Most product teams manage trade-offs. The best teams eliminate them. In this interactive workshop, participants apply Jobs-to-be-Done, TRIZ, and Generative AI to their own product challenges and leave with concrete solution concepts and a repeatable framework for solving tough innovation problems. Product teams are skilled at identifying customer needs and translating them into JTBD. The harder part is execution: building products that satisfy those jobs while navigating competing constraints. In this workshop, Bill and Jay show how JTBD and TRIZ work together to uncover constraints behind difficult product problems and systematically resolve them. While TRIZ is one of the most powerful innovation frameworks, it is often viewed as difficult to learn and apply. Traditional training can take days, and true mastery years. Most product professionals simply don't have the time. This workshop distills TRIZ into practical techniques product teams can apply immediately. Attendees will learn a simple method to translate JTBD statements into a format that enables TRIZ analysis. They then use a game-style technique to apply TRIZ principles to generate design concepts, resolve contradictions, and unwanted outcomes: all while working on the attendees' real-world challenges. In the second half, Jay demonstrates how Generative AI can dramatically accelerate TRIZ application, making advanced problem-solving accessible to every product manager--not just experts.

Most AI tools rollouts fail at the last mile. Not because the technology or tool underperforms, but because organizations treat adoption as a training problem instead of a behavior problem. This workshop introduces a behavioral science-driven framework that attacks adoption resistance at its root: how people actually make decisions, what sustains their engagement, and how new tools become automatic behavior. Participants will work through three progressive phases: First, they apply behavioral economics principles (choice architecture, loss aversion framing, default effects, and friction reduction) to redesign how AI tools are communicated and positioned within their organizations. Second, they design lightweight gamification mechanics (progress loops, achievement systems, social proof dynamics) that make adoption engaging rather than obligatory. Third, they architect habit formation systems using cue-action-reward loops that transform intentional AI use into daily automatic behavior.

Open Innovation only delivers value when it is executed through clear, repeatable, and scalable processes. At the BMW IT Tech Office, Open Innovation is designed as an end-to-end system that connects external ecosystems, internal R&D, and business demand through structured engagement models, decision gates, and execution pathways. This session breaks down the Open Innovation processes used to identify opportunities, engage partners, validate emerging technologies, and transition ideas into enterprise-ready solutions. Attendees will learn how external inputs--from startups, universities, and Big Tech--are systematically sourced, evaluated, and matched to real business challenges, while aligning with internal R&D maturity models and Technology Readiness Levels.

Leaders struggle to walk the tightrope between innovation risk and predictable revenue growth while at the same time being expected to do more with less and less. I’m going to share a framework for breaking free of that contradiction with an approach that provides 3 levers necessary for predictable growth acceleration: 1) fill your pipeline with the right opportunities, the ones that meet a real need that customers will pay to solve and that you have a right to win, 2) execute those opportunities brilliantly, and 3) create resilient processes that support your teams in that mission. Mike will briefly share 3 accelerators for each strategy and then provide a deep dive into three of the accelerators. Those include 1) a firewall based on customer value and what that really means, 2) An assessment for driving into profitable adjacent markets, and 3) an engaging exercise showing how a simple pipeline control mechanism can double the number of projects they get across the finish line. Attendees will be provided access to a set of free Excel-Based tools so they can pilot these concepts in their business.

Description coming soon.

Customer feedback is no longer confined to structured research studies or formal VOC programs. Today, customer needs are expressed continuously across product usage data, service interactions, sales conversations, online communities, and more. This feedback is often fragmented, unstructured, and increasingly interpreted through AI tools. For product and innovation teams, the challenge is no longer access to customer input, but knowing which needs to focus on to win in the marketplace. As AI accelerates data synthesis, innovation teams risk reacting to surface level signals rather than understanding the deeper needs that should guide product decisions. This workshop reframes Voice of the Customer as a decision making mindset rather than only a set of research methods. Participants will explore how AI is changing the way customer voice is captured and summarized, and why strong VOC thinking remains essential for interpreting and acting on insight. Through hands on, collaborative exercises, attendees will practice separating signal from noise to uncover the underlying why that drives innovation worthy needs. Participants leave with a tangible VOC Focus Brief they can immediately use to align teams, guide product direction, and make more confident, customer centric decisions. Key topics include: Why VOC is fundamentally about improving product decisions.