Coming soon · Book Profile
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In an age intoxicated by big data and algorithms, true insight into human behavior comes only from 'sensemaking'—rigorous cultural inquiry grounded in the humanities.
A profile of this book is on the way.
What it’s about
Sensemaking is a passionate defense of humanities-based thinking in a world obsessed with STEM, big data, and Silicon Valley's algorithmic promises. Drawing on nearly two decades of consulting with the world's largest corporations through ReD Associates, Christian Madsbjerg argues that numbers stripped of context obscure rather than reveal the truth about people. Through five principles—culture not individuals, thick data not just thin data, the savannah not the zoo, creativity not manufacturing, the North Star not the GPS—he shows how masters like George Soros, Ford's Mark Fields, architect Bjarke Ingels, and winemaker Cathy Corison achieve extraordinary results by immersing themselves in the rich reality of human worlds. Blending twentieth-century philosophy (Heidegger, Husserl, phenomenology) with vivid business case studies, the book makes the case that cultural knowledge is not a luxury but a competitive advantage—and that in the coming century, the hardest and most lucrative problems will be cultural ones only humans can solve.
The through-line
- Who it’s for
- A leader, executive, strategist, or thoughtful professional who wants to genuinely understand people, markets, and cultures in order to make wise decisions.
- The problem
- Their organization is losing touch with customers, markets are shifting nonlinearly, and data-driven approaches keep producing wrong conclusions. They feel they have 'lost their intuition,' are anxious and directionless, starved on a diet of abstract facts and figures.
- The plan
- Embrace culture, not individuals: study the shared worlds and contexts that shape behavior
- Gather thick data—moods, experiences, narratives—not just thin numerical traces
- Escape the zoo and observe human experience on the savannah using phenomenology
- Reframe your problem as a phenomenon and apply humanities and social-science theory
- Stay open in a state of grace to let creative insights come through you
- The payoff
- Recovered intuition and a finely honed perspective on where you are and where you are headed · The ability to make killer bets on human behavior and see nonlinear shifts before competitors · Work that is more truthful, more meaningful, and more profitable—for both bank account and life
See our guide
Related profiles we’ve built
Additional reading
- OpenAI Prompt Engineering Guide · OpenAI
Official documentation from the model provider on how to craft effective prompts, recommended by the author.
- Anthropic's Prompt Engineering Guide · Anthropic
Official guide from another major LLM provider, offering best practices for prompt design.
- dair-ai/Prompt-Engineering-Guide · DAIR.AI Community
A comprehensive, community-maintained GitHub repository covering a wide range of prompt engineering techniques.
- GPT Researcher · assafelovic
An open-source project that inspired the book's research summarization engine, recommended for further exploration of building production-grade LLM applications.
- Building Effective AI Agents · Anthropic
An article recommended by the author for a deeper understanding of the distinction between agentic workflows and autonomous agents.
- Model Context Protocol (MCP) Official Website · Anthropic and Community
The primary source for the MCP specification, tutorials, and ecosystem resources, essential for building or consuming MCP servers.
- FastMCP 2 Documentation · jlowin and community
The official documentation for the recommended Python SDK for building MCP servers, used in the book's example.
- Knowledge Graph Enhanced RAG · Tomaž Bratanič and Oskar Hane
Recommended by the author for readers interested in diving deeper into combining knowledge graphs with RAG.
- Database Management Systems · Gehrke and Ramakrishnan
Referenced in Chapter 2 as a resource for readers unfamiliar with the basics of SQL and Relational Database Management Systems (RDBMSs), which are foundational for the structured data analysis chapters.
- Survey of Hallucination in Natural Language Generation · Ji, Z., Lee, N., Frieske, R., et al.
Cited in Chapter 2 to explain the concept of 'hallucinations' in LLMs and to underscore the importance of verifying information provided by models.