Product Mastery Now for Product Managers, Leaders, and Innovators Podcast Por Chad McAllister PhD arte de portada

Product Mastery Now for Product Managers, Leaders, and Innovators

Product Mastery Now for Product Managers, Leaders, and Innovators

De: Chad McAllister PhD
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Welcome to Product Mastery Now, where you learn the 7 knowledge areas for product mastery. We teach product managers, leaders, and innovators the product management practices that elevate your influence and create products your customers love as you move toward product mastery. To see all seven areas go to https://productmasterynow.com. Hosted by Chad McAllister, PhD, product management professor and practitioner.Copyright © Product Mastery Now and The Everyday Innovator™ · All rights reserved. Economía Exito Profesional Gestión Gestión y Liderazgo Marketing Marketing y Ventas
Episodios
  • 586: Is this the future of JTBD? – with Mike Boysen

    586: Is this the future of JTBD? – with Mike Boysen
    Apr 6 2026
    An outcome-driven innovation perspective on Jobs-to-be-Done Watch on YouTube TLDR In this episode of Product Mastery Now, I’m talking with innovation veteran Mike Boysen about making Jobs-to-be-Done (JTBD) practical, fast, and accessible thanks to AI-powered tools and frameworks. We revisit what JTBD really means, how it has evolved, why practitioners sometimes get stuck, and how AI helps drive cost-efficient, actionable customer insights. This episode is perfect for product managers looking to skip the noise and deliver genuine value efficiently. Introduction Most product leaders have heard of Jobs-to-be-Done (JTBD). Some of you have even tried it. But given all the benefits of JTBD, why are you not still using it? In this discussion, we are going back to basics to define what Jobs-to-be-Done actually is, and we are going to show you how to execute it faster than ever before. You will learn a simplified workflow for applying Jobs-to-be-Done that cuts through the noise. We will walk through how AI accelerates the process, so you can stop guessing and start building what customers actually need. Our guest is Mike Boysen, Managing Director of Disruptive Innovation. Mike is a veteran of the JTBD movement, having served as a Director at Strategyn alongside Tony Ulwick. He has spent years in senior consulting and innovation roles, and today he helps companies use AI to make Jobs to be Done practical, accessible, and fast. Summary of Concepts Discussed for Product Managers What is Jobs-to-be-Done?Mike clarifies the confusion around JTBD by outlining the various schools of thought, including marketing-based frameworks. Mike’s perspective on JTBD focuses on outcome-driven innovation (ODI). Within this framework, product managers seek to understand the outcome the customer is seeking. Unlike other schools of thought, ODI values disruption and looking outside the current paradigm. Why JTBD Efforts Fail:Many teams attempting JTBD get stuck at some point. Mike explains that without clear problem definitions and rigorous, hypothesis-driven models, JTBD research can become aimless. Especially in ODI, biases early in the process can compound, making outcomes hard to take action on. Three Paths to Innovation:Mike describes three approaches organizations can take to innovation: expanding to new personas/markets, sustaining and improving what exists, and pursuing disruptive, paradigm-shifting innovation. He notes the power of focusing on the job beneficiary, especially for B2B innovation. How AI Transforms JTBD:Mike’s workflow leverages AI to break down ideas, solutions, and industries to first principles, uncovering fundamental truths and mapping out the jobs, metrics, and outcomes efficiently. This approach massively reduces the time and cost of qualitative JTBD, making it accessible to companies of all sizes, not just the Fortune 500. No More JTBD Surveys?Mike argues that expensive, time-consuming JTBD surveys are often unnecessary, especially for greenfield or disruptive innovations. Instead, AI-driven job maps, first-principle analyses, and hypothesis-validation interviews quickly reveal which opportunities are worth deeper investment, saving time, money, and effort. Job Maps, Metrics, and Practical Tools:Mike explains that AI can generate job maps in minutes rather than weeks. These tools provide clarity for product teams, showing value, friction, or overservice in the customer journey. Useful Link Check out Mike’s Substack Innovation Quote “Spend the least to learn the most.” – Mike Boysen Application Questions How would you describe the job your product is hired to do?What biases or limiting beliefs might be holding your team back from re-imagining your product or process?Are there opportunities to use smaller, hypothesis-driven experiments rather than expensive or time-consuming surveys?How could AI tools streamline and focus your JTBD or customer discovery efforts?If you created a job map for one core customer outcome, what would the steps and frictions look like? Bio For over 25 years, Mike Boysen drove CRM strategy and digital transformation for Fortune 50 enterprises, earning top analyst recognition as a thought leader in the space. However, after observing the expensive failures of traditional innovation, his relentless search for “why” prompted a transition from CRM strategist to an Innovation Engineer. Today, as a leading expert in Jobs-to-be-Done (JTBD), Mike challenges industry “sacred cows” by employing a capital-efficient, deterministic methodology to uncover exactly what customers want. His engineering approach rests on three core pillars: applying First Principles Thinking to distill a problem down to its indivisible physical, digital, or economic truth to eliminate human bias; mapping the exact human executor’s 9-step chronological struggle using AI-powered tools to generate solution-agnostic Customer Success Statements (CSS) tied directly to those atomic truths; and ...
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    22 m
  • 585: Prompt-Eval-Iterate loop for AI-driven software development life cycles – with Avinoam Zelenko

    585: Prompt-Eval-Iterate loop for AI-driven software development life cycles – with Avinoam Zelenko
    Mar 30 2026
    How product managers can get the most out of AI-native development processes Watch on YouTube TLDR This episode, featuring Avi Zelenko, Principal Product Manager at Atlassian, explores how AI is transforming the traditional software development lifecycle (SDLC). Our discussion focuses on Atlassian’s Prompt-Eval-Iterate loop, using AI with PRDs, the creation and use of “golden datasets,” and the use of LLM judges to deliver higher quality AI products. Product managers will hear actionable insight into AI-native development processes and tips for involving cross-functional teams and customers in the journey. Introduction Is the traditional Product Requirement Document dead, along with the standard “Build-Test-Launch” cycle? AI-driven Software Development Life Cycles (SDLCs) are making changes in what has been standard practice. In this discussion we’ll explore the AI-native SDLC used at Atlassian. By the end of this episode, you’ll have a new framework to bring back to your team: The Prompt-Eval-Iterate loop. We’ll discuss why your PRD should be a “behavior contract,” how to build “golden data sets,” and how to use LLM judges to ship higher-quality software faster than ever. Our guest is Avinoam Zelenko. He is a Principal Product Manager at Atlassian, where he is currently leading the transition to AI-native development for Confluence. With a career spanning leadership roles at LinkedIn and Feedvisor, and years spent teaching the next generation of PMs at Product School, he knows exactly how to bridge the gap between high-level AI strategy and day-to-day execution. Summary of Concepts Discussed for Product Managers Evolution of SDLCs:We discuss the limitations of linear Software Development Life Cycle (SDLC) approaches like “build, test, launch” in the era of AI. Avi explains that product managers must now co-own quality, moving beyond handoffs and static PRDs, as AI-driven features require deeper, ongoing commitment. Prompt-Eval-Iterate Loop:Atlassian’s approach starts with collaborative prompt design and exploration, not lengthy specs. Instead of guessing feature outcomes upfront, teams build out golden datasets and use rapid iterations to let real data and metrics refine both the product and its requirements. Golden Datasets:A golden dataset is a living collection of well-curated real-world examples and edge cases from customers. It helps teams define what “good” looks like and allows continuous improvement of AI features, with new findings fed back into the dataset for better output and coverage. Maintaining Customer Proximity:Avi emphasizes that core product management tasks like customer interviews and understanding unmet needs remain vital. Atlassian leverages AI agents to automate customer feedback loops, enabling PMs to connect with more users and gather data on a much larger scale. PRD as a Behavior Contract:The Product Requirements Document (PRD) evolves into a behavior contract, encoding what the AI should do in specific scenarios, along with clear metrics, safety guardrails, and references to the golden dataset. This contract is drafted after substantial hands-on exploration and iteration, keeping specs grounded in reality. Evals and LLM Judges:Quality assurance uses two types of evals: deterministic checks (yes/no, hard criteria) and LLM judges (AI-based evaluators) for assessing nuances like faithfulness to source material, narrative, and tone. These automated evals create quality gates for each product milestone. Collaboration and Transparency:Atlassian encourages cross-functional teams—from engineering and support to sales and marketing—to participate early in the process. This open, inclusive approach gathers a breadth of perspectives and aligns objectives across the organization. Useful Links Connect with Avi on LinkedInLearn more about Atlassian Innovation Quote “Sometimes immersing works better than observing.” – Avi Zelenko Application Questions How can your team evolve its SDLC to better integrate AI-driven features and ongoing iteration?What would a “golden dataset” look like for your product, and how would you begin building it?In what ways can you involve more customers, support, sales, or marketing in defining the behavior of AI features?How does shifting from a static PRD to a “behavior contract” change your collaboration with engineering and other teams?What new skills or practices must PMs develop to balance automation with human judgment in AI product development? Bio Avinoam “Avi” Zelenko is a Principal Product Manager at Atlassian, where he leads product strategy for Confluence, the company’s flagship collaboration platform. With more than 16 years of experience in B2B SaaS, he has built and scaled products at companies including LinkedIn, where he helped shape the feed experience for hundreds of millions of users, as well as LivePerson, ClickTale, and Feedvisor, spanning intelligent chat, analytics, and ...
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  • 584: Practical product experimentation without special tools – with Jeff Lash

    584: Practical product experimentation without special tools – with Jeff Lash
    Mar 23 2026
    Case studies of scrappy product management experiments Watch on YouTube TLDR In this episode, I’m interviewing Jeff Lash, VP of Product Management at Insperity, to demystify product experimentation for product managers. Jeff unpacks scrappy ways to test assumptions, mitigate risk, and maximize learning, sharing case studies from his work in B2B product management. We discuss real examples, key principles for experimentation, and navigating organizational dynamics to drive informed product decisions. Introduction Most product managers think experimentation requires expensive A/B testing software, a team of data scientists, and thousands of users. They’re wrong. You can and should be testing your riskiest assumptions today, and doing so in ways that are fast and frugal. By the end of this episode, you’ll have a toolkit of testing methods that you can deploy immediately. Our guest is the perfect guide for this. Jeff Lash is the Vice President of Product Management at Insperity. Before that, he spent nearly a decade at Forrester and SiriusDecisions, where he advised the world’s top product organizations on exactly these strategies. He is the author of the long-running How To Be A Good Product Manager blog and a product management veteran who has transitioned from practitioner to researcher, analyst, and adviser, and then back to the front lines of product leadership. Summary of Concepts Discussed for Product Managers The Purpose of Experimentation:Experimentation prevents product managers from jumping to solutions by validating that they’re solving the right problems with the right solutions. Jeff emphasizes that effective experimentation requires humility and an openness to learning. This approach helps avoid costly mistakes of building products based on unverified assumptions and mitigates business risk. Fast, Frugal Experiments:Jeff explains that experiments should deliver maximum learning for minimum investment. Experiments should be built upon foundational customer research and always include measurable objectives. He reminds product managers not to rely solely on digital tools, especially in B2B contexts where the customer base is smaller and sales cycles are longer. Case Studies of Product Experimentation:Through several case studies, Jeff Lash illustrates experimentation methods: Using mock-ups for concept testing: Before building a new data-reporting SaaS, a product team manually created mock-up sample reports and pitched them to clients. The low demand they discovered helped avoid unnecessary development.Sales-Driven Product Testing: Collaborating with sales, an organization defined clear success metrics, launched a pilot with a limited customer group, and used real buying signals (not just sales enthusiasm) to validate new offerings, minimizing risk and maximizing buy-in.Content Access Limits: Unsure about the right threshold for content access in a subscription product, a company temporarily gave all customers unlimited access to gather data on which content they were accessing, later allowing them to set limits that balanced user delight and business goals.Testing with a Sales Presentation: In response to sales insisting there was a market for a new product, a product team created a sales pitch deck. After several meetings and pitches, they found zero customer interest, which revealed the real gap was not product, but access to the right buyer. This low-cost experiment saved significant time and resources by preventing the team from building an unwanted solution. Navigating Organization Dynamics:Not all experiments yield the result everyone wants. Jeff discusses how to align teams around experiment outcomes—even unpopular ones—and communicate evidence while managing executive or sales pressure. He stresses the importance of cross-functional alignment, especially in B2B, and framing experiments by the core questions they’re meant to answer. B2B vs. B2C Experimentation:While B2C may allow rapid, large-scale testing, B2B experimentation requires more coordination with sales, legal, and customer success to avoid customer confusion or contractual risks. Building internal buy-in and clear communication is critical for successful, reversible tests. Useful Links Visit Jeff’s websiteRead Jeff’s blog, How To Be A Good Product ManagerConnect with Jeff on LinkedInLearn more about InsperityListen to episode 127: B2B product management – with Jeff Lash Innovation Quote “It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself.” – Leon Megginson Application Questions What assumptions in your current product strategy could be tested with a simple experiment this quarter?How does your team define success criteria for experiments? Who needs to be involved in that definition?Have you ever faced resistance to ...
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    43 m
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