In the latest episode of the Corporate Explorer series, brought to you by Wazoku, we discuss the importance of balancing personal instincts with data and evidence in business decisions. Guests, Product Marketing Manager for Electric Vehicles & Customer Experience with Cadillac, Sarah Spoto and Vincent Ducret from ChangeLogic share their experiences of implementing experimental frameworks in China for General Motors. They emphasize the critical steps of assumption analysis, prioritizing hypotheses, and iterative experimentation, while also addressing challenges in corporate environments, such as maintaining cultural buy-in and celebrating failures as learning opportunities. Sponsored by Wazoku, the episode offers deep dives into innovation management, strategic risk-taking, and the use of connected collective intelligence to drive business success. 00:00 Introduction: The Pitfalls of Instinct-Driven Decisions 00:40 The Role of Data in Corporate Exploration 01:07 Sponsor Message: Wazoku's Innovation Ecosystem 01:56 Incubation and De-risking New Business Ideas 02:19 Meet the Corporate Explorers: Sarah Spoto and Vincent Ducraix 02:50 Sarah Spoto's Journey in Corporate Innovation 04:40 Vincent Ducraix's Background and Experience 06:55 The Business Learning Life Cycle Framework 08:10 The Importance of De-risking in Corporate Ventures 13:04 Challenges and Strategies in Corporate Experimentation 14:33 The Role of Leadership and Team Buy-in 21:08 Case Study: Experimentation in China 30:28 Final Thoughts and Contact Information Find Sarah here: Find Vincent here:     Key Takeaways This conversation explores business experimentation using a framework outlined in the Corporate Explorer book. Here are the key takeaways: Challenges of Corporate Innovation: Reliance on past experience can lead to failure in new ventures due to high uncertainties. Traditional methods like surveys might not capture the right data for untested ideas. The Business Experiments Framework: Identify Key Assumptions: List all critical assumptions about your new venture (customer value proposition,problem addressed, etc.). Prioritize Assumptions: Focus on the riskiest assumptions that could derail the project. Design Experiments: Create creative experiments to test these assumptions with real customer data (not just surveys). Collect Learnings: Analyze experiment results to validate or disprove assumptions. Iterate: Use learnings to refine your approach and potentially conduct new experiments on remaining assumptions. Benefits of Business Experiments: Reduces risk by validating assumptions before significant resource investment. Provides data-driven decision making for building the right product for the right customer. Challenges of Implementing Business Experiments: Balancing Speed and De-risking: There's a tension between demonstrating progress and taking the time to properly de-risk the project. Cultural Shift: Organizations need to embrace experimentation and see failures as learning opportunities. Team Buy-in: Both leadership and working-level teams need to understand the value of experimentation. Success Factors: Strong Leadership Support: Having advocates who understand and champion the approach is crucial. Cultural Investment: Educate teams on the process and celebrate failures as learning opportunities. Focus on Learning: Value the insights gained from experiments, even if they disprove assumptions. Example Implementation: Sarah Spoto's experience in China highlights the importance of setting the right incentives and building a cross-functional team for experimentation. Assumptions analysis is a valuable starting point, even if full experimentation isn't implemented. Overall, business experimentation offers a structured approach for de-risking new ventures and making data-driven decisions in the face of uncertainty.