From Assumption to Validated Learning
Our learning model combines recognized theories with AI support to transform uncertainty into actionable knowledge. From the first assumption to an active decision – we guide you through the entire learning journey.
– Vi guider dig gennem hele læringsrejsen.
Experiment Kanban Structure
Our unique Kanban-based structure guides experiments through six crucial phases. From the first idea to scaling, the system ensures nothing is lost and every decision is data-driven.
Idea Phase
Problem and observations are defined. What do we see? What do we know? The journey starts with documenting symptoms and facts.
Assumption Mapping
Assumptions are prioritized by risk and uncertainty. We identify what we believe – and what is most important to test first.
Hypothesis
The most critical assumption is transformed into a testable hypothesis with clear success criteria and measurable indicators.
Experiment & Test
The hypothesis is tested in practice with the chosen method. Data is collected systematically, and deviations are documented.
Learning & Assessment
Results are analyzed and compared with expectations. What did we learn? What surprised us?
Scaling
Based on the learning, a decision is made: Test the hypothesis again, reject it, or scale the solution across the organization.
Built on Solid Theory
Our approach integrates five recognized learning models, each contributing unique perspectives on how organizations learn and adapt.
Kolb's Learning Cycle
Concrete experience → reflection → abstract thinking → active experimentation. We structure learning as a cyclical process where experience is transformed into knowledge.
Why we chose this theory:
Kolb's model ensures we don't jump from experience directly to action. By including reflection and abstract thinking, we transform random experience into repeatable knowledge.
Double-Loop Learning
Challenges not only "what went wrong?" but also "are our basic assumptions correct?". We go behind the symptoms and test the deep beliefs.
Why we chose this theory:
Most organizations only fix symptoms. Double-Loop forces us to question underlying assumptions – this is where truly transformative learning happens.
NASA Lessons Learned
Systematic documentation and sharing of learning across the organization. Quality assurance inspired by NASA's approach to critical knowledge sharing.
Why we chose this theory:
NASA has developed the world's most stringent system for capturing and sharing learnings. We integrate their principles to ensure valuable knowledge isn't lost between teams and projects.
Lean Startup
Build → Measure → Learn. Fast, cheap experiments that validate assumptions before large investments are made. Fail fast, learn faster.
Why we chose this theory:
Lean Startup methodology reduces risk by validating assumptions early. By testing hypotheses with minimal resources, organizations save time and money – and learn faster.
Surprise Index
Measures the distance between expectation and reality. The greater the surprise, the more valuable the learning – even when the hypothesis is disproven.
Why we chose this theory:
Surprise is the currency of learning. By measuring how much reality deviates from our expectations, we identify exactly where our mental models need updating.
Your Journey from Problem to Action
Each experiment follows a structured journey that ensures learning doesn't happen randomly, but is systematically transformed into better decisions.
Problem
Assumption
Hypothesis
Test
Learning
Decision
Action
AI as Your Learning Partner
Artificial intelligence is integrated throughout the process – not as a replacement for human judgment, but as a sparring partner that ensures quality and structure.
Guided Reflection Questions
AI asks the right questions at the right time to challenge assumptions and deepen understanding of results.
Learning Quality Assurance
A NASA-inspired scoring system evaluates the quality of your documentation and ensures important insights are not lost.
Intelligent Suggestions
Based on your context, AI suggests relevant hypotheses, test methods, and decision alternatives.
Automatic Documentation
AI helps structure and summarize learnings so they can be shared and retrieved across the organization.
From Learning to Action
Experiments don't end with data – they end with a conscious decision. We support six decision types, each representing a clear path forward.
Iterate
Adjust and try again. Results show potential but require refinement of the approach.
Persevere
Continue on current course. Data confirms the direction is right.
Pivot
Change direction fundamentally. Learning shows a different approach is needed.
Scale
Roll out at larger scale. The experiment has proven its value and is ready for broader implementation.
Pause
Put on hold. External factors or resources require a pause in the work.
Kill
Stop the project. Data clearly shows this path does not lead to value.