The Innovators Studio with Phil McKinney

Phil McKinney
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  • Entrepreneurship
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Forty years of billion-dollar innovation decisions. The real stories, the hard calls, and the patterns that repeat across every organization that's ever tried to build something new. Phil McKinney shares what those decisions actually look like. Phil was HP's CTO when Fast Company named it one of the most innovative companies in the world three years running. He co-founded a company and took it public. Now he runs CableLabs, the R&D engine behind the global broadband industry. This isn't theory. It's what happened. And what you can see coming if you know what to look for. Running since 2005, originally as The Killer Innovations Show, now The Innovators Studio. Tens of millions of downloads. Full archive at killerinnovations.com. New episodes at philmckinney.com.

  1. Aug 26

    How to Recognize Failure Patterns: How HP Quit and Apple Won

    Recognizing failure patterns is the closest thing an innovator has to seeing the future.   If you can recognize the patterns, you can change the future, because most failures are not original. They repeat, and that repetition is the pattern: the same handful of patterns reappearing in one organization after another, decade after decade.   This one stings a little.   In 2011, Bill Geiser told me, almost word for word, how the project we had spent the past two years building was going to fail. He saw the pattern before I did; I heard him say it, and I never forgot it.   The failure still happened.   This is not a pre-mortem. A pre-mortem imagines new ways your plan could fail. Recognizing failure patterns means learning from failures that have already happened elsewhere and spotting the early signals before you repeat them, while there is still time to act.   By the end of this episode, you will have four patterns in your own library, a five-minute way to check any project against them, and the four steps to take when you find one.   Let's get into it. The Smartwatch We Killed In 2004, Fossil hired a watch-technology executive named Bill Geiser to help build innovative technology for their watches. A few years later, he and I started spending real time together, me as HP's CTO, him running watch technology at Fossil. Between us, we had an idea we both believed in: a connected wearable, years before anyone used that phrase, co-innovated by HP and Fossil, with each bringing its expertise.   Fossil named the resulting platform the MetaWatch. It ran an ultra-low-power processor with a 96 by 96 display, an accelerometer, and Bluetooth. It was designed to last a week on a charge, and it shipped with a full developer kit so anyone could build apps for it. We revealed the partnership in March 2011, at an HP event in China. And between us, we had the one thing Apple did not have in 2011: distribution. HP held roughly ten percent of consumer-electronics shelf space. Fossil sold through twenty thousand retail stores that carried its watches.   Bill saw the ending before anyone. He told me in 2011: "Phil, I wouldn't be shocked if Apple evolved the Nano to take advantage of this space. They'll legitimize it in consumers' minds worldwide."   So the man building the watch spotted the failure in advance, out loud. And naming it changed nothing.   The signs kept arriving in plain sight. HP went through three CEOs in thirteen months. In August 2011, Leo Apotheker killed HP's consumer mobile strategy and WebOS, which removed the platform that made a smartwatch matter to HP at all. The battery lasted three to four hours against the original target of a week. We ran month-long approval cycles for changes that startups could implement in days.   Then I went out on medical leave. When I came back six weeks later, HP had killed Palm, WebOS, and the connected wearable project.   Here is what the ignored warning turned into. The Apple Watch shipped in April 2015. It sold 4.2 million units in its first quarter, and by that fall Apple was selling three out of every four smartwatches on the planet. The market we walked away from grew from three hundred thousand units in 2012 to forty-five million by 2018, and Apple held fifty-one percent of the market share.   The idea was never the hard part. It never is. The hard part is committing. What Recognizing Failure Patterns Means Nothing that killed the MetaWatch was new, and none of the signals were faint. They were loud; they were ignored, and each one was a pattern that has killed projects for decades.   Recognizing failure patterns has two halves. The first is building a library of how failures repeat. The second is matching the situation in front of you against that library, and forcing what you find into an actual decision while the fix is still cheap. Bill did the first half. Neither of our companies did the second, and the gap between those halves is where the Apple Watch came from.   Here are four entries for your library, straight from this one failure. Each one ends with a test question. By the end, you will have a four-question checklist, and then I will show you what to do when a pattern shows up. Pattern 1: Success Protects Itself Fossil's traditional watch business grew from $950 million in 2004 to $3.25 billion by 2013. It was tripling while we were building the thing that might replace it, and that growth made cannibalizing it politically impossible. Fossil never had to kill the MetaWatch outright. Fossil positioned the watch as a two-hundred-dollar development platform, something no ordinary customer would ever be handed at a retail counter.   When we constrain what we're innovating so it doesn't risk the present, we've lost the future.   Test it: Is the new thing priced, staffed, or positioned so that it cannot hurt the current thing? If the answer is yes, this pattern is already running. Pattern 2: The Warning That Changes Nothing Bill's warning was specific, early, and exactly right, and it still changed nothing. I heard it directly, and hearing is not the same as deciding: nobody re-ran the plan with "Apple arrives and legitimizes the category" as an input, no roadmap changed, and no budget moved.   Test it: What decision changed after the warning? If the honest answer is none, the warning was never acted on, no matter how many people remember hearing it. Pattern 3: The Problem Nobody Owns A week of battery life was the MetaWatch's central promise, and it shipped at three to four hours. Both companies saw the gap. No one owned fixing it. The hardest problem on the project sat on the seam between two companies, and problems that sit on seams get reported, tracked, and carried forward without ever belonging to anyone who can be asked why the problem is still there. We ran that partnership for two years and never settled whose job it was to lose sleep over the one number that mattered most.   Test it: Who owns the hardest problem, by name? If the answer is a partnership, a committee, or a pause, then nobody owns it. Pattern 4: The Pace Mismatch Earlier, I shared that we ran month-long approval cycles for changes a startup could implement in days. That is a pacing problem: the organization's internal pace versus the market's. A smartwatch in 2011 was a fast-moving product running through slow-moving machinery, and no amount of talent inside the project could close that gap. The delay was structural, not personal.   Test it: How long does one small change take to approve, against how fast the market moves? Time a real one. Do not estimate it. Five Minutes on a Project That Died The four patterns are the start of your library. Before you use it on a live decision, test it on a dead one.   You have a dead project in your past. Everybody does. Pick the one that still stings and give it five minutes against the four test questions.   Was an existing success being protected while the project starved, in pricing, staffing, or positioning? Did people raise a warning, and what decision changed after it was said? Who owned the hardest problem, and can you name the person? And how long did a small change take to approve, against how fast the market was moving?   When I run the MetaWatch through those four questions, I find all four patterns. Your project will probably show fewer. Every pattern you find is one you will now recognize as it happens on the project you are working on right now. The Four Steps When You Spot a Failure Pattern Which brings up the harder half of the skill, because recognition alone did not save us. Suppose you had been standing next to me in 2011, holding all four of these patterns. It would not have been enough. Being right about the future means nothing without the organizational machinery to act on that insight.   So when a failure pattern appears on a live project, follow this sequence.   Step one: Say the failure pattern out loud, in the room where the decision lives. Not in the hallway afterward. Use the pattern's name.   Step two: Get it onto the decision memo. A warning that lives only in conversation changes nothing. Write the pattern and what it costs into a document that will drive a decision.   Step three: Attach an owner. If you identify a critical problem, assign it to one person. Not a team or an organization. Someone you can ask next sprint why the problem is still there.   Step four: Attach a date. Being early provides an advantage, and a failure pattern without a date on it fades unnoticed.   Bill was right for four years, and Apple was the one who acted on it. That could have been us if we'd had the organizational courage to back our vision with meaningful resources. Conclusion You now have what nobody handed me in 2011: four patterns of failure, a five-minute check, and the four steps to run when you spot one. What you do in the room where the decision lives is the part Bill's warning never got. Additional Resources How HP and Fossil Handed Apple the Smartwatch Market: The inside story of vision without execution: why being right about the future means nothing without the courage to act on breakthrough insights. https://www.philmckinney.com/how-hp-and-fossil-handed-apple-the-smartwatch-market/ The story of MetaWatch with its founder, Bill Geiser: https://www.philmckinney.com/the-difference-between-a-good-idea-and-a-great-idea-is-the-timing-s11-ep25/

    How to Recognize Failure Patterns: How HP Quit and Apple Won

  2. Aug 19

    How to Improve Your Abductive Reasoning Skills

    Sherlock Holmes never once used deduction.   Open any of the stories and watch what he actually does. He notices a tan line on a wrist or mud dried on a boot and leaps to the best-fitting explanation. Then he went looking for evidence. Deduction guarantees its conclusions. What Holmes did was guess. He was better at it than everyone around him because he treated guessing as a discipline.   The discipline of guessing is one of the most useful thinking skills nobody ever taught you.   Let's get into it. What Is Abductive Reasoning? There are three kinds of reasoning. School taught you two of them.   Deduction moves from a general rule to a specific conclusion. For example, all mammals have hearts. Dogs are mammals. So dogs have hearts. If the starting statements are true, the conclusion must be true.   Induction goes the other way, from specific observations to a general pattern. For example, if you see a thousand white swans, you conclude all swans are white. Probably right, but never guaranteed. Europeans believed exactly that until they reached Australia and found black swans. I covered both in an earlier episode on logical reasoning skills.   Abduction is the third kind, the one school skipped, and it runs reasoning backward. You start at the far end, with the result or fact in front of you, then you work back to whatever would explain it. For example, the lawn is wet at six in the morning. You didn't see it rain, and nothing rules out a broken sprinkler. But rain explains it best, so you accept it for now and get on with your day.   Notice that you did that without deciding to. You are running abductive reasoning constantly, on faces and sales numbers and to explain the silence after you finish talking in a meeting. We all leap from evidence to an explanation and then treat the explanation as fact. You already know how to do this. What you don't have is the habit of catching yourself at it and doing it deliberately when it matters.   Now go back to Holmes. In A Study in Scarlet, the first thing he ever does on the page is shake hands with a stranger and say, "You have been in Afghanistan, I perceive." Holmes explains it later. The man had a medical look but a soldier's bearing. His face was dark, but his wrists were fair, so the tan came from somewhere hot. His left arm hung stiffly, so it had been injured. None of those clues proves anything on its own. Holmes asked which single story would cover them all: an army doctor, wounded and sent home from the Afghan war. That is abduction, not deduction, and Conan Doyle used the wrong word for it his entire career. Why AI Can't Do Abductive Reasoning Abduction is the only one of the three that creates a new idea. Deduction draws out what was already inside the starting statements, and induction stretches a pattern you already saw. That difference used to be a philosopher's distinction. It matters now because AI has gotten very good at the other two. AI finds patterns in billions of examples and extends them, induction at a scale no human can match. What it cannot reliably do is face a fact that fits no pattern and come up with an explanation worth betting on. That leap is still uniquely human, and the people who make it well are getting more valuable every year.   Meanwhile, abduction as a skill is getting harder to keep, because search engines and chatbots now answer most questions in under a minute, and abduction doesn't work that way. It asks you to live with "probably" for a while, holding an answer you know might be wrong and working anyway. How to Improve Your Abductive Reasoning Skills None of this is a talent you either have or don't have. Watch a good doctor or a talented designer at work, and you will see the same four moves. Each one can be practiced. 1. Notice What Doesn't Fit In 1847, a Hungarian doctor named Ignaz Semmelweis ran a maternity clinic in Vienna. It had two wards, one staffed by doctors and one by midwives, and in the doctors' ward new mothers were dying of fever at three times the rate. Some women gave birth in the street rather than be admitted, and the street births survived at a better rate.   Everyone was ignoring the numbers. Semmelweis treated the numbers as the question. What could explain the best-trained people in the hospital losing the most patients? His answer came when a colleague cut his finger during an autopsy and died of the same fever. Doctors went from dissecting corpses straight to delivering babies. Midwives never touched corpses. He ordered handwashing in chlorine, and the deaths collapsed decades before anyone had heard of germ theory.   Charles Sanders Peirce, the philosopher who gave the skill its name, built that noticing into his own definition of abduction. The surprising fact is observed, he wrote, and only then does the search for an explanation begin. When something fits what you already believe, we accept it without noticing. Surprise is the alarm that the automatic version has failed. It means the failure that matters happens before any of the reasoning starts. The risk is that you cannot explain a surprise you never see. We have spent years learning to rationalize them away.   When reality does something you didn't predict, write it down before you talk yourself out of it. 2. Generate More Than One Explanation Last week I told you about the chrome-looking keyboard snafu. An HP laptop was running below plan, and none of the data would say why. Before anybody knew about the keyboard, I asked the product team what they thought was happening.   I got three answers. It needs more memory. It needs a bigger hard drive. It needs longer battery life. Each one arrived with a competitor's machine held up beside ours. That one has more memory. That one has the bigger drive. Every comparison was accurate, and each one had been picked because it supported the answer.   Three explanations sounded like the work was done. All three said the same thing: that we were losing on specifications, which is what the team was already organized to believe. None of the explanations would have sent someone into a store on a Saturday to watch a customer make the purchase decision.   So when you catch a genuine surprise, run it through this sequence instead:   State the surprise in one sentence. "Sales dropped 15% in a quarter when we improved the product." If you can't state it cleanly, you don't yet know what you're explaining. Force at least three explanations. The third is usually where the thinking starts, because the first two are the ones everybody already believes. Include one explanation you don't want to be true. If every explanation on your list flatters you and your team, the list isn't finished. Write down what each explanation would predict. If A is true, what else should you be able to see? That turns a guess into something you can check. 3. Choose the Best Explanation, Not the First One Philosophers call abduction "inference to the best explanation," and the key word is "best." Best doesn't mean first, and it rarely means the cleverest.   The one you want covers the most facts while assuming the least. Then, before you commit, decide what evidence would make you drop it. A doctor hearing chest pain does this out loud, ruling out the dangerous explanation first even when she expects it to be something ordinary. 4. Hold It Loosely and Test It Cheaply The best explanation is still a bet, nothing stronger. The moment you forget the word "bet," the skill starts working against you. Many will make a sharp abductive leap, fall in love with it, and then spend six months protecting it instead of testing it.   Designers handle this better than almost anyone. A prototype is an abductive test, the cheapest way to let the world argue with your guess before you commit real money. Whatever your field, find your version of the prototype. Good innovators are wrong as often as everyone else. They just find out sooner, and it costs them less.  Try This Before Friday Think of the last thing at work that went differently than you expected. A deal that died, a number that moved the wrong way, a good person who quit. You already have an explanation for it. Notice how fast it came. Somebody asked what happened, and you had an answer before they finished the question, and everyone nodded, because it was reasonable, and reasonable answers end conversations.   Go back to it this week. Not to find out whether you were right, because you were probably close enough. Go back because two other explanations fit those same facts and you never considered either one. Find them, and make one something you would rather not be true.   Then find the smallest test that would tell them apart. A phone call. One question to somebody who was in the room. It is almost never expensive, and that is the part worth sitting with, because it means the answer has been sitting there the whole time and nobody went and got it. Conclusion Abductive reasoning is how new ideas enter the world, and Holmes made a career of it under the wrong name. The more leaps you make and test, the sharper the next one gets.

    How to Improve Your Abductive Reasoning Skills

  3. Aug 12

    The Customers Who Walked Away

    Think about the last thing you almost bought and did not. You picked it up, you looked at it, you put it back down. You had a reason for choosing one item over its competitors, and you know exactly what it was. Did anybody ever ask you why you didn't choose the loser? Of course not. And somebody did the same thing to you this week. Something you made, or wrote, or suggested. An idea you put into a meeting that got a polite nod and then went nowhere. They considered it, decided against it, and you never found out the real reason. Almost everything that comes back to you comes from the people who said yes. Inside a business, the machinery makes that official. Satisfaction surveys go to people who have bought. Reviews come from people who have bought. The customer list is a list of people who have bought. The one who put it back down is invisible to all of it, and that person is holding the answer. So, where do you point a question to reach somebody who is not there? Last week, we took a question apart and rebuilt it. But a well-made question aimed at the wrong thing comes back empty. Where you point a question is the other half of the skill. Forty-two cards of Killer Questions A killer question is one that has been tested and proven to spark ideas beyond the obvious. The name comes from the old phrase "killer app," where "killer" meant standout, not lethal. I built this collection of questions the slow way. I designed structured tests into my innovation workshops, which I was running: specific questions put to specific groups, and a record of what each produced. The rule for keeping a question was that it had to trigger something in that room. Did somebody walk out seeing their customer, their product, or the way they work differently than when they walked in? If nothing moved, the question was cut. If there was a good idea underneath one and I had simply worded it badly, I rewrote it and ran it again in another workshop. There were hundreds of questions, and most did not survive. Forty-two did. When I sorted the survivors, they fell into three groups. Not by subject. By where they aim your thinking.  Three places to aim Every question in the deck points to one of three things. Who, what, and how. WHO is the person or organization who will benefit from what you make. Usually, your customer, though the gap between "usually" and "always" is where a lot of new business hides. WHAT is the product, the service, the solution that creates the value for the WHO. HOW is the way your organization builds, delivers, and supports the WHAT for the WHO. Three places an idea can come from, and the questions exist to send you into each one on purpose rather than by accident. The cards all say "product." Read that as whatever you make for somebody else. A service counts. So does a proposal you hand to your boss. Thirteen of my cards aim at WHO. Thirteen at WHAT. Sixteen at HOW. That split was never a plan. It is where the questions that kept surviving pointed, and eventually, I stopped arguing with it. WHO What are your unshakable beliefs about what your customers want? The phone companies believed their customers wanted reliability above all else, and they were right. For a century, they built toward 99.999 percent uptime. A dial tone that worked in a storm, in a power failure, always. Then somebody turned that belief over and looked at it with no assumptions about what the customer wanted. Where were there people who would give up call quality for something else? That is the question that led to voice over IP, a phone call carried over the internet. In the early days, it sounded terrible. Calls dropped. By the standard the old industry had spent a century perfecting, VoIP was not a serious product. But underneath it sat an idea nobody in that industry had allowed themselves to consider: that many people would trade call quality for price and mobility, and would do so happily. They did. That market opportunity existed before anyone built it, waiting for someone willing to challenge the old standard on its head.  WHAT What is surprisingly inconvenient about my product? It does not ask whether the product is good. Your team will defend that all day, and they will be partly right, which will only make the conversation worse. Surprisingly inconvenient means some part of using your product that nobody inside your company has ever seen a real person struggle with. The fifteen minutes with the instructions. The step everyone on your team skips automatically because they built the thing. On the back of that card sits the shortest useful question I own: "Do you use your own product yourself?" Hold on to this one. In a few minutes, it turns up at a Best Buy, and the strange part is that I wasn't aiming at it when I found it. HOW What do people not like about the buying experience for my product? For the whole time I was CTO at HP, I spent nearly every Saturday in a Best Buy. While traveling, I found a local electronics store in whatever country I was in. I was not shopping. I was standing in the aisle watching strangers choose, because this question has no other answer. You cannot survey somebody who did not buy.  My family called it my "digital stalking". My kids would have done almost anything else rather than be seen with me in a Best Buy on a Saturday. When somebody picked up a competitor's product and headed for the checkout, I would walk over, introduce myself, and ask what made them choose that one. I did it long enough that the sales staff around Silicon Valley knew me, and would change how they talked to a customer if they saw me standing there. Stay somewhere long enough, and you stop being an observer. You influence what you are measuring. Then came the Saturday that changed a product. A customer set down an HP laptop and bought a competitor's. I asked him why. He told me he could not see the keys. This was during the stretch when every laptop was going aluminum, and somebody in our laptop group had given ours a chrome-looking keyboard. High gloss. On a store shelf, it looked expensive, which is exactly what it was designed to do. It also meant that anyone whose eyesight had started to go could not read the letters on it. Sales on that product had been running under plan, and nothing in the data said why. We changed the keyboard back to matte black with high contrast white lettering. Sales went up on that one change. No survey would have revealed the issue. By the time he told me, he was already somebody else's customer. Notice what that question actually turned up. I went into the store carrying a HOW question about the buying experience. What came back was a WHAT answer, something surprisingly. Inconvenient about the product itself. You choose where to aim. You do not choose where the answer comes from. Testing for Great Questions How do you find and test great questions? Ask the question, then watch what happens in the next four seconds. If the room goes quiet, and then somebody says, "Hang on," you are holding a good one. That pause is the sound of a person arriving somewhere they have not been. You just unleashed a great question. If someone answers immediately and confidently, the question isn't that great. A fast answer means you aimed where the team has already been, and they are reciting. I threw away hundreds that way and got down to the questions that caused impact. Practice Exercise  Take a decision you are facing this month and give it ninety minutes. Start with whichever of the three you are least comfortable with, which for most people is HOW. Take a question from it at random. Do not hunt for the one you like, because the one you like is the one you have already answered. Set a timer for thirty minutes and write down every idea that question produces. No judging, no editing, just quantity. Then do the same thing with a question aimed at your customer, and again with one aimed at the product. When you have worked through all three, read back over everything and pick the best three ideas to start on. That is how you run it on your own. If you would rather do it with a team, there is a second version designed for groups of 4 to 6 people. Both are written out step by step on killerquestions.com, so you are not working from memory. You can buy the deck at innovation.tools, either as printed cards or as a digital download for your phone, tablet, or PC.  Send me the question that worked best for you. I am also still on the search for new questions for volume 2 of the card deck. Send your suggestions, and they might just be included. That closes out this three-part series on questions. If you came in at the end, part one is about why you cannot stop yourself from answering a question, and part two is about how a single word inside a question steers the answer you get back. The next episode is about abductive thinking. Deduction hands you a conclusion you can prove. Induction hands you a pattern you can bet on. Abduction is what you reach for when you have neither and still have to decide, which is most of the time. It is how a doctor arrives at a diagnosis, and it is how most real innovation actually happens. Subscribe wherever you watch or listen, and you will get that one when it lands.

    The Customers Who Walked Away

  4. Aug 5

    When One Word Steers Your Answer

    In 1974, a psychologist named Elizabeth Loftus showed a group of people a film of a car accident. Afterward, she asked half the group one question: how fast were the cars going when they hit each other? The other half got the same question with a single word swapped: smashed instead of hit. The smashed group estimated higher speeds. Same film. Same crash. One word.   A week later, everyone came back and answered a new question: Did you see broken glass? There was no broken glass in the film. The smashed group remembered it anyway. One word inside one question changed what people reported seeing. Then it reached back and rewrote what they remembered. Somebody did this to you this week. A question steered your answer, and you never noticed. Last week, I showed you that your brain cannot refuse a question. You hear one, you start answering, whether you agreed to or not. This week is the other side of that power. If every question forces an answer, then how the question is built decides which answer you get back. Questions have an anatomy. Almost nobody looks at it. Before we're done, a motorcycle taxi in Bangkok is going to show you what a question built right can find.  Let's get into it. ===== Every question you ask carries three things, whether you put them there on purpose or not. It carries assumptions, the things it treats as already settled. Ask "Why did the launch fail?" and you've ruled the launch a failure before anyone speaks.   It carries scope, the range of answers it permits. "Coffee or tea?" permits exactly two answers. "What should we drink?" opens the room.   And it carries a load, the specific words that steer the answer. That's what "smashed" did. Nobody in that study felt steered. The steering is invisible to the person answering, and most of the time to the person asking too.   Once you see the parts, you can take any question apart. Take "why did the launch fail?" one more time: it assumes failure before anyone answers, its scope is only explanations, and "fail" is the loaded word doing the steering. Hold on to that one; we'll rebuild it later. Let's start with the ones built to do damage. The worst question in business "That presentation was fantastic, wasn't it?" That's a tag question: a statement dressed up as a question, built so every answer is closed off except agreement. The person asking isn't really asking, not in any way that risks hearing something different. They're collecting a signature. When lawyers use them in court, it's called leading the witness. When managers use them in conference rooms, it's called alignment. Tag questions did damage for years at HP, in the design reviews, every product went through before customer briefings. One review was for a prototype, one of our first laptops in what's now called the "thin and light" category. The product team wanted to lead that category without straying too far from what already sold. That's the tradeoff tag questions live in. Nobody wants to sound negative about it. So the pushback arrived dressed as agreement: "We'll still have four USB ports, right?" You don't get thin and light with a case full of legacy ports, and the designer knew it. I watched the exasperation cross his face. Before it became a battle, I stepped in and rebuilt the question: "What's the right mix of ports for this segment, and why?" Then: "Have we tested that mix with the target customers?" The answer came back fast. "No need. We know what the customer wants." I nearly smacked my forehead. One rebuilt question had uncovered the real problem, and it wasn't ports. It was an untested assumption sitting in the middle of a flagship product plan. That's what tag questions cost you. The entire point of asking a question is to get information, input, or ideas. A tag question collects compliance instead. Enough of them, and people stop bringing you anything you don't already believe. The two kinds of good questions Real questions are split into two categories: factual and investigative. A factual question retrieves information. How many units did we sell last week? You may not know the answer, but you know exactly how to get it: one phone call. Factual questions keep the world running. They just can't uncover anything because they only retrieve what somebody already knows. An investigative question can't be answered with a yes, a no, or a lookup. It's divergent: more than one correct answer exists, so the person answering has to go investigate. You felt this difference last week. "What is half of thirteen?" is a factual question. "How many ways could you answer: what is half of thirteen?" is an investigative question. One is arithmetic. The other is the one that a classroom answered thirty-two different ways. Socrates built his entire way of teaching on investigative questions, pushing every student past "I've heard it said that..." until they could say what they themselves thought, and why. The first step toward knowledge, he insisted, is admitting yours is incomplete. So here's the definition I've worked from for years. A great question makes the other person genuinely think before they answer, and it surfaces an insight that had been eluding them. Both halves matter. The first half costs you real effort. The second half is the payoff: you see something you've been looking at for years and never actually seen, and it surfaces possibilities you'd never have found on your own. How to build one How I build them is with four moves, in the order I use them. You already watched three of them work in that design review. Strip the answer out. Read your question and find the smuggled conclusion. "Right?" and "wasn't it?" come off first. Then the buried assumptions. "Why did the launch fail?" becomes "what happened when we launched?"   Open the exits. If it can be answered with yes, no, or a single lookup, it's factual. That's fine when information is all you need. When you want discovery, rebuild it until more than one correct answer exists. The rebuild is usually small. "Should we do this?" becomes "what are the ways this could work, and what are the ways it dies?"   Weigh every word. This is Loftus's lesson. "How do we make this cheaper?" and "what happens if we triple the price?" point at the same product, and they will never produce the same ideas. There is no neutral wording. Every word carries load, so choose it deliberately instead of inheriting it.   Aim it where nobody is looking. If you can already predict the answer, the question is just decoration. Point it at something unexamined: the assumption everyone stopped checking, the customer nobody ever talks to. A great question should surprise you.   What a well-built question uncovers Late 1994, Bangkok. Half an hour stuck in traffic, twenty-eight minutes until a critical meeting. At eighteen minutes, I gave up, jumped out of the limo, and hailed a motorcycle taxi. 125ccs of terror, flat out through the heat, my briefcase clutched to my chest. Somewhere between the tuk-tuks, I started thinking about the machine underneath me. Cheap, efficient transportation. The streets were flooded with them. And on the other side of the world, Harley-Davidson was selling motorcycles as big-ticket luxury. A product that began as cheap transportation for the American working man had become a premium item that people waited months for. In that evolution, somebody had to ask a question like this: What if we stop trying to make this cheaper and go the other way? Everything needed to see that opportunity had been lying in plain sight for decades, waiting for someone to ask for it. That's the payoff of this craft. The next discovery in your business is probably sitting in an assumption nobody's checked in years, waiting on the right question to find it. Practice Exercise: Before your next meeting, write down the one question you most need to ask, exactly as you'd naturally say it. Then take it apart on paper. What does it assume? What answers does it permit? Which words are steering, and in what direction? Rebuild it with the four moves and ask for the rebuilt version instead. Pay attention to what comes back that the original never would have surfaced. Conclusion This is part 2 of a three-part series on questions as the skill behind better thinking, better ideas, and better innovation. Next in on the specific questions I spent twenty years collecting and testing, the ones that reliably uncover what everyone else misses. Full show notes for this episode are up at philmckinney.com. And if you want more than the show episodes, check out my articles over on Substack, including the first four chapters of my next book.  Subscribe on YouTube or wherever you get your podcasts so you don't miss the next episode, and don't forget to hit the notification bell if you want to know the moment it's up.

    When One Word Steers Your Answer

  5. Jul 29

    What Half of 13 Reveals About Your Thinking

    What is half of thirteen? Stop. Answer it. Don't think ahead, just answer. You said 6.5. I know you did, because everyone does. Nobody chose to answer that question. Your brain solved it before you decided whether you even wanted to play along. That's the power of a question: whoever hears it cannot stop themselves from answering. Ask a person something and their mind starts working on it right away, whether they wanted to or not.  If you gave that answer on a math test, it would get ‌marked as correct. Give that same answer on a test of innovation, and you're average, because that's where everyone stops. Push beyond the obvious answer, and that's what puts you top of the class. Here's the version of the question that changes everything: How many ways could you answer "what is half of thirteen?" Sit with that for a second, because the honest reaction most people have is mild panic. There's the obvious one. Then what? Split the number down the middle and you get a 1 and a 3. Split the word into syllables and you get "thir" and "teen." Every one of those is a real answer. None of them occurred to you the first time, because the first time, your brain wasn't looking for options. It was looking for an answer to the question. I've run this exercise for years in my Innovation Boot Camp and the Innovation Essentials Workshop. One professor who uses my book in their class now opens every semester of her course with it. The class brainstorms as many answers to the question as possible. The record so far is thirty two different ways to answer that one question. And remember, asked the first way, that same question only gives you one answer. This isn't just a classroom trick. Researchers have measured this exact mental muscle since the 1960s, asking people how many uses they could find for a brick. Some people list four. Some list forty. The gap between those two people has nothing to do with intelligence. It's whether their mind treats the first answer as the end of the search or the beginning of one. Practice Exercise: Take one recurring question: a decision at work, a plan for the weekend, even "what should I make for dinner." Before you settle for the obvious answer, ask "how many ways could I answer this?" and write down at least ten. Not ten good ones, just ten. See where the eleventh one takes you. This part 1 of a three-part series on how to use questions as the skill behind better thinking, better ideas, and better innovation. Next week, we get into what actually separates an average question from a great one.

    What Half of 13 Reveals About Your Thinking

  6. Jul 8

    How to Tell a Good Decision From a Lucky One

    You trust your gut because it's been right before. But "right" is exactly the thing you've been measuring wrong. A hitter never has this problem. His batting average is honest. It counts hits, nothing else, across a whole season, and he can't argue with the number. Your gut is supposed to work the same way: every decision an at-bat, every result feedback, a career sharpening your instincts the way a season hands a hitter a real number. But you keep your own scorebook. You mark every win as good judgment the second it lands. The trouble is that a skilled call and a lucky one produce the same win. In your book they look identical. Train your gut on that for thirty years and it grows certain about things that were never true. I know, because I trained mine that way. The Award and the Bankruptcy At twenty-eight, I won, and the win felt like proof. I was at a company called ThumbScan, and I took a piece of government security technology and repackaged it for the business PC market. We called it PCBoot. PC World named it Security Product of the Year at COMDEX in Las Vegas, in front of the whole industry. I drew the obvious conclusion. My gut was good. I could see what the market wanted before the market did. Except I didn't see it coming. In early 1988, computer viruses became front-page news. The New York Times ran it on the front of the business section, the story spread to nearly every paper in the country, and overnight every company in America decided it needed security. My product was already built and sitting on the shelf when the panic arrived. I had built a solution that needed a problem, and the people writing and spreading those viruses are the ones who handed it one. It was nothing I did. I hit the timing right, and the timing was luck. It took an honest audit, years later, to admit that, and the same look turned up the opposite story. The other ThumbScan product was the one I was proudest of. It put fingerprint security on a personal computer, the first one under a thousand dollars you could attach to a PC. Your thumb instead of your password. The reasoning was sound and the technology worked. The market wanted none of it. PCs were barely in homes yet, biometrics sounded like science fiction, and the company bled cash and folded. That product wasn't worse thinking than the one that won the award. It was the same thinking, aimed at an idea that turned out to be twenty-five years early. Today it sits on every phone, and hundreds of millions of people use it before breakfast. I wasn't wrong about the concept. I was wrong about the clock, and the clock runs mostly on luck. The award and the bankruptcy came out of one gut, separated only by the year each idea landed in. What I did, years later, has a name. I ran the version of events that didn't happen, stripped the result off each decision, and looked at the call cold. That's counterfactual thinking, and it's the whole skill. It's uncomfortable, because the result already handed you a verdict and now you're reopening it. It's also the only feedback that makes you better. Why Your Results Lie to You None of this is your fault. It's a measurement problem. Your gut got trained on bad data, and it had no way of knowing. The more decisions you've stacked up, the more confident it's become, and confidence built on a bad stat is worse than no confidence at all. A junior person knows they're guessing. Twenty years in, the guessing feels like knowing. Your own record is full of the same thing. Wins you credited to your own judgment when they really came down to timing, or to a competitor's mistake you had nothing to do with. Good calls you stopped making because one of them lost, even though losing was always on the table and the call was still right. None of that is carelessness. You recorded every result accurately. You just recorded the wrong thing, and then you trained on it. The world isn't helping. Every outcome now arrives with its explanation already attached, ten confident takes by lunchtime, most written backward from the result. I covered that warning in "Hindsight Is Not 20/20." So go back and run the audit on yourself. Rebuild what you knew on the day you decided, set the result aside, and ask whether the call still holds up without it. The hard part is doing this to wins, because taking apart a success while you're still proud of it feels like bad manners and bad luck at once. That's the reason your wins are where your worst lessons hide. Read Your Competitors' Moves You just watched me run this backward, over my own record. It points two other directions too. The first is sideways, at everyone else. The same move works just as well on decisions that aren't yours. When a rival's bet pays off, the instinct is to copy it. When it craters, the instinct is to swear it off. Both stop at the result. So rebuild their decision the way you rebuilt your own. Say a competitor ships a feature and it takes off, and three teams in your space scramble to copy it. What they miss is that the feature didn't carry the launch. It landed the week the category leader had an outage, and every angry customer went shopping. Copy that same feature into a calm market a year later and nothing happens, because you copied the move and not the moment. You're hunting for the hinge, the single thing the outcome really swung on, and it's rarely what the headlines credited. Get this wrong and you don't copy a rival's strategy. You copy the luck that came with it, and luck doesn't travel. Pressure-Test Your Next Decision The other direction is forward, into a choice still in front of you, and it's where the skill pays you back the most. Most of us pick options by their best case. You picture each road going well and take the one that goes best. Turn that around. Walk each option forward until it falls apart, because every option falls apart somewhere, and the one you can't picture breaking is just the one you haven't looked at hard enough. Pull in the alternatives you've already talked yourself out of, and count doing nothing, since that's a choice too. Then decide on the part nobody likes to look at: the downside you'd have to live with. A modest plan you can walk away from beats a brilliant one that takes you down with it. Most decisions that seem obvious stop seeming that way once you walk the alternatives all the way out. The ones that still look right after that walk are the ones worth making. Practice Exercise: Audit a Win You're Proud Of This is the drill that matters most, and the one you'll want to skip. Pick a win from the last year. Not a loss. Something that worked, that you've been glad to take credit for. Write down what you knew the day you decided. Only that, nothing you picked up afterward. Run the version where it went the other way. How close did it come, and what would have had to break differently? Answer straight. Good decision, or good result? If it's hard to sit with, you're doing it right. The wins you can't bring yourself to examine honestly are the ones costing you the most. A good decision and a lucky one keep looking identical until you do the work to tell them apart. Do that work often enough and you stop mistaking the breaks that fell your way for things you did well. Over a career, that is most of the gap between people who get reliably good and people who just had a good run.

    How to Tell a Good Decision From a Lucky One

  7. Jun 24

    How to Improve Weak Signal Judgment

    Everyone collects weak signals now. Most of what they collect predicts nothing. A weak signal isn't a thing you spot, it's a prediction you make, and the edge goes to whoever bets on it while being wrong is still cheap. So how do you become the one placing the bet, not the one still collecting reports? Let's get into it. What a Weak Signal Actually Is A weak signal is a faint piece of evidence that points to something a customer will want before they can name it, and before the market has priced it in. Faint, because if it were loud, everyone would already be acting on it. Deniable, because you can always explain it away as noise, and most people do. That deniability is the whole point. The moment it becomes undeniable, the advantage is gone and the price has moved. Why Noticing Stopped Being the Edge Ten years ago, noticing was hard. You needed sources, a network, time to read widely, a feel for the edges of your industry. That was the moat. It isn't anymore. Every team has a trend report and three newsletters and an AI tool surfacing emerging behaviors on a schedule. The noticing got automated. What didn't get automated is the judgment about which signal predicts a structural change and which points to nothing real, and the nerve to act early. Inside Roche's Innovation Board I sat on Roche's diagnostics innovation board, the only outsider in the room, helping decide which ideas got funded. At one point we took on diabetes care. I am not diabetic. So I had Roche ship me every meter and test strip they made, and I pricked my finger up to a dozen times a day to feel what their customers felt. You cannot innovate for a customer whose day you have never lived. Skip that, and everything after is a guess. Roche was a leader in blood glucose testing with its Accu-Chek meters, and the math looked obvious. Someone with type 1 diabetes tests around eight times a day, every day, for life. A big, stable business. Type 2 was the smaller story per patient. Those patients tested once, maybe twice a day, so each one looked worth less, and we filed the category under "less interesting." We could already see type 2 climbing. We weighed it against the per-patient math and explained it away. Then type 2 diagnoses exploded into one of the fastest-growing chronic conditions in the world. And the category stopped being about counting tests per day at all, because monitoring went continuous, the always-on sensors people wear today. We had seen the early edge of both shifts. We even predicted them. We just didn't move fast enough, and the reason is the one that kills most weak signals inside a big company. Project approval and annual budgets are built to fund what's already proven, not to chase something still faint. Roche got there. Accu-Chek SmartGuide, its real-time continuous monitor, is on the market now. I just wish we had moved the moment we saw it, instead of waiting for the next budget cycle to make it safe. How to Read a Weak Signal We didn't miss the type 2 signal for lack of noticing. We noticed. We missed it on the three things that come after, and those you can train. The moves start once you've got a signal you can't quite dismiss, and the skill is what you do with it. Tell the Canary From the Costume A canary in a coal mine matters because the air changed. It signals something structural, a shift in the environment that affects everyone in it, whether they've noticed yet or not. A costume is the opposite. A few people put it on, it's striking, it spreads for a season, then they take it off and the room is exactly as it was. On day one the two look identical. A behavior appears, it's unusual, it's spreading. The only question that matters is whether it predicts a change a customer can't reverse, or a moment that will pass. Back in 2018 I wrote about telling a trend from a fad, and the test still holds: ask what need the behavior reveals. Type 2 was a canary, and we read it as a costume, because we counted testing frequency instead of the need underneath it. That need, millions of people learning to manage a lifestyle disease, only grew. The discipline is refusing to let the size of the spike tell you which one you're looking at. Costumes spike too, sometimes higher. You're reading for the need, not the noise. Read the Window A signal's window is short. Too early, you can't tell it from noise and you waste resources chasing ghosts. Too late, it's obvious, everyone sees it, and the advantage is already priced in. The value lives in the narrow gap between. Waiting for more evidence feels like better judgment, but the evidence that finally convinces you has already reached your competitors. Certainty and advantage move in opposite directions, so by the time you're sure, sure is just another word for too late. The question isn't whether the signal is real yet. It's how much longer you can be the only one taking it seriously. Act While Being Wrong Is Cheap This is the move that separates the people who read signals from the people who collect them, and almost nobody is willing to make it. A signal you predict but never act on is still just watching. Ideas without execution are a hobby, and I'm not in the hobby business. The whole value of an early signal is that you move before it's confirmed. Wait for proof and you've waited too long. So you act on thin evidence. And thin evidence is wrong a lot, which means you will be wrong a lot. People hear that and freeze, because they picture the cost of being wrong as the failed product, the wasted year, the budget burned on a guess. People call that caution. It isn't. The skill is structuring the bet so that being wrong is cheap. You don't commit a product line to a deniable signal. You commit a prototype. A landing page. One conversation with ten customers. A two-week test that costs you a sprint and buys you information you can't get any other way. Being early and wrong should cost you a week. Being early and right should put you a year ahead. You're not betting on being right. You're buying the option to be right, cheap enough that being wrong doesn't hurt, and you scale up only as the signal firms up. That's why the noticing crowd never gets here. Noticing carries no risk, so it never builds the muscle for cheap commitment. They watch, they report, they wait for certainty, and they call it foresight. It's the safe choice, and it's worth nothing. Practice Exercise: Run a Signal Through All Three Pick one behavior you've been dismissing as noise. Something you've seen more than once, in your customers, your kids, your own habits, that you waved off because it looked too small or too strange to matter. Then run it through the three moves. Canary or costume. What need does the behavior reveal? A need the person can't go back from, or a novelty they'll set down in a season? Write the answer in one sentence. If you can't, you don't understand the signal yet. Find the window. How much longer does this stay deniable? Who else is likely seeing it? If the honest answer is "it already feels obvious," pick a different signal. You're late on this one. Design the cheap bet. What's the smallest thing you could do this month to test whether you're right, where being wrong costs a week and being right puts you ahead? Name the bet. Name the cost. Name what you'd learn. Do this with one real signal and you'll feel the difference between collecting signals and using them. Collecting is comfortable. Using one costs you a decision. If you want a sparring partner for that, I built one. From Signal to Bet is a set of AI prompts that run a signal through these same three moves and argue with your read at each one. It's free at innovation.tools. The exercise teaches you the moves. The prompts make you defend them. The signal was always there, for you and for everyone reading the same reports you read. The edge was never in seeing it. It was in what you were willing to do before it was safe to do anything at all. Get good at that, and you stop reacting to the future and start arriving early.

    How to Improve Weak Signal Judgment

  8. Jun 10

    How to Improve Your Second-Order Thinking Skills

    In 2000, Toys R Us paid Amazon $50 million a year to sell their toys online. It looked like a great deal. The company that defined toy retail for two generations was solving the internet problem in one move. Four years later they were suing each other. Seventeen years later Toys R Us was gone. Every store closed. Every job lost. And every step of what happened was visible from the day the deal was signed. Nobody at Toys R Us saw it. What Is Second-Order Thinking? First-order thinking asks what happens next. Second-order thinking asks what happens to the people who see what happened next. The skill isn't caution. It's the willingness to keep looking after the room has stopped. Inside HP, 2006 In 2005, HP launched Halo, a premium telepresence system co-developed with DreamWorks. For a brief period it reported into my organization. The next year, Cisco launched TelePresence and went straight at us. I called the HP team closest to Cisco and asked what they made of it. The answer was reassuring: Cisco is aiming down-market, we're fine. We were premium; they were chasing volume. That answer satisfied the room. It did not satisfy me. The room was asking "will Cisco hurt Halo?" That was the wrong question. The right one was sitting underneath: why did our partner of twenty years decide to do this without us? Nobody had an answer to that one. The HP team didn't think it was the question. They were focused on the product collision, and I kept coming back to the partnership. A company that had cooperated with us for two decades had just decided they didn't need to anymore. The product was the surface. The relationship had quietly ended, and we were the only ones who hadn't noticed. Three years later, Cisco launched a direct attack on HP's core server business with Unified Computing System. HP responded by acquiring 3Com and going after Cisco's core networking business. A twenty-year alliance ended in under two years. Neither side ran the second-order analysis at any point along the way. By the time the right question got asked, the partnership was already gone. The Three Skills These three skills stand on their own. Each one solves a different problem most decision frameworks miss. The first picks up signals before there's even a decision to analyze. The second uncovers what's actually driving the other party's timing. The third shows you what people will do once they see your decision land. If you've watched the November 2025 episode on the basics of second-order thinking, these skills add to that foundation. If you haven't, you can still apply all three starting today. Sense the Weak Signal, Not the Loud Event Most failures don't announce themselves. The loud event, the launch, the lawsuit, the lost customer, is usually the visible end of something that started much earlier as a quiet shift somebody noticed and explained away. A weak signal is a small piece of information that doesn't fit the story you're already telling. A customer's casual comment that contradicts your data. A team member's evasive answer in a status meeting. A supplier missing a deadline they've never missed before. The reflex is to make it fit the story you already believe. The skill is to refuse. Go looking before you have one. Once a week, scan three places where weak signals live. Customer-facing teams. Data points that surprised you and got brushed off. Topics that smart people you respect are paying attention to, but you aren't. You're not looking for problems. You're looking for things that don't quite fit. Name the thing that doesn't fit. Be specific. "Their CFO made a comment about the budget that didn't match what we were told last quarter." Not "something feels off." The more specific the signal, the more useful it becomes. List the stories that would make the signal make sense. At least three. Force yourself to consider explanations that don't fit your current assumptions. Ask which of those stories you'd act on if it were true. If one of them would change a decision you're about to make, that's the signal you can't afford to ignore. Find one more data point before you decide. A single signal can mislead. Two signals pointing the same direction is usually real. The Cisco TelePresence launch was a weak signal about the partnership. The team read the product. I read the relationship. Neither of us pushed it far enough. Ask "Why Now" Before "What's Next" Most people jump straight to the future: what will the other party do next? That's the wrong starting question. Ask why now first. Why is this happening now, when it could have happened a year ago? The timing tells you what changed in their world, and that change tells you what they're likely to do next, often more reliably than asking the question directly.   State the move that just happened. A competitor launched a product. A regulator opened an inquiry. A customer asked for a discount. Name it plainly. Ask what changed. What was true a year ago that isn't true now? What can they do today that they couldn't do then? Their capability, their pressure, their read of you, their read of the market. Identify the shift. Use the change to predict their next move. What's the natural follow-on from the thing that made this move possible? That's usually where the real consequence lives.   Cisco didn't enter telepresence in 2006 because telepresence was suddenly interesting. They entered because they'd decided the partnership with HP no longer constrained them. "Why now" would have surfaced that. "What's next" wouldn't have caught it in time. Watch the Response, Not the Result Your decision produces a result. The result triggers a response from everyone watching, your competitors, your customers, your team, your investors. Most analysis stops at the result. The response is where the actual consequence lives.   Toys R Us could have predicted that Amazon would sell more toys. That was the result. What they didn't predict was Amazon's response: opening the platform to third-party sellers, learning the toy business, and using the data to compete directly. By the time Toys R Us understood the response, Amazon had already replaced them. State the immediate result of your decision in one sentence. What will be visibly different in the world after you act? List who can see that result. Be specific. Name people if you can, not categories. For each one, ask: what does the result tell them about you? Your priorities, your weaknesses, your appetite. The result is information about you they didn't have before. Ask what they're now in a position to do that they weren't before. The result changes what's available to the other actors, not just the market. Identify the responses you can't undo. A customer who loses trust. A competitor that smells weakness. A regulator who opens a file. Those are the ones to model carefully. HP launching Halo was the result. Cisco entering TelePresence was the response. By the time anyone at HP said the word "over," the partnership had been over for three years. Practice Exercise: Run All Three on One Decision Pick one decision you're currently working through. Run the three skills against it in sequence. Weak signal. What have you noticed in the last 90 days connected to this decision that doesn't quite fit your current story? Don't explain it away. Name it. Why now. What changed in the world recently that's making this decision feel urgent now? Was that change visible six months ago? Watch the response. Who will see the result of this decision, and what does it tell them about you that they didn't know before? The first time you run this, you'll miss things. That's normal. The skills sharpen with repetition. The fifth time you sit down with a real decision and work through all three, you'll catch signals that other people in the room aren't even seeing yet. That's what improvement looks like. If any of the three turns up something the room hasn't discussed, you've found the work that needs to happen before the decision is made. Take what you found and run it through the two skills from the November 2025 episode. Map how people will respond. Ask "and then what?" two or three more times. All five skills work as one system. The link to the November episode is in the description below. Most second-order failures do not arrive as surprises. They arrive as something somebody noticed once, didn't have a way to act on, and explained away.

    How to Improve Your Second-Order Thinking Skills
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About

Forty years of billion-dollar innovation decisions. The real stories, the hard calls, and the patterns that repeat across every organization that's ever tried to build something new. Phil McKinney shares what those decisions actually look like. Phil was HP's CTO when Fast Company named it one of the most innovative companies in the world three years running. He co-founded a company and took it public. Now he runs CableLabs, the R&D engine behind the global broadband industry. This isn't theory. It's what happened. And what you can see coming if you know what to look for. Running since 2005, originally as The Killer Innovations Show, now The Innovators Studio. Tens of millions of downloads. Full archive at killerinnovations.com. New episodes at philmckinney.com.

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