ProductLab's proprietary discovery framework designed to transform noise into clear product decisions. It applies to any digital product, with or without AI, using intelligence and structured sense-making to reduce uncertainty and guide confident decisions.
When to Use SIGNAL™
Use SIGNAL™ for product discovery, roadmap definition, opportunity assessment, and strategic decision-making across any type of product. It is particularly valuable in environments with:
- âś“High ambiguity and competing priorities
- âś“Information overload and noisy signals
- âś“Complex stakeholder landscapes
- âś“Uncertain market conditions
Core Principle
SIGNAL™ replaces static discovery phases with continuous sense-making focused on reducing uncertainty rather than validating assumptions. The goal is not to prove you're right — it's to learn what's true.
The SIGNAL™ Loop
A continuous cycle of four interconnected phases
CONTEXT
Build shared understanding
What do we already know — and what are we assuming?
Activities: Problem definition, constraint mapping, decision history review, stakeholder alignment
AI Acceleration: Information synthesis, living memory, noise reduction
Output:
Problem Statement + Constraints Map + Assumptions List
AMPLIFY
Expand insights & patterns
What signals exist that we haven't yet detected?
Activities: Feedback analysis, market research, data exploration, hypothesis generation
AI Acceleration: Pattern detection, feedback clustering, non-obvious insights
Output:
Opportunity Map + Signal Inventory + Hypothesis Backlog
SENSE
Interpret & prioritize
Given what we know, what should we prioritize?
Activities: Impact assessment, trade-off analysis, difficult decisions, prioritization
AI Acceleration: Scenario simulation, comparative analysis, decision modeling
Output:
Prioritized Hypotheses + Trade-off Matrix + Decision Rationale
EXPERIMENT
Learn with low cost
How can we test our hypotheses with minimum investment?
Activities: Prototyping, user testing, MVPs, rapid validation
AI Acceleration: Test design, results analysis, accelerated learning
Output:
Validated/Invalidated Hypotheses + Learning Log + Next Iteration
When to Move from Sense to Experiment
Move to Experiment when all three conditions are met:
You can write the hypothesis in one clear sentence
You know what evidence would prove you wrong
The cost of testing is lower than the cost of more analysis
SIGNAL™ Canvas
One-page discovery tool — print and fill
SIGNAL™ Canvas Preview
Download the full canvas template below
Free template for product discovery and decision-making
Canvas Sections:
- • Context: Problem statement, constraints, assumptions
- • Amplify: Signals detected, patterns, data sources
- • Sense: Top hypothesis, trade-offs, rationale
- • Experiment: Test design, success criteria, learnings
Best Practices:
- • Use for each discovery iteration
- • Share with stakeholders for alignment
- • Update as new signals emerge
- • Archive completed canvases for learning
Apply SIGNAL™ in Your Organization
Our AI Opportunity Discovery service uses SIGNAL™ to help you find 3-5 AI quick wins in just 1 week.