27 Oct 2026 Defining What Good Looks Like
"Explores AI product development challenges, emphasizing measurable outcomes, customer-centric evaluation, cross-functional collaboration, and real-world impact over technical metrics."
More All Things Product episodes

Published 13 Oct 2026
Duration: 00:17:51
"Continuous product discovery training requires habit-building and practice, not just short sessions, as teams often underestimate the depth of skill development needed for mastery."
You've got twelve people and one hour booked at next week's offsite. Can someone teach your team to do continuous discovery in that hour? Teresa Torre...
The podcast discusses the limitations and realistic outcomes of short training sessions, particularly in the context of complex skills like continuous product discovery and managing up. It emphasizes that while one-hour talks or brief workshops can inspire and introduce high-level concepts, they cannot instill deep expertise or lasting behavioral change. Continuous discovery, for example, involves mastering 13 distinct habits that require ongoing practice, repetition, and a supportive environment - elements that cannot be achieved through a single session.
The discussion also highlights common misconceptions among teams and organizations about what training can deliver. Many request training without fully understanding their skill gaps, leading to mismatched expectations. Short sessions may offer awareness or motivation for small behavior changes, but they cannot replace long-term coaching or address systemic barriers such as lack of leadership alignment or access to customers. To be effective, training must be part of a broader, structured learning journey that includes practice, feedback, and organizational support.
What if you ran a 30-minute discovery audit with 3 team members before your next client talk?
What if you redesigned your next 1-hour talk to end with 30 minutes of facilitated Q&A?
What if you packaged a single continuous discovery habit into a 7-day micro-challenge for solo developers?
27 Oct 2026 Defining What Good Looks Like
"Explores AI product development challenges, emphasizing measurable outcomes, customer-centric evaluation, cross-functional collaboration, and real-world impact over technical metrics."
6 Oct 2026 Recorded Conversations
"AI and technology's role in recording conversations raises ethical, psychological, and social challenges, including trust, evolving opinions, memory inaccuracies, and the impact of transcripts on relationships and accountability."
29 Sept 2026 Unpacking Innovation
"Innovation in product development prioritizes solving user problems through simplicity and usability, balancing novel solutions with proven methods to avoid unnecessary complexity, driven by cross-functional collaboration and real user needs."
22 Sept 2026 Trash Can Tracking
Recommended: What you throw away shows you what you value.
"Trash can tracking measures product feature success by analyzing discarded ideas, emphasizing early experimentation, clear decision-making, and leadership's role in fostering a culture that discards misaligned or low-value solutions."
15 Sept 2026 Delivery Isn't Free
"AI cuts software costs but demands skilled engineering for maintenance, security, and scaling, with rapid feature expansion risking spaghetti code and inefficiency - production-grade AI products require traditional best practices and significant refinement."