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.

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.

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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.

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.

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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.

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

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