Mario Rodriguez

In this episode of the Mob Mentality Show, join us as we delve into "The Discovery of Ignorance in Product Development: Time for a Smarter Way to Deliver Customer Value" with our special guest, Gary Cohen 🟦: https://lnkd.in/ggGMHyfu https://lnkd.in/gx2tXEu6 Drawing on his rich experience across diverse sectors—from government agencies to startups—Gary brings a wealth of insight into optimizing product discovery and delivery for true customer impact. ### **Key Topics Explored: The Product Discovery and Delivery Intersection** Even if a team is familiar with the 80/20 rule (e.g., where 80% of features go unused), how does it determine the right 20% to deliver? Gary shares strategies to tackle this challenge, advocating for a shift from traditional roadmap fixation to embracing dynamic, continuous discovery that keeps customer value at the forefront. Together, we examine the effectiveness of "strict" roadmap discipline (the practice of adhering to set timelines and objectives) and if it becomes a barrier to true innovation and customer value. We also briefly touch on the FAST framework and its relevance to product discovery, emphasizing how collective discovery through mobbing allows teams to stay closer to evolving customer needs. ### **The Role of GenAI in Modern Product Development** Is AI is reshaping product discovery? We explore how GenAI can be a collaborative asset for mobs, not only for code development but also for bringing value by uncovering insights into customer behavior and streamlining feature prioritization. We compare "introverted" AI and "extroverted" AI in mob environments and discusses how developing refined AI inquiry skills is key to leveraging AI’s full potential. From training mobs in nuanced prompt engineering to using AI to help drive a more lean delivery, we share ways to maximize AI's impact while maintaining expert human oversight. ### **Unpacking Behavioral Expectations: A Smarter Approach to Product Decisions** Beyond the technical, we look at the behavioral side of product monitoring, asking, “What responses do we expect from our users?” Gary emphasizes the importance of behavioral prediction in discovery, cautioning against information bias and providing ways to gauge responses to feature experiments in real-time. We highlight the importance of monitoring usage data and what it reveals about customer engagement—whether a feature elicits a “wow” or a mere “meh.” Chris Lucian

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New blog post: The little things that got me into stream processing (https://lnkd.in/gU7Ba5Ym)

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We're not always collaborating when we think we are. This is interesting approach to the scale/model which describes three levels between which each collaboration moves. https://lnkd.in/dWPjfSsX

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