Traditional risk registers are performative theater. Use a 'Learning Board' with three columns: 'Assumption,' 'Test,' and 'What We Learned.' This reframes risk management as a continuous discovery process and serves as a transparent communication tool for stakeholders, replacing bureaucratic documentation.
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Instead of creating a massive risk register, identify the core assumptions your product relies on. Prioritize testing the one that, if proven wrong, would cause your product to fail the fastest. This focuses effort on existential threats over minor issues.
To move beyond static playbooks, treat your team's ways of working (e.g., meetings, frameworks) as a product. Define the problem they solve, for whom, and what success looks like. This approach allows for public reflection and iterative improvement based on whether the process is achieving its goal.
Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.
Instead of stigmatizing failure, LEGO embeds a formal "After Action Review" (AAR) process into its culture, with reviews happening daily at some level. This structured debrief forces teams to analyze why a project failed and apply those specific learnings across the organization to prevent repeat mistakes.
To make risk management tangible, build specific tests for Value, Usability, Feasibility, and Business Viability directly into each epic. This moves risk assessment from a separate, ignored artifact into the core development workflow, preventing it from becoming a waterfall-style gate.
To avoid repeating errors during rapid growth, HubSpot used a 'Pothole Report.' This process involved a post-mortem on every significant mistake, asking how it could have been handled or what data was needed a year ago to prevent it, effectively institutionalizing learning from failure and promoting proactive thinking.
Formal slide decks for sprint readouts invite a "judgment culture." Instead, use an "open house" format with work-in-progress on whiteboards. This frames the session as a collaborative build, inviting stakeholders to contribute rather than just critique.
Before starting a project, ask the team to imagine it has failed and write a story explaining why. This exercise in 'time travel' bypasses optimism bias and surfaces critical operational risks, resource gaps, and flawed assumptions that would otherwise be missed until it's too late.
A sophisticated learning culture avoids the generic 'fail fast' mantra by distinguishing four mistake types. 'Stretch' mistakes are good and occur when pushing limits. 'High-stakes' mistakes are bad and must be avoided. 'Sloppy' mistakes reveal system flaws. 'Aha-moment' mistakes provide deep insights. This framework allows for a nuanced, situation-appropriate response to error.
Executives crave predictability, which feels at odds with agile discovery. Bridge this gap by making your learning visible. A simple weekly update on tested assumptions, evidence found, and resulting decisions provides a rhythm of progress that satisfies their need for oversight without resorting to rigid plans.