Sensemaking by Christian Madsbjerg Summary

Sensemaking argues for integrating human observation and humanities-based analysis with data-driven decision-making. Madsbjerg challenges overreliance on algorithms, advocating five principles: understanding cultural context, prioritizing thick qualitative data, observing behavior in natural settings, trusting creative insights, and balancing human/technical tools. Examples range from Ford’s customer research to hostage negotiation tactics.

Business leaders, strategists, and professionals in marketing, product development, or organizational design will benefit. It’s particularly relevant for those seeking to counterbalance data-heavy approaches with human-centric frameworks. Madsbjerg draws from his consulting work with companies like Adidas and Chanel, making it practical for corporate innovators.

1. Culture: Analyze societal norms shaping consumer behavior.
2. Thick Data: Use qualitative insights from direct observation.
3. Savannah: Study subjects in real-world contexts, not labs.
4. Creative Revelation: Value sudden “Eureka!” moments.
5. Tool Balance: Combine human judgement with algorithms.

Thick data refers to nuanced, context-rich insights gained through ethnographic methods like interviews and behavioral observation. Unlike big data’s quantitative metrics, it reveals why people act through cultural/philosophical analysis—exemplified by Ford’s shift to customer experience-focused car design.

• The U.S. Naval Academy reinstating celestial navigation training alongside GPS.
• Investor George Soros using philosophical frameworks for market predictions.
• Hostage negotiator Chris Voss applying humanities training to secure journalist Jill Carroll’s release.

Madsbjerg warns that algorithmic overreliance creates blind spots—like misunderstanding cultural shifts or customer emotions. He cites education systems prioritizing STEM over liberal arts, risking workforce imbalance. However, he advocates hybrid models, not outright rejection of analytics.

Phenomenology—studying experiences as they occur naturally—anchors Madsbjerg’s methodology. For businesses, this means observing customers in authentic settings (e.g., retail environments) rather than labs. IKEA famously used this approach to redesign store layouts based on shopper behavior patterns.

Both emphasize human sciences in business, but Sensemaking expands the framework with philosophical rigor and contemporary case studies. While Moment introduced “thick data,” this book codifies systematic practices for applying it.

Some reviewers note Madsbjerg occasionally uses jargon (e.g., “hyper-reflection”), though he clarifies terms thoroughly. Others argue his consulting-focused examples may limit accessibility for non-corporate readers. Despite this, the book maintains a 4.3/5 Amazon rating for its actionable insights.

The book’s warning against AI overreach resonates amid ChatGPT-era debates. Its principles help organizations audit algorithmic decisions for cultural bias—a key issue in healthcare and hiring AI systems. BMW and Pfizer have recently cited the text in ethics training programs.

• “The savannah—not the zoo”: Prioritize real-world observation over controlled studies.
• “Machines don’t think. They rearrange”: Critique of AI’s limitations in creative problem-solving.
• “Culture eats strategy for breakfast”: Updated Peter Drucker axiom emphasizing context.

While Clear and Kahneman focus on individual psychology, Madsbjerg tackles organizational/cultural dynamics. Sensemaking complements these works by providing a macro-level framework for systemic change, making it ideal for team leaders rather than solo readers.