Show Notes
Rebecca Shaddix, a seasoned marketing executive and go-to-market strategist, joins Melissa Perri to explore the balance of acceptable mistakes in market strategy. Rebecca discusses the importance of defining acceptable mistakes to avoid unnecessary delays and to better align team efforts with strategic goals. This episode focuses on how acknowledging acceptable mistakes can accelerate development without compromising core objectives. Rebecca also addresses the overemphasis on latest trends like AI, stressing the importance of intentional use rather than succumbing to hype. She provides insights into managing internal pressures and steering conversations back to meaningful customer outcomes. If you want to learn how to effectively balance strategic goals with market pressures while maintaining a focus on customer value, tune in to gain practical insights from Rebecca’s experiences.
You’ll hear us talk about: 09:04 - The Concept of Acceptable Mistakes Rebecca explains what an acceptable mistake entails, offering examples such as opting for fewer customer interviews to accelerate go-to-market strategies without significant risk. 29:19 - Navigating AI Hype Discusses the cycle of trendy technologies and how AI has become a prevalent, though often superficial, part of product strategy, emphasizing the importance of intentionality. 15:32 - Product and Product Marketing Partnership Highlights the need for a bidirectional relationship between product management and product marketing, focusing on how they should collaboratively drive strategy and insights. Listen to the full episode for more in-depth discussions and insights.
Episode resources: Rebecca on LinkedIn: https://www.linkedin.com/in/rebeccashaddix/ Try Liveblocks: https://liveblocks.io/
Timestamps: 00:00 Intro 02:57 Dear Melissa 06:17 Defining acceptable mistakes in go-to-market 12:22 How product and go-to-market should collaborate 17:48 What data product and marketing teams should track 22:53 Turning competitive noise into strategy 29:21 Responding to AI pressure from the market 34:29 Crafting real differentiators, not buzzwords 39:29 Advice for future product marketers
More from Product Thinking
In this solo episode of the Product Thinking Podcast, Melissa Perri shares the results of the State of AI in Product 2026 survey of 309 product leaders, co-published with Product Circle. The tools are everywhere, but the work has not changed. Melissa walks through five findings and what they mean for you. Adoption is nearly universal, yet only a third of leaders say AI is strengthening their operating model. Melissa explains why the bottleneck is still upstream in discovery and decisions, and why AI acts as a multiplier rather than an equalizer, helping mature organizations and exposing the cracks in everyone else. She unpacks the dataset's largest gap, between executives who think an AI strategy exists and the PMs who never see it reach their work, and what leaders and PMs should do about it. Melissa also shares some news: the podcast is taking a pause after this episode, though she is not going far.
What does it actually take for experimentation to stick inside a product organization? In this compilation episode of the Product Thinking Podcast, Melissa Perri brings together three perspectives on the leadership behaviors, portfolio decisions, and iteration loops that separate real experimentation from theater.
Continuous discovery sounds simple and breaks down constantly in practice. In this compilation episode of the Product Thinking Podcast, Melissa Perri brings together three perspectives on what it actually takes to build the habit and why slowing down on discovery is still the fastest path to shipping the right thing. Teresa Torres, author of Continuous Discovery Habits, breaks down the structure underneath every method: outcome, opportunity, and solution. Teams doing this right never have to stop and replan. Christina Wodtke, lecturer at Stanford and formerly at Zynga, follows with the weekly playtesting rhythm she now teaches. Julia Austin, former senior lecturer at Harvard Business School, closes the episode by pushing back on the temptation to skip discovery in the age of AI. Her 80/20 rule: spend most of your time on foundation work, because the false security AI offers cannot replace real conversations with real customers.
What does it mean for a product to actually be “done”? Not code in production, but customers buying, using, and loving it. In this compilation episode of the Product Thinking Podcast, Melissa Perri brings together three product leaders to explore the systems that make whole product launches consistent. Trisha Price, then Chief Product Officer at Pendo, argues for shifting the team's definition of done from "code complete" to "whole product complete." She reframes product marketing as a strategic voice in discovery, not a translation layer at launch, and shares how cadences keep the work moving. Kate Towsey, an independent research ops advisor with experience at BBC and Atlassian, frames organizational knowledge as water that needs a dam to stop it leaking away. Jessica Soroky, then Senior Director of Product Operations at Pendo, closes with what whole product launch and cadences look like in practice.
AI is moving quickly from experimentation into real products, but in regulated and high-trust environments the stakes are much higher. In this compilation episode of the Product Thinking Podcast, host Melissa Perri brings together perspectives from multiple product leaders to explore what it really takes to ship AI responsibly when accuracy, trust, and risk management matter. You will hear from Maryam Ashoori, who works at IBM and was involved in building Watsonx. She explains what AI agents actually are, why large language models hallucinate, and how guardrails and human oversight help teams manage calculated risk as systems become more autonomous. The episode also features Magda Armbruster, Head of Product at Natural Cycles, and Jessica Hall, Chief Product Officer at Just Eat Takeaway. Together they share how embedding regulation, prioritizing data privacy, and being honest about cost, governance, and capability building can turn compliance and trust into enablers rather than blockers for AI-driven products.