The Bias Laundering Machine: Evolving "Data-Driven" Innovation

Introduction: A Practitioner's Journey

For years, I was a dedicated practitioner of the Outcome-Driven Innovation (ODI) methodology. I saw its power firsthand, both during my time working at Strategyn and in my work after. But over time, I also saw its limitations. I observed persistent problems with the approach—moments where the data felt confident, but the resulting strategic path felt… off. It needed to evolve, but I just didn't know how at the time.

This article is the answer I was searching for. It’s a forensic look at a systemic flaw not just in ODI, but in many "data-driven" innovation processes. It's a concept I call the "Bias Laundering Machine": a system that takes a small, flawed, and inherently biased qualitative input and uses the power of surveys and AI to create a dangerous illusion of objective, statistical truth.

This isn't about tearing down a methodology that has provided immense value. It's about building upon it. It's about identifying an unexamined flaw and proposing a more robust, principle-driven evolution that leads to truly defensible strategies.