エピソード

  • PM Powerskills- Communication

    PM Powerskills- Communication
    2026/08/18
    Ever leave a meeting thinking you were crystal clear, only to find out everyone walked away with a different interpretation? In this episode, Joe Ghali and Ryan Cantwell dig into why communication is one of the most important power skills in product management, and why being clear is about more than saying the right words. They talk through how to tailor your message for executives, peers, and teams, why storytelling often lands better than a wall of facts, and how preparation, listening, and simple communication frameworks can help create real stakeholder alignment. You’ll also hear the break dancer versus ballet dancer story that shows just how much the right analogy can change the way a message lands. If you want to spend less time wondering whether people understood you and more time creating clarity, trust, and action, pull up a chair on the porch and sharpen the way you communicate. Time Stamped Notes: Introduction [00:00] Episode setup - Joe and Ryan introduce communication and presentation skills as the next Product Porch power skill. Personal Stories and Communication Revelations [00:50] Coaching miscommunication - Joe shares how “be aggressive” led his softball team to hear something very different. [03:17] Understanding matters - Communication is not what you say, it is what the other person understands. Why Communication is Critical for Product Managers [04:03] Beyond presentations - Communication skills matter in formal meetings and quick everyday conversations. [05:18] Cross-functional reality - Product managers rely on communication to align teams across nearly every part of the business. Why Communication is Hard [09:27] Invisible progress - Communication can feel less satisfying because the results are harder to see than tangible deliverables. [11:13] Play it back - Ryan suggests asking people to repeat what they heard to test whether the message actually landed. Communication Tips and Strategies [12:20] Stories beat facts - Joe explains why storytelling helps ideas stick better than isolated information. [16:33] Tailor the message - Keep the vision consistent while changing how you communicate it for each audience. The Break Dancer vs Ballet Analogy Story [16:51] Wrong presentation - Ryan recalls using an ROI-heavy pitch that failed to win support. [18:42] One image lands - A break dancer and ballet dancer metaphor turns a complicated problem into something the room immediately understands. [18:59] Audience translation - The lesson is not charisma, it is knowing what your audience cares about and speaking their language. Audience-Specific Communication [19:52] Match the audience - Joe explains why different stakeholders need different levels of detail. [20:49] Executive brevity - Simple infographics and short presentations help leaders grasp the point quickly. Lightning Round: What Great Communicators Do [23:28] Storytelling over statistics - Great communicators give information emotional weight and connect it to a real problem. [23:44] Simplicity wins - Plain language makes communication easier to understand. [24:03] Create clarity - Start with the takeaway instead of overwhelming people with information. [24:32] Preparation builds confidence - Knowing the audience and doing the homework makes clear communication easier. [25:47] Listen first - Great communicators listen before they speak. Communication Frameworks [26:27] One, two, three - Ryan introduces a simple framework built around one thing, two types, and three steps. [27:54] Framework in action - Joe practices the approach live using cars as the topic. How to Practice Presentations [29:08] Practice everywhere - Everyday conversations can become low-stakes opportunities to build communication skills. [30:30] Record yourself - Ryan recommends recording presentations and reviewing how you actually come across. [31:01] AI practice partner - Voice tools can simulate an audience and give you another way to rehearse. [31:56] Be relatable - Joe shares advice about making presentations feel natural and authentic. Handling the Unexpected: Improv Skills [33:43] Prepare to improvise - Strong communicators practice how to respond when presentations get interrupted or redirected. [35:00] Listen and build - Improv techniques help communicators respond to ideas instead of shutting them down. Key Takeaways and Conclusion [36:26] Communication compounds - Better communication can improve influence, alignment, and career growth over time. [37:05] Alignment over information - Sharing information is not the same thing as creating shared understanding. [37:58] Closing thoughts - Joe and Ryan wrap the conversation and close out the episode. Help keep the Product Porch lights on by giving at https://www.patreon.com/TheProductPorchJoin our email list and never miss an episode at theproductporch.com
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    39 分
  • AI Tools Don’t Make You a Better Product Manager

    AI Tools Don’t Make You a Better Product Manager
    2026/08/04
    Does knowing how to use AI make you better at product management, or just better at using another tool? In this episode, Joe Ghali, Ryan Cantwell, and Todd Blaquiere dig into what AI competency should really look like for product managers. They talk about why completing training or knowing your way around ChatGPT, Claude, Figma, GitHub, or SQL is not the same as having sound product judgment. The real test is whether those tools help you make better decisions, create clearer outcomes, and focus on problems worth solving. The group also gets practical about how deep PMs should go with technical tools, why AI has made ideas cheap, and why leaders should measure impact instead of usage. More prompts and more tokens do not automatically mean more value. Good judgment still matters. So does knowing when to use the tool, when not to, and how to tell whether the output is any good. If your team is racing to prove it is “AI ready,” pull up a chair on the porch, rethink the scorecard, and listen for what still separates strong product work from shiny tool use. Introduction and Episode Setup [00:00] AI math - Todd argues that AI hasn’t changed how teams define and measure value. [00:31] Depth question - Joe introduces how deeply product managers should understand AI and technical tools. [01:15] Tooling debate - A conversation with a colleague sparks the episode’s central question. Tools Don’t Make the Product Manager [01:52] Jira test - Writing user stories in Jira doesn’t make someone a product owner. [02:13] Roadmap test - Knowing Aha! doesn’t automatically make someone a strong product manager. [02:40] AI extension - Joe asks whether using Claude to create product artifacts changes the answer. [04:01] Tools as enablers - Ryan explains why knowing the job matters more than owning the tool. [05:52] Stethoscope analogy - Todd shows why access to professional tools doesn’t create professional judgment. Measuring Real AI Competency [06:16] Competency challenge - Organizations want AI-literate PMs but struggle to define or measure proficiency. [06:54] Checkbox training - Course completion can create the appearance of competency without proving practical skill. [08:17] Experience matters - Todd argues that real capability comes from using tools on real work. [09:07] Learning the edges - Video editing teaches Todd how practice reveals a tool’s limits. [10:14] Two kinds of knowing - Watching training and building through experience produce very different capability. Outcomes, Discernment, and ROI [11:14] Evaluator problem - Leaders with limited AI knowledge may still be responsible for judging its use. [12:24] Vanity metrics - Counting tokens or tool activity says little about whether better work happened. [12:48] Pivot-table magic - Ryan recalls how basic Excel knowledge once looked like wizardry to executives. [13:28] Confident outputs - Polished AI answers can mislead leaders who don’t know how to challenge them. [14:31] Discernment skill - Product people must learn to evaluate AI output, not simply generate it. [15:07] Same equation - Teams should define the objective, apply AI, and measure whether results improve. [17:42] Tool business case - AI software should face the same cost and ROI questions as any other tool. AI, Strategy, and Product Focus [19:44] Product replacement debate - Ryan rejects the idea that AI can simply turn engineers into product managers. [20:13] Build everything - Todd imagines removing product oversight and letting every function create its own tools. [21:38] Strategy means focus - Ryan explains why unrestricted building is the opposite of a strategy. [22:17] Output isn’t growth - More products and features don’t guarantee revenue, adoption, or customer value. [23:18] Do less to grow - The group argues that focus often creates more growth than added activity. [24:13] Ideas are cheap - AI makes concepts and prototypes plentiful, but worthwhile problems remain scarce. [25:27] Making the diamond - Product judgment applies focus and pressure to problems worth solving. [26:29] Building isn’t enough - Successful bets still need marketing, sales, and operational support. [27:40] Roles versus titles - Todd defines the accountabilities a product organization needs regardless of job titles. Depth Check: How Deep Should PMs Go? [29:14] Depth Check begins - Joe asks how deeply PMs should understand specific technical tools and concepts. [30:04] GitHub knowledge - The hosts debate whether PMs need branching and merge-conflict experience. [31:19] Conceptual fluency - PMs should understand GitHub workflows without necessarily managing repositories themselves. [35:25] Figma prototypes - PMs can go deeper in prototyping because visuals help stakeholders react to ideas. [36:41] Design boundaries - Designers still protect design systems and consistency while PMs create early concepts. [37:19] SQL depth - AI can write queries, but PMs still need enough knowledge to assess requests ...
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    44 分
  • PM Power Skills - Emotional Intelligence

    PM Power Skills - Emotional Intelligence
    2026/07/21
    Join us on the porch for our series on Product Manager Power Skills. These are the skills that help good product managers become great ones. They stay valuable across tools, processes, and product types, and building them can help you grow your impact and your career. What happens to product managers when AI gets better at more of the work? In this episode, Joe Ghali and Ryan Cantwell dig into emotional intelligence and why it may be one of the most important power skills for product people right now. They talk through what EQ is, why it matters more as AI takes on more execution, and how product managers can get better at reading the room, managing their own reactions, and understanding what other people are really responding to. The conversation gets practical fast. Joe and Ryan share stories about stakeholder resistance, low-EQ behavior, and the trap of confusing empathy with people-pleasing. They also break down how to build emotional intelligence through curiosity, feedback, vulnerability, and simple reflection after tough moments. If you want to sharpen the human side of your product craft, handle pressure better, and bring stronger judgment to the moments that matter, pull up a chair on the porch, think about the last hard conversation you had, try one of these EQ practices in your next meeting, and see what changes. Introduction and Setting the Stage [00:00] Episode intro - Ryan opens with the EQ theme and frames it as a PM power skill. [00:51] Morning porch - Joe and Ryan joke about the unusual morning recording and Todd missing the session. [01:39] Power skills series - They connect this conversation to the broader PM Power Skills series. Why Emotional Intelligence Matters Now [02:02] Why EQ now - Joe asks why emotional intelligence matters more in product management today. [02:12] AI can’t do this - Ryan explains that AI can assist with work, but not relationships or empathy. [03:02] Suspension system analogy - EQ is framed as the system that helps you handle pressure without breaking. [04:20] Defining EQ - Ryan defines emotional intelligence as noticing and responding well to emotional dynamics. Can Emotional Intelligence Be Built? [06:22] Trainable skill - Ryan argues EQ can be developed through practice, like reps in the gym. [07:25] Running start - Joe notes some people seem to develop EQ more naturally than others. [09:57] Common misconception - Ryan shares why making everyone happy is not the same as EQ. [10:25] The jar label - They unpack why feedback matters when judging your own emotional intelligence. Reading the Room and Building EQ [08:30] Painful presentation - Joe tells a story about watching someone fail to read the room. [09:48] Notice then respond - Ryan describes EQ as the bridge between noticing and responding. [11:30] Vulnerability first - Joe says growth starts with being open to feedback and willing to improve. [12:18] Attitude over aptitude - They argue curiosity and coachability matter more than polish alone. [13:02] Empathy in practice - Ryan reframes empathy as understanding someone, not simply agreeing with them. Stakeholder Empathy in the Real World [14:06] Curiosity muscle - Ryan explains that asking simple “why” questions helps build EQ anywhere. [15:20] FinTech rollout - Joe shares how EQ helped him navigate resistance during a major product launch. [16:23] Mirroring concerns - He explains how listening and reflecting concerns changed stakeholder reactions. [17:34] Turning skeptics - The conversation shows how emotional understanding can shift resistance into support. Managing Low-EQ Stakeholders [18:33] Hardest part - Joe admits low-EQ people are often the most frustrating to work with. [19:50] Control yourself - He reflects on learning not to take stakeholder reactions so personally. [21:22] Don’t fix them - Ryan says the goal is not changing people, but staying clear and grounded. [21:59] Find alignment - He recommends centering discussions on shared problems and desired outcomes. [22:55] Don’t escalate - The key is noticing their state without mirroring it back emotionally. Empathy, Boundaries, and the Dark Side of EQ [23:04] Empathy vs EQ - They clarify that empathy is part of emotional intelligence, not the whole thing. [23:35] Business still matters - Joe explains that PMs still need to prioritize, say no, and make tradeoffs. [25:18] Not people-pleasing - Ryan draws a line between understanding people and trying to make everyone happy. [26:07] EQ for evil - They explore how emotional intelligence can also be used in self-serving ways. [29:01] Multiplier skill - Ryan argues EQ strengthens good judgment and product sense rather than replacing them. Using AI to Build EQ [30:24] AI as coach - Ryan suggests using AI to reflect on meetings and practice better responses. [31:11] Simulate reactions - He recommends asking AI to role-play stakeholders before tough conversations. [31:31] Poke holes first - Joe shares how he uses AI to pressure-test decks ...
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    36 分
  • Why Product Management Still Matters in the Age of AI

    Why Product Management Still Matters in the Age of AI
    2026/07/07
    Why do so many teams still misunderstand what product management is actually for? In this episode, Joe, Ryan, and Todd take on a question that keeps showing up in the age of AI: if engineering can build faster than ever, do we still need product managers? The answer is yes, but probably not for the reasons a lot of people think. They dig into why product so often gets treated like project management, ticket writing, or an expensive wrapper around engineering, and why that misses the point. This conversation is really about the value of product management. Not speed. Not ceremonies. Not pushing features through the system. Real product work is figuring out what is worth solving, asking better questions, and connecting customer value to business outcomes. If you’ve ever had to explain your role, push back on feature factory thinking, or help your company see product more clearly, pull up a chair on the porch, challenge a bad assumption, sharpen how you talk about your work, and listen in. **Welcome to the Porch** [00:00] Build vs outcomes - Todd jokes that building 100 products is easier than hitting a revenue target [00:29] Core question - Joe frames the episode around why companies still need product managers **Why This Question Is Back** [01:28] AI speeds delivery - The team explains why faster engineering makes leaders question product’s role [02:13] Building is not the hard part - Todd argues the real challenge is choosing the right problems [02:42] Misunderstood function - Product is often framed as project management or message carrying **What Product Gets Mistaken For** [03:28] “The fixer” story - Ryan shares a customer meeting that exposed a bad read on product’s purpose [04:36] Giant waste of money - Todd says if product only relays requests, the function should not exist [06:04] Wrong metrics - Measuring product by speed and output sets up the wrong expectations **AI, Claude Code, and the Feature Factory Trap** [07:08] Prototype to production gap shrinks - Joe explains how AI makes execution feel much closer [08:21] Decoupling headcount from growth - Ryan shares how leaders are pushing for more output with fewer people [09:48] Building gets easier - Todd says coding is now the easiest part of the job [10:19] Build trap warning - More product can be built than ever, but that does not create value by itself **Game Segment: What Product Is Not** [12:16] On-time delivery confusion - Getting projects done on time should not define the PM role [13:12] Managing engineers - Product works with engineers, but engineering managers lead engineers [13:36] Business case inputs - PMs help shape the case, but the case itself should be built with the team [14:56] Sales requests are not strategy - Product is not there to blindly build what sales asks for [16:38] Ask why - Real product work starts when PMs move past order taking and investigate the problem [18:23] Budget tradeoffs - PMs need to understand budgets without owning project control [18:38] Quality role - Product has a role in acceptance and validation, but not sole ownership of QA [19:16] Escalation trap - Ryan pushes back on product being treated like the default complaint handler [20:12] Execution wrapper - Joe sums up the anti-pattern as an expensive wrapper around engineering **The Hammer Analogy** [21:40] Wrong tool, wrong job - Todd compares product to a hammer being misused to cut branches [22:37] Ticket writers at risk - Joe connects the analogy to AI and the feature factory model [23:08] What survives - PMs who decide what is worth solving stay valuable **What Product Is Actually For** [23:15] Customer value to business outcomes - Todd defines the real job of product in one line [23:57] Blast radius of every bet - Every product decision affects sales, marketing, support, and success [24:45] Find the right yes - Great PMs work through the no’s to place better bets and align the company **How to Change Perception** [26:04] Take the harder path - Todd says PMs have to choose real product work over easy order taking [26:50] Hold yourself to outcomes - Measure value even when leadership only asks for features and velocity [27:46] Know your audience - Ryan says product has to show stakeholders it cares about what they care about [28:35] Start where the company is - Todd explains why product change has to happen in increments [29:47] Solve a shared business problem - Ryan argues this is how product earns credibility across functions **Change Management and the Product Blueprint** [32:41] Show, do not declare - Joe frames this as change management, not just a process announcement [33:15] Execution friction vs thinking friction - AI speeds shipping, but not judgment or strategy [33:52] Product blueprint - Todd describes mapping the full product job so leaders see how small development really is [34:29] Business skills matter - Ryan argues it is easier to teach PMs AI than to teach engineers deep business context **Homework and Closing ...
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    38 分
  • How to Show AI Value

    How to Show AI Value
    2026/06/23

    How do you get leaders to keep backing AI exploration when they’ve already invested, but the biggest upside still isn’t fully clear yet? In this episode, Joe Ghali, Ryan Cantwell, and Todd Blaquiere dig into a listener question about one of the hardest parts of AI adoption in product teams: justifying the paths, gains, and possibilities that do not show up as a flashy feature or an obvious ROI line right away.

    They talk through the real challenge product managers are facing now. Leaders have already said yes to AI. They have approved the tools, the licenses, and the experimentation. But now they are looking back and asking what that investment has delivered. The conversation unpacks how to make the case for time and space to explore AI potential while still tying the work to things leadership cares about, like cost, revenue, capacity, and smarter decisions. They also break down how to communicate early wins, how to show progress before the full upside is known, and why invisible AI value still matters when it helps the business move faster and work better.

    If you are trying to earn more room to explore AI, defend the investment already on the table, or tell a better story about what the work is producing, pull up a chair on the porch, listen for the signals leaders care about most, pick one meaningful gain your team can show today, and use it to make the next conversation stronger.

    Time Stamped Notes:

    The Question That Started It All
    [00:00] Intro + newsletter plug – Safe “Pixar movie” analogy for how leaders interpret messaging.
    [00:38] Listener question – How do you prove AI is worth it when ROI isn’t obvious yet?
    [01:56] Core tension – AI value is real but invisible because it’s embedded in workflows, not shipped as features.

    Why AI Value Is Hard to Show
    [03:42] Two problems – Measuring AI value vs communicating it to leadership.
    [04:32] Communication gap – Leaders expect visible outputs, not invisible workflow improvements.
    [05:07] Executive lens – Everything ultimately gets reduced to revenue and cost.
    [06:05] Growing skepticism – More AI projects are being questioned or abandoned due to unclear value.
    [06:51] Cost risk – AI tools and subscriptions quietly add up without clear ROI.
    [08:27] “So what?” moment – Efficiency gains exist, but leadership wants business impact.

    Moving from Efficiency to Real Value
    [09:48] Maturity shift – From experimentation → operational measurement → financial impact.
    [10:57] Turning time into value – Efficiency becomes either more output or fewer resources needed.
    [11:50] Headcount example – AI can remove future hiring needs and create real cost savings.
    [12:26] Baselining – You need a starting point to prove anything has changed.
    [13:14] Start small – Focus on one meaningful problem instead of measuring everything.
    [13:59] Estimates are okay – Directional impact is enough to start building credibility.
    [14:12] Finance partnership – Helps validate and strengthen assumptions.

    The Real Problem: Invisible AI
    [14:44] Invisible AI – Leadership doesn’t see “engine improvements,” only visible outputs.
    [15:30] Expectation gap – Leaders expect obvious, reportable AI wins.
    [16:10] Bottom-line vs top-line AI – Cost savings vs new revenue opportunities.
    [17:08] Investor lens – Unit economics matter more than features or tools.
    [18:12] Scalability – AI becomes valuable when it improves cost structure or leverage.
    [19:44] What to lead with – Pick the metric that gets executive attention.

    How to Communicate AI Value So It Lands
    [21:23] Leading vs lagging indicators – Cycle time and rework must connect to financial outcomes.
    [25:56] Rework reduction – Builds trust and improves downstream execution.
    [27:32] Storytelling discipline – You have to repeatedly connect AI work to business value.
    [30:20] Internal optimization – Identify high-cost, low-value work and target it with AI.
    [31:16] Hiring impact – Efficiency gains translate into real hiring and capacity decisions.
    [32:50] Decision tools – Simple cost-benefit thinking helps prioritize AI investments.
    [34:38] Core rule – Always lead and end with financial impact, not tooling.
    [37:55] Final takeaway – PMs are translators between AI capability and business value.

    Help keep the Product Porch lights on by giving at https://www.patreon.com/TheProductPorch

    Join our email list and never miss an episode at theproductporch.com
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    40 分
  • PM Power Skills - Critical Thinking

    PM Power Skills - Critical Thinking
    2026/06/09

    Join us on the porch for our series on Product Manager Power Skills. These are the skills that help good product managers become great ones. They stay valuable across tools, processes, and product types, and building them can help you grow your impact and your career.

    What happens when a product manager gets really good at moving fast… but stops questioning their own thinking? In this episode, Todd Blaquiere, Ryan Cantwell, and Joe Ghali dig into critical thinking as a power skill for PMs and why it matters more than ever in a world full of AI, strong opinions, and easy answers. They break down how better thinking helps product managers ask sharper questions, spot weak assumptions, consider other points of view, and catch second-order consequences before they turn into expensive mistakes. They also get practical about how to use AI the right way: not as a replacement for judgment, but as a tool to challenge your thinking and make your decisions stronger.

    If you want to make better product decisions, earn more trust, and avoid the kind of mistakes that look obvious in hindsight, pull up a chair on the porch, pressure-test your own thinking, and give this one a listen.

    Time Stamped Notes:

    What Critical Thinking Means
    [00:00] Power skills series – Critical thinking opens the new Product Porch power skills series.
    [01:13] Working definition – “Thinking about thinking” as a path to better judgment.
    [01:53] Reasoning model – Paul and Elder framework connects directly to product work.

    Better Product Decisions
    [03:55] Point of view – Strong decisions require multiple stakeholder perspectives.
    [06:17] Consequence mapping – First-order and second-order effects shape product outcomes.
    [08:19] Assumption testing – Five whys helps expose weak reasoning early.
    [09:10] Thinking discipline – Frameworks and discovery habits create more defensible decisions.

    Standards and Maturity
    [11:27] Intellectual standards – Clarity, accuracy, relevance, logic, and fairness improve product thinking.
    [15:26] “So what?” test – Data needs meaning, not just volume.
    [20:38] Thinker stages – PM growth moves from unreflective thinking to practiced judgment.
    [23:02] PM maturity range – Most product managers fall between challenged and practicing thinker.

    AI and Critical Thinking
    [22:27] AI as challenger – AI can pressure-test ideas and surface blind spots.
    [23:47] Work slop risk – Polished output can hide shallow thinking.
    [26:58] High AI literacy – Better outcomes come from critical use, not passive reliance.
    [29:54] Final judgment – Product decisions still require human context and trade-offs.

    Building the Skill
    [32:27] Practicing thinker habits – Better questions, broader evidence, stronger reasoning.
    [33:34] Postmortem habit – Retros reveal where judgment held up or broke down.
    [35:35] Power skill payoff – Critical thinking moves PMs from good to great.
    [37:57] Daily self-check – Competing evidence and opposing views strengthen decisions.
    [38:27] Leadership example – Teams lose the skill when leaders stop modeling it.

    Help keep the Product Porch lights on by giving at https://www.patreon.com/TheProductPorch

    Join our email list and never miss an episode at theproductporch.com
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    40 分
  • Decisions in Uncertainty (Part 2): How to Make the Call

    Decisions in Uncertainty (Part 2): How to Make the Call
    2026/05/26

    So now you have defined the starting point and the ending point. Check out Part 1, “Decisions in Uncertainty (Part 1): When ‘Go Build This’ Is All You Get.” What do you do next? In part two of this conversation, Todd Blaquiere and Ryan Cantwell define the messy middle and give you the playbook for making the call when certainty is still out of reach.

    They walk through how to use a decision-making rubric, identify the highest-risk assumption, and test what matters most before a team sinks too much time into the wrong thing. They also dig into one of the hardest parts of product work: how to read unclear signals, weigh imperfect evidence, and know when you have enough confidence to stop researching and start recommending.

    If part one was about slowing down long enough to get clear, this episode is about what it takes to move forward anyway. Pull up a chair on the porch and know how to make the call.

    Time Stamped Notes:

    What should a product manager do after getting clear on the outcome?
    [00:00] Part two setup - Episode moves from clarity to decision-making
    [01:01] Why this still feels hard - Clear requests can still hide unclear success
    [02:19] What defines the target - Business outcomes and customer outcomes shape the call
    [04:05] Why success must be defined - Better decisions start with clear success criteria

    How can a product manager make a better decision when the answer is not obvious?
    [05:13] Why a rubric helps - Shared criteria make hard calls easier
    [06:04] What goes into a rubric - Value, demand, fit, timing, and right to win
    [07:50] Why weighting matters - Some criteria matter more than others
    [08:08] Why confidence matters too - Weak evidence should not count the same as strong evidence
    [11:43] What a rubric is really for - Alignment matters more than fake objectivity

    How can a product manager figure out what to test first?
    [13:07] What the “monkey” means - The highest-risk assumption can kill the idea
    [13:32] How to move faster - Tiny Acts of Discovery focus on the biggest risk first
    [14:27] Example: missing data - No data can mean no product
    [15:31] Example: willingness to pay - Real pain does not always lead to real revenue

    How can a product manager test assumptions without fooling the team?
    [19:32] Say versus do - Real behavior matters more than polite feedback
    [19:54] Why direct asks work - Simple requests can reveal the truth faster
    [20:33] What commitment looks like - Pre-orders, signups, and emails show stronger intent
    [21:02] What to avoid - Friendly audiences can give false confidence
    [22:12] Why the answer stays messy - Evidence is rarely perfect or complete

    How does a product manager know when it is time to make the call?
    [23:04] How to use the evidence - Outcomes, rubrics, assumptions, and tests work together
    [24:07] Why a second lens helps - Another prioritization method can expose weak thinking
    [25:01] What “enough confidence” looks like - Full certainty usually never comes
    [25:27] When the job changes - Research mode must turn into recommendation mode
    [29:06] How to present the call - Start with the ask, the risk, the test, and the recommendation
    [30:22] What leaders want first - Executive audiences want the answer up front
    [36:00] Who owns the decision - Judgment cannot be handed to someone else

    Help keep the Product Porch lights on by giving at https://www.patreon.com/TheProductPorch

    Join our email list and never miss an episode at theproductporch.com
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    41 分
  • Decisions in Uncertainty (Part 1): When “Go Build This” Is All You Get

    Decisions in Uncertainty (Part 1): When “Go Build This” Is All You Get
    2026/05/12

    Have you ever been told to “go build this” and felt that little pit in your stomach because you were not totally sure what success even meant? In this episode, Todd Blaquiere and Ryan Cantwell dig into one of the most uncomfortable parts of product management: making decisions when the path is unclear and the pressure to move is high. They talk through why vague direction creates misaligned expectations, how product managers get trapped into building motion instead of outcomes, and what it takes to slow down long enough to clarify business outcome, customer value, and the real risk underneath the request.

    Along the way, they share stories of getting it wrong, and dig into why so many product managers freeze or default to the safest path, and offer a more practical way forward.

    _If you have ever felt stuck between vague direction and the pressure to act, pull up a chair on the porch, rethink how you make decisions under uncertainty, and start building the judgment that sets great product managers apart.__

    Time Stamped Notes

    What should a product manager do when the direction is vague but action is expected?
    [00:00] Episode framing - Uncertainty and pressure to act set up the core problem
    [00:57] Why this feels hard - Product managers feel overwhelmed when the path is unclear
    [02:50] Two kinds of uncertainty - Empowered decision-making versus unclear direction with no real guidance

    Why is “just go build it” such a dangerous trap?
    [03:11] Blind execution trap - Following the request without understanding the outcome creates risk
    [04:04] LA Times example - Building the thing without asking why leads to misalignment
    [05:27] Hardware example - Big expectations show up before customer, problem, or business context is clear
    [06:29] False progress - Busy work, polished decks, and shallow analysis can hide the real problem

    How should a product manager clarify what success actually means?
    [09:33] Start with why - Better decisions begin with understanding the outcome behind the request
    [10:34] Curiosity over confrontation - Better questions create alignment without triggering defensiveness
    [13:06] Outcome alignment - Business goals and stakeholder goals need to be made explicit
    [15:55] Playback and check-ins - Repeating understanding and revisiting direction reduces drift

    What makes a good decision framework when certainty is impossible?
    [18:43] Business outcome and customer value - Both sides are needed to make strong product decisions
    [20:26] Specific customer definition - Clear value starts with identifying the exact customer
    [23:27] Business context - Portfolio gaps, business risk, and cost of inaction sharpen the goal
    [24:20] Partial certainty - Strong product decisions often happen before perfect clarity exists
    [26:12] Working rubric - Outcomes and value become a hypothesis for what to test

    How should a product manager reduce risk before committing to a path?
    [28:09] Monkey on the pedestal - The riskiest assumption should be tackled first
    [29:35] Hard thing first - Early validation reduces waste and improves confidence
    [32:53] AI feature scenario - Sales pressure becomes a practical example of reframing a request into assumptions and tests
    [38:19] Discovery patterns - Lost deals, customer segments, and recurring signals help focus investigation
    [41:15] Closing takeaway - Simple mental models help product managers move forward under uncertainty

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