Your best innovation ideas aren't losing to bad ideas – they're losing to exhaustion.

Sometimes that was the case … but most of the time, I was wrong.

We've created an innovation economy that's too innovative to innovate. And if you're wondering why your breakthrough ideas keep getting ignored, dismissed, or tabled “for later review,” this video will show you the real reason.

I'm going to reveal why even brilliant ideas are dying from attention scarcity, not their merit. And why this crisis will determine which companies dominate the next decade.

Monday, I shared the complete story in my Studio Notes newsletter about how I first discovered this crisis at HP. For a comprehensive analysis and its implications for your company, please visit the link below. 

In this episode, I will share with you a practical framework for recognizing and addressing this problem within your organization.

Let's start with the math that's breaking everyone's brain.

Every C-suite leader I know is evaluating 40+ innovation proposals monthly. That's what they tell me when I ask why good ideas are getting ignored—two per business day, every day, without break.

However, what's happening psychologically is that decision-makers are developing reflexive skepticism toward all innovation claims as a survival mechanism.

It's not cynicism – it's cognitive self-defense against proposal overload.

In conversations with dozens of executives over the past year, nearly three-quarters tell me “innovation fatigue” has become their top decision challenge.

Think about that. The problem isn't a lack of innovation. The problem is too much innovation.

Good ideas are dying not from merit evaluation but from attention competition. We've created an innovation economy where the sheer volume of innovation prevents genuine innovation from emerging.

And here's the irony – I'm using the same overloaded language that's part of the problem. When every idea is described as “revolutionary”, the words lose all meaning.

Last month alone, I was pitched 23 “revolutionary” AI solutions. Most were solid ideas with real potential. But none got the attention they deserved because my brain had already tagged them as “more innovation noise” before I could properly evaluate their merit.

And that's when it hit me: if someone whose job is literally to analyze innovation decisions can't focus properly, what chance do overwhelmed executives have?

The cruel mathematics are simple: breakthrough ideas need deep consideration, but executives only have bandwidth for surface-level evaluation.

Let me show you exactly how this plays out in the real world.

I first discovered this crisis when I was running HP's Innovation Program Office – what we called the IPO.

The IPO was HP's dedicated engine for identifying, incubating, and launching breakthrough technologies that would become the company's future growth drivers. We had frameworks, funding, and brilliant people. Harvard and Stanford now teach case studies about the HP IPO and our process design.

But here's what those case studies miss entirely: we were drowning.

The HP Innovation Program Office received more than 3,000 ideas and pitches every year. 

Think about that number. That's nearly 60 new innovation opportunities hitting our desk every week. Each claiming to be a breakthrough. Each demanding evaluation. Each potentially containing the next billion-dollar opportunity for HP.

No team – no matter how smart, no matter how well-resourced – can properly evaluate 3,000 innovation ideas annually. The mathematics are impossible.

Fatigue became our constant battle. We would clear 50 proposals in one week, only to find 65 new ones waiting the next. It felt like trying to empty an ocean with a bucket.

We built sophisticated tools. We created evaluation frameworks. We hired brilliant people and trained them extensively.

But underneath all our sophisticated processes was a gnawing feeling that haunted every decision: somewhere in those 3,000 ideas were genuine breakthroughs that we weren't giving proper attention.

I remember one particularly brutal month when we evaluated around 250 innovation pitches.

By week three, I caught myself skimming proposals that deserved hours of consideration. By week four, I was unconsciously looking for reasons to say no rather than reasons to say yes.

That's when I realized the system had broken me. And if it could break someone whose job was literally to find breakthrough innovation, it was breaking everyone.

The most painful part? Years later, I would occasionally encounter innovations in the market that looked suspiciously familiar. Ideas that had been buried in our pipeline, dismissed not because they lacked merit, but because they arrived during a week when we were too overwhelmed to give them the attention they deserved.

We had built the most sophisticated innovation evaluation process in corporate America, and it was systematically filtering out the very breakthroughs it was designed to find.

Not because our frameworks were wrong, but because human attention has limits that no framework can overcome.

That HP experience taught me to recognize the pattern. Since then, I've worked with dozens of companies facing the same crisis. The scale varies – some see 500 pitches annually, others see 5,000 – but the attention mathematics always break the same way.

Through analyzing failures across multiple companies, I've identified three distinct types of innovation fatigue destroying breakthrough potential.

The symptom: Senior leaders developing reflexive skepticism toward ALL innovation pitches, regardless of merit.

Here's what's happening: The overpromise/underdeliver cycle has trained executives to expect disappointment from innovation investments. After being burned by “revolutionary” solutions that delivered incremental improvements, leaders develop cognitive firewalls against innovation enthusiasm.

I recently spoke with a Fortune 500 CEO who instituted a company-wide moratorium: “No more innovation pitch meetings for six months. We're drowning in breakthrough promises and starving for actual execution.”

This wasn't anti-innovation leadership. This was a smart executive recognizing that innovation overload was preventing innovation success.

The impact: Even legitimate breakthroughs get dismissed as “more innovation theater” before receiving proper evaluation.

The symptom: Innovation teams are burning out from launching initiatives that consistently get killed or ignored during the evaluation process.

The cause: Organizations creating continuous innovation pressure without building the decision infrastructure to evaluate and support breakthrough ideas properly.

At HP's IPO, I watched our most creative evaluators essentially stop fighting for breakthrough ideas. When I asked why, one told me: “When you're processing 60 pitches a week, you learn to spot the safe bets quickly. Fighting for the truly revolutionary ones takes energy I don't have anymore.”

This is innovation death by a thousand small compromises.

The impact: The best innovators leave for companies with more explicit innovation mandates and better decision-making processes.

The symptom: Customers and investors becoming increasingly skeptical of innovation claims, treating all “breakthrough” announcements with equal skepticism.

The cause: “Revolutionary” has lost all meaning through overuse. Every product launch, every startup pitch, every feature update is positioned as game-changing innovation.

Consider how “AI-powered” became the new “cloud-enabled” – meaningless marketing speak that signals nothing about actual innovation value.

When everything is revolutionary, nothing is revolutionary.

The impact: Actual breakthroughs struggle to differentiate from incremental improvements because the market has developed immunity to innovation language.

This isn't just another innovation challenge we can solve with better processes or more resources. This crisis is fundamentally different from anything we've faced before.

Historically, innovation slowdowns were resource problems. Companies couldn't innovate because they lacked money, talent, or technology. The solution was always to invest more resources in innovation capability.

Today's crisis is a problem of attention and focus scarcity in an abundance economy. We have unlimited innovation opportunities and limited cognitive bandwidth to evaluate them properly.

More resources won't solve the attention mathematics.

Consider this trend: innovation proposals are growing exponentially, but human decision-making bandwidth remains constant.

The danger: We're not innovation-starved – we're innovation-overwhelmed. Breakthrough opportunities are getting lost in the noise of marginal improvements.

However, here's the opportunity: The companies that figure out attention allocation will dominate the next decade, while everyone else struggles with their own innovation success.

This isn't about having better ideas. It's about being heard above the innovation noise, and most organizations have no systematic approach to cutting through their own opportunity abundance.

So what do you do about this? Let me share a practical framework I use with companies to diagnose and address innovation fatigue.

Step 1: Count Your Innovation Inputs

Track everything hitting your innovation pipeline for one month—ideas, pitches, proposals, “quick conversations” about breakthrough opportunities. You'll be shocked by the number.

Step 2: Calculate Your Evaluation Capacity

How many innovation decisions can your team properly evaluate monthly? Not skim – properly evaluate. Be brutally honest about the time quality evaluation requires.

Step 3: Identify the Attention Gap

Subtract your capacity from your inputs. That gap is where breakthrough ideas go to die.

Step 4: Design Attention Triage

Develop systematic methods to quickly identify the 10% of ideas that warrant in-depth evaluation. Not perfect – but systematic.

Step 5: Build Decision Infrastructure

Most companies have innovation processes but no decision infrastructure. These are different things.

Remember: The goal isn't to evaluate everything. The goal is to evaluate the right things properly.

Here's what I want you to do right now: Look at your innovation pipeline. How many ideas are currently stalled not because they lack merit, but because nobody has the attention bandwidth to evaluate them properly?

If you're honest, it's probably most of them.

Innovation fatigue is just one of ten critical innovation decision challenges that nobody's addressing directly. Over the past month, I've identified the unspoken problems that determine whether billion-dollar innovation bets succeed or fail.

Add your voice to choose which challenge I should cover in an upcoming episode. Should I reveal why smart teams kill their own best ideas? How fear corrupts innovation thinking? Why executives make terrible choices about breakthrough opportunities?

The voting is live right now in a special Studio Notes post: “You Get to Choose: Which Innovation Decision Topic Should I Tackle First?” Monday's complete analysis of innovation fatigue – including the case studies I couldn't share here – is in this week's regular Studio Notes.

Vote – What Should We Explore Next?

The companies that master innovation decisions while their competitors remain overwhelmed will write the next chapter of business history.

Which chapter will your company write?

If you want to improve the thinking behind your innovation decisions, check out our video on critical thinking skills – it's been our most popular this year. 

And don't forget: the complete innovation fatigue analysis, including what I learned from 30,000 failed evaluations at HP, is in this week's Studio Notes.

Share in the comments: What innovation decision challenge is keeping you awake at night?

To learn how I evaluated innovation ideas, listen to this week's show: I Evaluated Over 30,000 Innovation Ideas at HP: Here's Why Most Failed.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

While competitors optimize existing solutions, breakthrough thinkers reimagine entire problems. The Lateral Thinking framework ‌shifts how you perceive challenges and find hidden opportunities.

A software engineer grabbed a random word from a dictionary – “beehive” – and within hours designed an algorithm that saved his company millions. While his colleagues were working harder, he was thinking differently.

I'm Phil McKinney and welcome to my Innovation Studio. In this episode, we will cover the lateral thinking framework. Not theory – a practical, step-by-step system you can use immediately. You'll try your first technique in the next five minutes. By the end of this episode, you'll have four specific techniques that transform how you approach problems, plus practice methods that make mastery inevitable.

And hey, if this kind of framework thinking resonates with you, then hit that subscribe and like button. It helps us with the algorithm. If you want to dive deeper into these topics, then subscribe to my Studio Notes on Substack. Plus, if you know someone who might find this episode useful, feel free to share it with them.

Alright, let's dive in.

Here's what most people miss: breakthrough solutions don't come from thinking faster or working longer. They come from thinking differently. While everyone else improves using existing tools and approaches, lateral thinkers reimagine entire problems.

For example, Southwest Airlines didn't create a better airline experience – they reimagined air travel as mass transportation. Tesla didn't build superior cars – they re-conceptualized personal mobility around sustainable energy. These companies succeeded by approaching familiar challenges through completely different frameworks.

The question isn't whether you're smart enough to solve problems – you are. The question is whether you're willing to disrupt your thinking patterns to discover solutions that conventional logical approaches miss.

But here's where most people get lateral thinking completely wrong, and understanding this distinction will determine whether you develop breakthrough capabilities or just become better at brainstorming…

What is the distinction between Linear and Lateral thinking? When faced with a problem, most people use linear thinking – they analyze what's wrong and optimize within existing frameworks. It's logical, sequential, and focuses on improving current approaches.

Lateral thinking does something completely different. Instead of improving what exists, it changes how you perceive the problem itself.

Let me illustrate the difference with a single example. When customers complained about long wait times, linear thinking said, “Make the elevators faster.” Lateral thinking asked, “What if waiting wasn't the real problem?” The solution? Install mirrors next to elevators. People stopped complaining because they were distracted, not because waits got shorter.

Linear thinking improved the elevator. Lateral thinking eliminated the problem by changing what the problem actually was.

This is Dr. Edward de Bono‘s systematic method for shifting perceptions entirely. As he explained: “To find breakthrough solutions, change where you're looking, not just how hard you're looking.”

The challenge isn't that people lack creativity – it's that they don't have systematic methods for breaking free from mental patterns that limit them. Lateral thinking offers specific techniques for generating what de Bono referred to as “movement” in thinking.

When everyone in your industry follows similar approaches, breakthrough opportunities emerge for those who think differently. While competitors optimize existing methods, lateral thinkers discover entirely different approaches.

This operates on four distinct levels that build systematic capabilities. The progression from beginner to expert follows a pattern that will surprise you…

The lateral thinking framework has four progressive levels. Here's a quick overview of each so you have context before we explore them each in detail.

Level One: Suspend Judgment and Break Patterns – Your foundation level. You'll learn to deliberately disrupt automatic thinking responses and embrace ideas that seem absurd. This creates the mental environment where breakthrough solutions can emerge.

Level Two: Random Input for Forced Connections – Intermediate level. You'll use systematic provocations to force your brain into unfamiliar territory. This isn't random creativity – it's controlled disruption that bypasses your brain's tendency to look for solutions in familiar places.

Level Three: Challenge Sacred Assumptions – Advanced thinking. You'll systematically examine and reverse the fundamental premises everyone else takes for granted. This is about creating “movement” in thinking by making the familiar strange.

Level Four: Embrace Deliberate Absurdity – Expert level. You'll find breakthrough solutions by seriously exploring ideas that seem obviously wrong. This isn't about being silly – it's about using absurdity as a systematic tool for discovering hidden insights.

Quick Demo: Before we dive deep, let's try one technique. Think of any current challenge you're facing. Now grab the nearest object – a pen, coffee mug, your phone, anything. Spend thirty seconds asking: “How is this object like my problem?” Force weird connections.

A pen runs out of ink – maybe your problem needs fresh input. A coffee mug holds liquid – maybe your challenge needs a container or boundary. Your phone connects people – maybe your issue needs better communication.

Notice how this random object sparked different angles? That's lateral thinking in action. This was just a taste – each level has systematic techniques that amplify this effect.

Here's what's powerful about this progression. You don't need to master all four levels to see dramatic results. Level One techniques alone can solve problems that teams couldn't crack in weeks. But when you combine all four levels, you develop innovation confidence – the unshakeable belief that creative solutions exist for every problem.

But the real power comes from developing what I call “innovation confidence” – the systematic ability to find creative solutions when conventional approaches hit dead ends.

Ready to transform how you approach problems? Let's start with Level One, but I need to warn you – what seems like the simplest technique often produces the most unexpected breakthroughs…

Your brain is a pattern-recognition machine. It looks for familiar situations and applies solutions that worked before. This efficiency usually helps, but when facing new problems, these patterns become invisible barriers that prevent you from finding new solutions.

Level One breaks these patterns systematically. Here are three specific techniques:

Technique One: Change Your Routine Disrupt both your thinking environment and daily patterns. Your brain associates thought patterns with specific locations and routines. If you always brainstorm in the same conference room, you'll have the same types of ideas.

Southwest Airlines used this brilliantly. Instead of studying airlines, they studied bus transportation. This environmental change broke their mental patterns about air travel. They discovered point-to-point routes, eliminated assigned seating, removed meal service, and focused on quick turnarounds. Every innovation came from thinking like a bus company, not an airline.

You can apply this by changing where you tackle problems, taking different routes to work, using your non-dominant hand for simple tasks, or changing when you tackle challenging problems during the day. These disruptions create mental flexibility that carries over into creative problem-solving.

Technique Two: Question Core Assumptions Write down three assumptions about your current challenge. Then ask, “What if the opposite were true?” Most problems have hidden assumptions we never examine.

Example: Improving customer service. Your assumptions might be that customers want fast responses, prefer human interaction, and contact you when problems occur.

Consider this: What if customers prefer thoughtful responses over fast ones? What if they choose good self-service over poor human service? What if you could help customers before problems arise?

Suddenly, you're thinking about proactive support, comprehensive self-service resources, and quality over speed. These insights come from questioning assumptions everyone else accepts.

Technique Three: Time-Box the Impossible Spend ten minutes seriously considering solutions that seem impossible. Often “impossible” means “we haven't figured out how yet.”

Amazon's same-day delivery seemed impossible until they reimagined warehousing. SpaceX's reusable rockets seemed impossible until they questioned whether rockets had to be disposable. What seems impossible in your field might just need different thinking.

Pattern breaking works because it forces your brain out of automatic mode. But what happens when you want to accelerate this process dramatically? The next level introduces something so counterintuitive that it seems almost absurd – until you see what it can create…

Level Two introduces controlled randomness that forces breakthrough connections. You'll learn to make your brain create links it would never make naturally.

This technique created Post-it Notes at 3M. A scientist had a “failed” adhesive that barely stuck. A colleague needed better bookmarks for his church hymnal. The random collision of these unrelated problems sparked repositionable sticky notes – now generating over a billion dollars annually.

The Process:

Step One: Define your challenge in one clear sentence. Be specific.

Step Two: Generate random input. Open a book to a random page and point to a word, use online random word generators, or grab three random objects around you.

Step Three: Force connections. Spend fifteen minutes finding ways to connect your random input to your challenge. No connection is too weird. The stranger, the better.

Real Example: Challenge: “Reduce employee turnover” Random word: “Garden”

Connections: Gardens need regular watering – employees need consistent check-ins. Gardens grow better with proper soil – work environment matters. Gardens require pruning dead parts – eliminate toxic behaviors. Gardens have seasonal cycles – adjust expectations based on business rhythms.

These random connections lead to employee development programs, environmental improvements, cultural changes, and seasonal workflow adjustments. None of these insights came from traditional HR thinking.

Why This Works: Random inputs bypass your brain's tendency to look for solutions in familiar places. They force neural pathways that wouldn't connect naturally. Breakthrough solutions often hide in unexpected combinations.

Which level is clicking for you so far? Pattern breaking or random connections? Both build the foundation for what's coming next…

Before we go deeper, let's practice what you just learned. Pick that same challenge from earlier. Now try a different Level 2 approach: grab any book, open to a random page, and point to a word. Spend one minute connecting that word to your problem. What new angles emerge? This compound effect – layering techniques – is where breakthrough thinking lives.

The Random Input Technique accelerates breakthrough thinking by forcing neural pathways that wouldn't connect naturally. But there's something even more powerful waiting in Level Three—a method that challenges the very foundations everyone else builds their solutions on…

Level Three challenges the assumptions that others take for granted. This is where lateral thinking becomes powerful – you'll start seeing opportunities that are invisible to your competition.

Netflix used “What If” thinking to transform entertainment. In 2007, they dominated DVD-by-mail with seven million subscribers. The industry assumed customers wanted to own movies, physical media provided the best quality, and broadband was too slow for streaming.

Netflix challenged every assumption: What if customers didn't want to own movies? What if delivery delays were barriers, not services? What if broadband became fast enough? Most radically: What if we cannibalized our own successful business?

These questions led to streaming launch in 2007. Today, Netflix has over 240 million subscribers, generating $31 billion annually, while competitors who didn't question assumptions have disappeared.

The Process:

Step One: List your core assumptions. These are things “everyone knows” are true in your field.

Step Two: Reverse each assumption completely. If customers want speed, ask “What if they preferred thoughtful slowness?” If more choices seem better, ask “What if fewer choices improved satisfaction?”

Step Three: Push to extremes and chain your questions. What if this took ten times longer? What if resources were unlimited? Take your best scenario and ask What If about that result. Keep pushing until you reach uncomfortable territory.

The biggest breakthroughs come when you challenge assumptions that feel fundamental– like rules.

“What If” thinking reveals hidden opportunities within your assumptions. However, the most counterintuitive breakthroughs often come from an approach that seems to violate common sense entirely, which brings us to the expert level that turns logic on its head…

Level Four is expert-level lateral thinking that embraces deliberate absurdity. You'll find breakthrough solutions by doing exactly the opposite of what seems logical. It sounds counterintuitive because it is – and that's precisely why it works.

IKEA revolutionized furniture by providing what seemed like terrible customer service. Instead of delivering assembled furniture, they made customers assemble it themselves. This reversed every industry assumption: customers would work instead of receiving convenience, invest time instead of getting immediate use.

The “backward” approach revealed hidden benefits: dramatically reduced shipping costs, minimal storage requirements, lower prices, and, surprisingly, customer satisfaction from successful assembly. What seemed like terrible service became a competitive advantage. IKEA now generates €38 billion annually.

The Process:

Step One: List how your industry typically handles similar challenges. Write down the conventional wisdom everyone follows.

Step Two: Reverse each approach completely. If your industry emphasizes speed, consider slowness. If everyone wants more features, consider fewer features.

Step Three: Explore the reversals seriously. Don't dismiss opposites immediately. Look for unexpected advantages in seemingly wrong approaches. Sometimes, the best solutions hide behind what appear to be terrible ideas.

Real Example: Dollar Shave Club did opposite thinking with razors. While the industry focused on premium features, advanced technology, and retail partnerships, Dollar Shave Club eliminated fancy features, used simple razors, and bypassed retail entirely by offering direct subscriptions.

Result: While major brands competed on blade technology and premium positioning, Dollar Shave Club captured a massive market share by doing everything the industry thought was wrong—and sold for $1 billion.

The most counterintuitive solutions often hide in directions that make everyone else uncomfortable. Don't just think outside the box – think in the opposite direction of the box.

You now have four levels of lateral thinking techniques. Do you know hat separates people who learn these concepts from those who actually master them? A system of practice that transforms theory into instinctive capability…

The following two practice approaches transform lateral thinking from a conceptual understanding into an instinctive skill. Use them solo for breakthrough thinking or with colleagues for collaborative problem-solving.

Approach One: The Assumption-Breaking Generator This combines three powerful techniques for compound breakthroughs. Start with a real problem you're facing.

Step 1: List three core assumptions about your challenge – things “everyone knows” are true.

Step 2: Reverse each assumption completely. What if the opposite were true?

Step 3: Grab a random word (book, phone, ask someone) and force connections between that word and your reversed assumptions.

Example:

This creates insights you'd never reach with single techniques alone.

Approach Two: The Escalation Challenge Perfect for pushing “What If” thinking to breakthrough levels. Start with a real problem. Propose a “What If” scenario addressing it. Keep pushing ideas to more extreme territory until you say, “That's impossible… but what if we could?”

Example:

“What if customers never waited?” → “What if they were served before arriving?” → “What if we predicted needs before customers knew them?” Suddenly, you're thinking about predictive analytics and anticipatory service.

Practice Tips:

Your Practice Plan: Choose one approach. Apply it to a current challenge. Spend 15 minutes. Document what emerges. Try another approach tomorrow. Build lateral thinking into your regular problem-solving routine.

Practice transforms these techniques from interesting concepts into instinctive capabilities. The more you use them, the more naturally you'll see solutions others miss.

Now comes the moment of truth. You have the complete framework, but what you do next will determine whether this becomes just another interesting video you watched, or the beginning of a fundamental shift in how you approach every challenge you'll ever face.

Companies investing in lateral thinking see documented ROI from 5:1 to 20:1. Individual professionals show 300% increases in viable ideas. But the real value is personal transformation – becoming someone who consistently finds breakthrough solutions.

Your assignment is simple: pick one problem you're currently facing and apply one technique right now. Not later – right now. Start with whichever level feels most accessible. Document what happens.

Share your discoveries in the comments. What patterns did you break? What random connections led to insights? What assumptions proved wrong? Your examples help others see possibilities and build our breakthrough thinking community.

To learn how to master lateral thinking skills, listen to this week's show: How To Master Lateral Thinking Skills.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

The most popular piece of innovation advice in Silicon Valley is wrong—and it's killing great ideas before they have a chance to succeed.

I can prove it with a story about a glass of water that sat perfectly still while a car bounced beneath it.

My name is Phil McKinney. I spent decades as HP's CTO making billion-dollar innovation decisions, and I learned the hard way that following “fail fast” advice cost us billions and robbed the world of breakthrough technologies.

Today, I'm going to share five specific signs that indicate when an idea deserves patience instead of being killed prematurely. Miss these signs, and you'll become another “fail fast” casualty.

So there I was around 2006, sitting in Dr. Bose's lab at Bose Corporation, and he was showing me what honestly looked like just a regular car seat mounted on some automotive hardware. I'm thinking, “Okay, what's the big deal here?”

But then he activates the system and has his assistant start driving over these increasingly aggressive road obstacles. And here's what blew my mind—the car chassis is bouncing around like crazy, but the seat? Perfectly still.

Then Dr. Bose does something that I'll never forget. He places a full glass of water on the seat and tells his assistant to hit a speed bump at thirty miles per hour. The chassis lurches violently, but not a single drop of water spills.

And here's what should terrify every “fail fast” advocate—this technology took fifty years to develop. Dr. Bose began developing the mathematical model in the 1960s. Under today's quarterly Wall Street pressure, this project would have been killed a hundred times over.

When I asked Dr. Bose how he could invest in an idea for fifty years, he explained that keeping Bose private meant they weren't subject to the quarterly results pressure that often destroys patient innovation at public companies.

At HP, we were trapped in that system—and it cost HP billions.

As a public company, we lived and died by quarterly earnings calls. Every ninety days, we had to show growth, and that quarterly drumbeat made us masters at killing promising ideas the moment they didn't produce immediate results.

Let me give you three examples that still keep me up at night:

WebOS: We acquired Palm for one-point-two billion dollars in 2010. Revolutionary interface, years ahead of its time. Killed it when it didn't achieve immediate dominance. Every time you swipe between apps today, you're using thinking we threw away.

Digital cameras: We literally invented the future of photography. Abandoned it the moment smartphones started incorporating cameras.

HP Halo: Immersive telepresence rooms with extraordinary meeting experiences. Sold to Polycom for eighty-nine million in twenty-eleven when quarterly pressures demanded focus. We bought Poly back for three-point-three billion in twenty-twenty-two. We paid thirty-seven times more to reacquire capabilities we built.

We weren't bad managers. We were trapped by the quarterly earnings system that makes “fail fast” the only option for public companies. And it was systematically destroying our breakthrough potential.

Visit Studio Notes over on Substack where I discuss how these quarterly pressures shaped our boardroom decisions and what we were really thinking.

Now, after making these billion-dollar mistakes, I had to figure out how to distinguish between ideas worth killing and ideas worth protecting. What I discovered changed everything—and it comes down to five things I now look for.

When I see all five, I know we've got something worth being patient with. Miss even one, and you're probably wasting your time.

Here's how to validate the science without being a scientist yourself.

Start with peer review. Has this been published in reputable journals? Are other researchers building on it? Red flag: if the only validation comes from the inventors themselves.

Next, bring in independent experts. Not consultants who'll tell you what you want to hear—find researchers who have no financial stake in your project. Share your core assumptions with them and ask them to identify any holes.

Look for mathematical elegance. Dr. Bose's suspension model was beautiful in its simplicity. Overly complex models with dozens of variables often hide fundamental flaws.

Here's your action step: Before investing serious money, get three independent technical reviews. If even one expert raises fundamental concerns about the underlying science, stop. No amount of patience fixes broken physics.

You need a dependency map. List every technology that has to work for your project to succeed. Then assess each one separately.

For each dependency, ask: Are we developing this ourselves, waiting for someone else to solve it, or hoping it gets solved by magic? If more than one critical piece falls in the “magic” category, you're not being patient—you're gambling.

Create realistic timelines for each component. Bose needed better actuators, smaller amplifiers, and faster control systems. They could systematically work on each piece. That's patient innovation.

But if you need breakthroughs in five unrelated fields simultaneously—like needing better batteries AND quantum computing AND room-temperature superconductors—that's not a plan, that's a wish list.

Your action step: Map your critical dependencies. If you can't draw a clear path to solving each one, either find a different approach or walk away.

Don't just look at today's market—study how problems evolve and new markets emerge.

Start with pain-point analysis. What specific problem does this solve, and how severe is that pain? Bose started with car comfort but found its real market when truck driver health became a safety issue.

Look for regulatory drivers. Often, breakthrough technologies become valuable when regulations change. Environmental rules, safety standards, health requirements—these create demand that didn't exist before.

Study early adopters. Who are the customers willing to pay a premium for imperfect solutions? This is your proving ground. If you can't identify specific early adopters willing to pay above-market prices, your timing is probably wrong.

Your action step: Identify three specific customer segments who would pay a premium for an early version of your solution. If you can't name them specifically, you're not ready.

This is about building defensible advantages that competitors can't easily copy.

Focus on system-level innovation, not just component improvements. Bose didn't just build better shock absorbers—they created an integrated electromagnetic system that required entirely different expertise.

Look for knowledge-accumulation advantages. The longer you work on something, the more you should know about it than anyone else. If competitors can hire away your key people and instantly catch up, you don't have a real advantage.

Consider manufacturing complexity. Technologies that require specialized production processes, custom tooling, or rare expertise create natural barriers to entry.

Your action step: Write down exactly why it would take competitors at least two years to match your solution, even if they threw unlimited money at it. If you can't make that case convincingly, keep working.

This is the make-or-break assessment. Most good ideas die here, not because the technology fails, but because the organization can't sustain the investment.

First, assess your funding horizon. How long can you sustain this before you need to generate revenue? Be brutally honest. Include the cost of delays, scope creep, and inevitable setbacks.

Second, evaluate the decision-maker's patience. Will the people approving your budget still be there in three years? Will they still believe in the project when competitors are winning quarterly battles?

Third, create protection mechanisms. This might mean dedicated funding that can't be raided for other projects, separate business units, or partnership structures that insulate the project from quarterly pressures.

Your action step: Calculate your true funding runway, including realistic setbacks and contingencies. If it's less than the time needed for fundamental breakthroughs, either get more patient capital or find a faster path to revenue.

These five assessments work in theory, but do they actually create billion-dollar returns in the real world?

Now here's the most compelling modern example—Dell's 2013 privatization. Michael Dell paid twenty-four-point-nine billion for one thing: freedom from the quarterly earnings pressure that was killing their long-term potential.

Dell explicitly stated the goal was “no more pulling R&D and growth investments to make quarterly numbers.”

And the results were remarkable. R&D spending went from one-point-one billion to four-point-four billion, Dell transformed from a declining PC manufacturer to an enterprise solutions leader, and by 2023, the investment had generated an estimated seventy billion dollar return.

One of private equity's most successful turnarounds, built on escaping the quarterly Wall Street system that makes “fail fast” the only option for most public companies.

Visit Studio Notes over on Substack where I share how Alex Mandl orchestrated this deal and what Michael Dell was really trying to accomplish.

The Bose Ride system launched in 2010, and it's been improving the lives of truck drivers who were facing whole-body vibration problems that had no technological solution. Academic studies have shown significant reductions in driver pain, lower fatigue levels, and faster recovery times after long road trips.

One driver told researchers something that really stuck with me: “I thought I was going to have to quit my job. I was in crisis because my back was in such bad shape, but now I feel great I am driving full-time.”

If Dr. Bose had followed “fail fast” advice, that driver would still be in pain, and the advance would never have happened.

So here's my challenge to you—and it starts with looking at what's sitting right in front of you.

Look at your current portfolio right now. Are there projects that passed mathematics validation but haven't shown commercial results? Projects where the ecosystem isn't ready but the fundamental science is sound?

Those might be exactly the innovations that deserve patience instead of “fail fast” pressure.

I use the seventy-twenty-ten model: Seventy percent core improvements that move quickly. Twenty percent on adjacent markets with moderate timelines. Ten percent transformational advances—your patient capital investments.

Patient innovation creates technological moats that rapid iteration cannot replicate. Once achieved, it often produces faster competitive advantage. You get overnight success after decades of systematic development.

The difference between companies that master this and those that don't comes down to asking the right questions.

Instead of asking “How quickly can we bring this to market?” try asking “What needs to get better before this becomes real?”

Instead of asking “What's our quarterly burn rate?” try asking “What's the breakthrough potential if we actually solve this?”

Instead of asking “Why hasn't this shown results?” try asking “Does the math work, and what pieces need more time?”

The companies that master this balance will dominate the next wave of transformative technologies. In a world obsessed with speed, patient innovators are building the technologies that will define the next decade.

What opportunity in your organization deserves patience instead of “fail fast” pressure? That question will determine whether you're building the next major advance or killing it before it has a chance to succeed.

Visit Studio Notes over on Substack where I tell the complete story of that day in Dr. Bose's lab and the boardroom decisions at HP that we're still paying for today.

If this resonates, share your own patient innovation experiences in the comments. And remember: thinking better creates better ideas. Sometimes those ideas need time to become the advances that change everything.

To learn why fail fast innovation advice is wrong, listen to this week's show: Why ‘Fail Fast' Innovation Advice Is Wrong.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

Analyzing the decision traps that killed a breakthrough partnership

Innovation partnerships can create breakthrough markets—or hand them to competitors through terrible decisions. I know because I lived through both outcomes.

Bill Geiser from Fossil and I had it exactly right. We built the MetaWatch—a smartwatch with week-long battery life, Bluetooth connectivity, and every feature that would later make the Apple Watch successful. We had HP's massive retail reach, Fossil's manufacturing scale, and the technical vision to create an entirely new market.

But our organizations couldn't execute on what we knew was right. Leadership chaos at HP and innovation paralysis at Fossil killed a partnership that should have dominated the smartwatch market—handing Apple a $50 billion opportunity. I've shared the complete behind-the-scenes story of the people, strategies, and decisions that killed our partnership in my Studio Notes post “How HP and Fossil Handed Apple the Smartwatch Market.”

Today I'm applying the DECIDE framework to our partnership failure. If you haven't seen my DECIDE framework yet, grab the free PDF—it's the innovation decision tool I've developed over 30 years of making high-stakes choices. Because here's what this partnership taught me: having the right vision means nothing without the right decision framework.

Let me start by explaining why the HP-Fossil partnership should have worked. This wasn't just another business deal—it was the perfect storm of complementary capabilities.

Bill Geiser, Fossil's VP of Watch Technology, had been working on smartwatches since 2004. The man was practically clairvoyant. In 2011, he told me, “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.”

Bill understood something most people missed: Apple didn't need to be first to market—they needed to be first to create a platform.

Meanwhile, I was developing HP's connected device strategy. We had the technology foundation, unmatched retail distribution—about 10% of consumer electronics shelf space—and the same retail muscle that helped launch the original iPod.

Together, Bill and I had solved the hard problems. We had the vision, the technology, and the market insight. But we couldn't overcome the organizational machinery that prioritizes short-term comfort over long-term position.

Innovation partnerships aren't just about having the right technology or market vision. They're about having the right decision framework when uncertainty meets organizational reality.

Before I show you how DECIDE could have saved our partnership, let me show you the three traps that derail even the smartest collaboration. Bill and I understood what needed to happen, but our organizations fell into every one of these traps.

This is when you apply the wrong decision framework because you've misidentified what type of innovation you're actually pursuing. It's the most common partnership killer because different innovation types require completely different approaches to risk, timing, and success metrics.

In our case, Bill and I understood that smartwatches represented a platform opportunity—a new ecosystem that would change how people interact with technology. But our organizations treated it as a product extension that wouldn't threaten their existing businesses.

HP's leadership viewed MetaWatch as another device in their portfolio, rather than as the foundation of a connected ecosystem spanning tablets, phones, and laptops. Fossil's leadership saw it as a “development platform” priced at $200—innovation theater that wouldn't cannibalize their traditional watch sales.

Here's the partnership recognition question: Have you correctly identified what type of innovation you're pursuing together? Because applying incremental decision frameworks to breakthrough opportunities, or product frameworks to platform opportunities, kills partnerships before they can succeed.

This combines revenue protection with organizational risk aversion. Both companies wanted to appear innovative without actually risking their core businesses. HP didn't want to cannibalize enterprise focus. Fossil didn't want to threaten traditional watch revenues.

So instead of going all-in on market creation, both organizations positioned MetaWatch as a “safe” innovation—a development platform for engineers, not a consumer product that could disrupt markets.

Bill faced an impossible organizational reality: Fossil's watch sales had tripled to $3.25 billion during the smartphone era. How do you convince leadership to risk that success for an uncertain new category?

The partnership recognition question: Are you innovating to create markets, or are you innovating to appear innovative while protecting existing revenue?

Bill and I found ourselves fighting the same battle on different fronts: convincing leadership that wearables represented a platform shift, not just a product extension. But even when we had the right vision, we couldn't execute at market speed.

Multiple stakeholders created governance complexity without clear decision authority. We'd have month-long approval cycles for changes that startups could implement in days. I remember one meeting where we spent 45 minutes debating some minor specification while Apple was probably finalizing their entire ecosystem strategy.

The fatal blow came from HP's leadership chaos. We cycled through three CEOs in 13 months, each with completely different visions. When Leo Apotheker arrived, he immediately decided HP should become an enterprise software company, not compete in consumer platforms.

Our partnership needed startup decision velocity, but we were trapped at committee speed across two large organizations with conflicting priorities.

Partnership recognition question: Can you move at market speed together, or are you trapped at the slowest organization's committee speed? Because innovation partnerships under uncertainty require aligned decision velocity.

Now let me show you how the DECIDE framework—which you can download free at philmckinney.substack.com—could have saved our partnership. This isn't theoretical. These are the specific decisions Bill and I needed our organizations to make differently.

Our first mistake was never clearly defining what we were actually deciding as partners. HP saw it as extending our mobile ecosystem. Fossil saw it as diversifying its product line. I saw it as platform creation. Bill saw it as market preparation for the inevitable.

But we never aligned on the fundamental question: Were we creating a new product category together, or were we each using the partnership to serve our existing strategies?

The DECIDE framework forces this clarity upfront. What decision are both partners actually making? Until you have alignment on the decision itself, you can't make it well together.

What we should have defined: “We are deciding to jointly create and dominate the smartwatch platform category before Apple legitimizes i Smartwatch Market.”

Traditional partnership evaluations focus on combining existing capabilities. Innovation partnerships need to evaluate market creation potential.

Bill understood this completely. He predicted exactly what Apple would do. But neither organization had a framework for evaluating market creation partnerships versus product extension partnerships.

For market creation partnerships, you can't study competitors or analyze customer segments that don't exist yet. You need different evaluation criteria focused on timing, platform potential, and ecosystem readiness.

What we should have examined: “Are we positioned to create and own a new category, or are we just building another device?”

Here's where partnerships get interesting. Fossil feared cannibalizing traditional watch sales. HP feared distracting from enterprise transformation. But we never addressed how a partnership could protect both companies from external cannibalization.

The framework question isn't whether the partnership will cannibalize existing business—it's whether the partnership creates better protection than going alone.

What we should have challenged: “What happens if Apple creates this category without us? What market position will each company have then?”

We treated our partnership like an incremental product collaboration when it was actually a breakthrough market creation partnership. These require completely different success metrics, timelines, and risk tolerance.

Incremental partnerships: You can require certainty, detailed market research, and predictable ROI.

Breakthrough partnerships: You have to accept uncertainty, optimize for learning speed, and measure different success metrics like market position and ecosystem readiness.

What we should have identified: “This is breakthrough innovation requiring platform thinking, not incremental innovation requiring product optimization.”

Traditional partnership planning tries to predict success. Innovation partnership planning prepares for multiple futures—together.

We should have built scenarios like: “What if Apple legitimizes this category in two years?” “What if consumers aren't ready for smartwatches?” “What if we succeed and create a billion-dollar market?”

But most importantly: “What if we don't partner and pursue this separately? What if we don't pursue this at all?”

What we should have designed: “Multiple scenarios where our combined capabilities create different outcomes than our individual capabilities.”

This is where our partnership completely failed. We optimized for committee consensus instead of rapid market learning. We needed to fail fast or succeed fast—together.

Instead of quarterly partnership reviews, we needed weekly sprint cycles. Instead of seeking approval for perfect plans, we needed permission to test and iterate quickly.

What we should have executed: “Rapid market validation cycles where both partners learn and adapt together, not separate approval processes that slow everything down.”

Want to apply this to your current innovation partnerships? Here's a 30-second audit using the DECIDE framework:

If you can't answer these questions clearly with your partners, you're not ready to make breakthrough innovation decisions together. And that's okay—clarity about partnership uncertainty is better than false confidence in flawed collaboration.

Let me be clear about what DECIDE would and wouldn't have guaranteed for our partnership.

Would this framework have eliminated uncertainty? No. Nothing eliminates uncertainty in breakthrough innovation partnerships.

Would it have guaranteed success? No. Even perfect decision frameworks can't overcome poor market timing or execution failures.

But would it have given the HP-Fossil partnership a fighting chance? Absolutely. It would have aligned our decision-making process with the type of decision we were actually making together.

Most importantly, it would have helped us recognize that we were making a platform partnership decision, not a product partnership decision. That recognition alone might have changed our entire approach to market timing, resource allocation, and success metrics.

Innovation partnerships aren't about having the best technology or the smartest people—they're about using the right decision framework when uncertainty meets organizational reality.

Bill Geiser and I had the vision, the technology, and the market insight. But our organizations couldn't execute on what we knew was right because we didn't have the decision framework to bridge individual company interests with partnership potential.

The DECIDE framework won't eliminate uncertainty—nothing can. But it will help you and your partners make better choices under uncertainty, which is what innovation partnership leadership actually requires.

So here's my challenge for you: What innovation partnership opportunity is your organization considering right now? Apply the DECIDE framework with your potential partners and see what you discover. Are you making the right type of decision together? Are you optimizing for the right outcomes? Are you prepared for multiple scenarios as a partnership?

Because somewhere out there, another brilliant partnership is about to hand their breakthrough innovation to a competitor through bad decision-making. Don't let it be yours.

The framework is free at philmckinney.substack.com. For the complete story of how organizational failures killed our partnership—including the specific people and pivotal moments that changed everything—read “How HP and Fossil Handed Apple the Smartwatch Market” in my Studio Notes. The partnership opportunity is waiting. The choice is yours.

What will you and your partners decide together?

To learn from the innovation partnership autopsy of HP and Fossil’s lost chance at market domination, listen to this week's show: Innovation Partnership Autopsy: HP, Fossil, and the Smartwatch Market.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

You know that moment when you walk into a meeting and immediately sense the mood in the room? Or when a proposal looks perfect on paper, but something feels off? That's your intuition working—and it's more sophisticated than most people realize.

Every leader has experienced this: sensing which team member to approach with a sensitive request before you've consciously analyzed the personalities involved. Knowing a client is about to object even when they haven't voiced concerns. Feeling that a project timeline is unrealistic before you've done the detailed math.

That instinctive awareness isn't luck or mystical insight—it's your brain rapidly processing patterns, experience, and environmental cues. The leaders known for “amazing judgment” haven't been blessed with superior gut feelings. They've learned to systematically enhance this natural capability through practical thinking.

By the end of this post, you'll understand the science behind intuitive judgment, why some people seem to have consistently better instincts, and how to use Practical Thinking Skills to make your own intuition more reliable and actionable.

Intuition is your brain's rapid processing of experiences, patterns, and environmental cues that occur below the level of conscious awareness. When you sense the mood in a room, your mind is instantly analyzing dozens of subtle signals: body language, tone of voice, seating arrangements, who's speaking and who's staying quiet.

This isn't mystical—it's sophisticated pattern recognition. Your brain has stored thousands of similar situations and can quickly compare current circumstances to past experiences, delivering a “gut feeling” about what's likely to happen or what approach will work.

Everyone has this capability. You use it constantly:

The difference between people with “great intuition” and everyone else isn't the quality of their initial gut feelings—it's how systematically they validate, investigate, and act on those insights.

Leaders who are known for excellent judgment have developed what I call practical thinking—the systematic approach to using their knowledge and experience to enhance their intuitive insights.

Here's what they do differently:

They treat gut feelings as valuable data, not emotions to dismiss or blind impulses to follow. When something feels off, they investigate systematically rather than ignoring the signal or acting without validation.

They've learned to distinguish between intuition based on genuine patterns and reactions driven by personal bias, stress, or recent events. They can separate “this timeline feels aggressive because similar projects have failed” from “this timeline feels aggressive because I'm overwhelmed today.”

They apply structured approaches to validate their intuitive insights before making important decisions. They don't just trust their gut—they use their gut as the starting point for systematic investigation.

They understand stakeholder psychology at a deeper level, using their intuitive read of people to design approaches that work with human nature rather than against it.

The leaders with reputations for “brilliant intuition” have simply learned to make their natural pattern recognition more reliable and actionable through systematic frameworks.

Practical thinking is the systematic approach to using your knowledge and experience to validate, investigate, and effectively implement your intuitive insights. It transforms valuable gut feelings into consistently reliable judgment.

Think of intuition as your brain's early detection system, and practical thinking as the methodology for investigating and acting on those signals systematically.

Your intuition signals: “This reorganization plan feels wrong.”

Practical thinking investigates: “What specific elements am I reacting to? Is it the timeline, the stakeholder alignment assumptions, or the communication approach?”

Your intuition warns: “This customer seems hesitant despite saying yes.”

Practical thinking explores: “What might they be worried about that they can't voice directly? How can I address their real concerns?”

Your intuition detects: “This team meeting feels tense.”

Practical thinking examines: “What underlying conflicts or pressures might be driving this dynamic? What does each person need to feel successful?”

When you combine intuitive insight with systematic investigation frameworks, you develop judgment that gets more accurate with experience. This is how great leaders seem to “just know” what will work—they've learned to systematically validate and act on the patterns their intuition detects.

The framework consists of three interconnected skills that transform your natural intuitive insights into reliable decision-making capabilities. Unlike traditional analytical approaches that ignore gut feelings or emotional approaches that follow instincts blindly, practical thinking creates a systematic bridge between your intuitive awareness and effective action. The power comes from combining all three skills—most people excel at one or two but miss the integration that makes intuitive judgment consistently reliable.

What it is: Use your intuitive insights to see situations as they actually exist, not as frameworks or org charts suggest they should be structured.

Why it matters: Your gut feelings often detect mismatches between official reality and actual reality. Most decision failures happen because people address the stated situation rather than the real situation.

How to apply it:

Example in action: Your gut says a reorganization plan “won't work” even though it looks logical on paper. Reality Recognition helps you investigate: your intuition is detecting that the timeline is too aggressive for this culture, key influencers weren't consulted, and the plan ignores current team workload realities. Your gut feeling was accurate—it sensed the gap between the plan and actual organizational dynamics.

Common mistake: Dismissing gut feelings because the official version looks reasonable.

What it is: Use your knowledge of similar situations and these specific stakeholders to adapt your approach, rather than applying generic solutions.

Why it matters: Your intuition draws on your unique experience with these people, this culture, and these types of challenges. That contextual knowledge is more valuable than best practices from other organizations.

How to apply it:

Example in action: Your intuition suggests that a client is hesitant about a proposal despite their positive words. Experience Application helps you investigate: you remember that this client typically asks detailed implementation questions when they're serious, but they haven't asked any. Your experience with them suggests they're worried about execution complexity but don't want to seem unsophisticated. You adapt by proactively addressing implementation support rather than pushing for a decision.

Common mistake: Researching what worked elsewhere instead of applying what you know about this specific context.

What it is: Decode what people really need and fear versus what they say they need, then design approaches that align with their actual psychology.

Why it matters: Most initiatives fail not because of logic or resources, but because they clash with stakeholders' deeper motivations, fears, or unspoken constraints that no one addresses directly.

How to apply it:

Example in action: A department head enthusiastically supports your efficiency initiative in meetings, but your gut says they're not really on board. Psychology Reading reveals they're actually worried that improved efficiency will make their large team look unnecessary, threatening their status and job security. Instead of focusing on efficiency benefits, you reframe the initiative as expanding their team's capabilities and strategic value. This transforms resistance into genuine partnership.

The key insight: While others try to convince stakeholders with better arguments, practical thinkers recognize that people's real decisions are driven by emotions, incentives, and fears that are rarely discussed openly. They design solutions that address these psychological realities, not just the stated requirements.

Common mistake: Focusing on logical persuasion when stakeholders are driven by emotional or political concerns they can't voice directly.

The 30-Day Practical Thinking Challenge

Week 1: Reality Recognition Practice Start documenting your gut feelings about situations and meetings. When something feels off, practice investigating what your intuition might be detecting. What mismatches between official reality and actual reality is your gut picking up on?

Week 2: Experience Application For each significant decision you face, consciously apply your experience with these specific people and situations. Instead of researching best practices, ask: “What do I know from previous similar situations with these stakeholders that should guide my approach?”

Week 3: Psychology Reading Practice reading stakeholder psychology by listening for what people don't say. When someone agrees quickly or shows unexpected enthusiasm, ask yourself: “What might they be worried about that they can't voice directly?” Design small experiments to test your psychological reads.

Week 4: Integrated Framework Combine all three steps for important decisions. Start with your intuitive read (Reality Recognition), apply your contextual experience (Experience Application), then design approaches that address stakeholder psychology (Psychology Reading). Track when this systematic approach improves outcomes.

Practice with others: Share your intuitive insights with trusted colleagues and walk through your practical thinking process. Explaining why you're getting a particular gut feeling often reveals additional insights and helps validate your approach.

Success indicators: You'll know your practical thinking skills are enhancing your intuition when your gut feelings become more accurate, when stakeholders volunteer information they usually keep hidden, and when your solutions work better because they align with how people actually think and behave.

Your intuition is already providing sophisticated insights about stakeholder dynamics, organizational realities, and what will actually work in practice. The challenge isn't developing better gut feelings—it's learning to systematically validate and act on the insights you're already receiving.

Practical thinking doesn't replace your natural judgment—it makes it more reliable, more explainable to others, and more actionable in complex situations. When you can read reality accurately, apply your experience systematically, and understand stakeholder psychology, you develop the type of judgment that improves with every decision.

This is the foundation of Decision Thinking—the approach I've developed for making effective decisions when information is incomplete, stakeholders have conflicting interests, and the stakes are high. Practical thinking helps you leverage your natural capabilities to navigate complexity that would overwhelm purely analytical approaches.

You already have sophisticated pattern recognition and stakeholder awareness. Practical thinking helps you turn those natural capabilities into systematic competitive advantages.

Ready to develop reliable, practical thinking? Join our community of leaders in Substack Chat where we're exploring this question: What's one situation where you had a strong gut feeling that something was off, but you didn't trust it enough to investigate—and later wished you had?

In the next episode, we will examine how the HP-Fossil partnership could have challenged Apple's dominance in the smartwatch market. Fossil’s Bill Geiser's intuition about Apple entering wearables was dead-on accurate. My assessment of the platform shift was equally accurate. But intuition without the decision-making frameworks to act on it just becomes expensive foresight. It will show how two Fortune 500 giants chose comfort over courage.

Share this with someone who has great instincts but struggles to act on them systematically—you'll be helping them turn natural judgment into reliable leadership capability.

To learn how intuitive judgment works, why some leaders seem to have better instincts, and how practical thinking makes gut feelings more reliable in complex situations, listen to this week's show: Why Great Innovators Read Rooms, Not Just Data.

Picture this: You're in a conference room with 23 executives, everyone has perfect PowerPoint presentations, engineering milestones are ahead of schedule, and you're about to sign off on a $25 million bet that feels like a sure thing.

That was the scene at HP when we were developing the Envy 133—the world's first 100% carbon fiber laptop. Everything looked perfect: engineering was ahead of schedule, we projected a $2 billion market opportunity, and the presentations were flawless.

Six weeks after launch, Apple shifted the entire thin-and-light laptop market, and our “sure thing” became a $25 million cautionary tale about decision-making.

Here's what I discovered: Your people aren't lying to you—they're protecting you. Every layer of management unconsciously filters out inconvenient truths. We had two massive blind spots:

Information in organizations goes through more filters than an Instagram photo. Each management layer edits out inconvenient truths—not from malice, but from basic human psychology. People want to be helpful, to be problem-solvers, to avoid being bearers of bad news.

I started treating information like a scientist treats data, using three temperature checks:

Technique 1: Pre-Mortem Confessions

Anonymous submission of biggest fears before major decisions. Read aloud without attribution to remove personal risk and stress-test plans against criticisms.

Technique 2: Messenger Reward System

Formally reward people who bring bad news, not just problem-solvers. Recognition in leadership meetings and promotion consideration. Within six months, intelligence quality improved dramatically.

Technique 3: Devil's Advocate Rotation

Assign someone to formally challenge assumptions in every major presentation. Rotate among team members to institutionalize dissent and make doubt safe to express.

Technique 4: Customer Voice Channel

Spend 25% of time with direct customer contact. This included executive briefings but also weekends in retail stores watching real customer behavior. The gap between what customers wanted and what product teams assumed was staggering.

Technique 5: Failure Story Requirement

Every presentation must include one failure story—not dwelling on failures, but incorporating lessons from setbacks into decision-making.

I developed a six-factor scorecard (1-5 scale) to measure information quality:

Review quarterly—scores below 3 signal information silos are forming.

Building a truth-telling culture isn't about finding better people—it's about creating better systems for handling difficult information. The market will always contain signals that contradict your plans. The question is whether those signals can survive the journey to your desk.

This Week's Challenge: Try one technique—run a pre-mortem confession on your next major decision or assign a devil's advocate to your next presentation. Small changes in how you handle information can prevent million-dollar mistakes.

For the complete Truth-Telling Scorecard and detailed frameworks, visit Phil's Studio Notes on Substack. For the full backstory on the HP Envy 133 project, including all the details, check out the complete article there.

Subscribe to the Killer Innovations Podcast | Watch on YouTube

To learn more about why best employees are sabotaging decisions, listen to this week's show: Why Your Best Employees Are Sabotaging Your Decisions (And How to Fix It).

The framework for spotting billion-dollar opportunities when they look like terrible ideas

Every breakthrough innovation starts the same way: everyone thinks it's a terrible idea. Twitter was dismissed as “breakfast updates.” Google looked “too simple.” Facebook seemed limited to “just college kids.”

Yet these “stupid ideas” became some of the biggest winners in tech history. After 30 years making innovation decisions at Fortune 100 companies, I've identified why smart people consistently miss breakthrough opportunities—and how to spot them before everyone else does.

The problem isn't intelligence or experience. It's that we ask the wrong questions when evaluating new innovations. We filter breakthrough ideas through frameworks designed for incremental improvements, not revolutionary changes.

Most innovation decisions fail because of three specific thinking traps that cause us to dismiss ideas with the highest potential for transformation.

The Question That Kills Innovation: “What existing problem does this solve?”

Why It's Wrong: Breakthrough innovations don't solve existing problems—they create entirely new behaviors and meet needs people don't even know they have.

Real-World Example: Airbnb seemed insane when it launched. Staying with strangers? Seeing them in the kitchen? The “problem” it solved—expensive hotels—wasn't what made it revolutionary. It created an entirely new behavior: experiential travel that hotels couldn't provide.

The Better Question: “What new human behavior could this enable?”

The Question That Kills Innovation: “Where are all the features? This looks too basic.”

Why It's Wrong: Simplicity isn't a lack of sophistication—it's the hardest thing to achieve. When something is designed to be insanely simple to use, that signals massive effort and thought behind the design.

Real-World Example: Google was just a white page with a search box while Yahoo crammed everything onto their homepage. Google looked unprofessional and incomplete, but it eliminated complexity everyone thought was necessary.

The Better Question: “What complexity is this eliminating?”

The Question That Kills Innovation: “How big is the addressable market? Why limit yourself so severely?”

Why It's Wrong: Breakthrough innovations don't serve existing markets—they create entirely new markets. The biggest opportunities come from ideas that seem too niche or focused.

Real-World Example: Facebook was just for college students requiring .edu email addresses. Critics said the market was too narrow. But social media users didn't exist before Facebook—the company created the entire market.

The Better Question: “What market could this create?”

When evaluating ideas that seem “stupid” or “too simple,” use this three-question filter:

These questions force you to look beyond surface-level problems and features to identify transformational potential.

For Investors: Stop asking “What problem does this solve?” Start asking “What behavior does this create?”

For Product Teams: Stop adding features. Start eliminating complexity.

For Leaders: Stop looking for big existing markets. Start looking for new market creation potential.

For Innovators: Stop following what everyone else thinks is smart. Start looking for ideas that violate conventional wisdom.

The current AI boom follows the exact same pattern as the dot-com bubble. Every company is racing to add AI to their pitch, just like they added “.com” in 1999.

But the real breakthrough opportunities? They're probably something completely different—ideas that look terrible to everyone following the AI herd.

The companies that will win are those that can recognize breakthrough potential when it violates everything the market thinks is smart.

Recognition is only half the battle. The hardest part is having the courage to act on opportunities when they contradict expert opinion and market consensus.

The biggest question isn't whether you can spot these opportunities—it's whether you'll have the conviction to pursue them when everyone else thinks they're terrible ideas.

Because twenty years from now, someone will be writing about the “stupid idea” they missed in 2025 that became the next trillion-dollar company.

This framework came from some painful (and expensive) lessons about dismissing breakthrough ideas. I share the full story—including how I wrote off the team that created Twitter after Apple destroyed their original business—in this week's Studio Notes.

Listen to the full analysis: Subscribe to the Killer Innovations podcast for deeper dives into innovation decision frameworks.

See the framework in action: Watch my case study of how these exact decision traps led to HP's $1.2 billion WebOS disaster.

To learn more about innovation decision traps that kill breakthrough ideas, listen to this week's show: 3 Innovation Decision Traps That Kill Breakthrough Ideas (And How to Avoid Them).

In 2011, HP killed a $1.2 billion innovation in just 49 days. I was the Chief Technology Officer who recommended buying it. What happened next reveals why smart people consistently destroy breakthrough technology—and the systematic framework you need to avoid making the same mistake.

HP had just spent $1.2 billion acquiring Palm to get WebOS—one of the most advanced mobile operating systems ever created. It had true multitasking when iOS and Android couldn't handle it, an elegant interface design, and breakthrough platform technology. I led the technical due diligence and recommended the acquisition because I believed we were buying the future of mobile computing.

Here's a question that should keep every innovation leader awake at night: How do you destroy breakthrough technology worth over a billion dollars in less than two months?

The answer isn't what you think. It's not about bad technology, poor market timing, or insufficient resources. It's about systematic thinking errors that intelligent people make when evaluating innovation under pressure. And these same patterns are happening in companies everywhere, right now.

I'm going to show you exactly how this happens, why your company is vulnerable to the same mistakes, and give you a proven framework to prevent these disasters before they destroy your next breakthrough innovation.

On my Studio Notes on Substack, I share the personal story of watching this unfold while recovering from surgery. In this episode, I want to focus on the systematic patterns that caused this disaster and the decision framework that can prevent it.

Read Studio Notes on Substack

Here's my promise: by the end of this episode, you'll understand the five thinking errors that consistently destroy innovation value, you'll have a complete decision framework to avoid these traps, and you'll know exactly how to apply this to your current innovation decisions.

Because here's what this disaster taught me: intelligence doesn't predict decision quality. Systematic thinking frameworks do.

Let me start with the fundamental problem that makes these disasters predictable. When the HP Board hired Leo Apotheker as CEO, they created what I call a “cognitive mismatch,” and it reveals why smart people make terrible innovation decisions.

Apotheker came from SAP, where he'd run a $15 billion software company. HP was a $125 billion technology company with breakthrough mobile platform technology. The board put someone whose largest organizational experience was half the size of HP's smallest division in charge of evaluating platform innovations he'd never encountered before.

But here's the crucial insight: the problem wasn't his experience level. The problem was how his professional background created mental blind spots that made him literally unable to see WebOS as an opportunity.

Here's what's dangerous: Apotheker couldn't see WebOS as valuable because his entire career taught him that software companies don't do hardware. His brain was wired to see hardware as a distraction, not an advantage. To him, WebOS represented exactly the kind of hardware business he wanted to eliminate.

Your expertise becomes your blind spot. You literally can't see opportunities outside your professional comfort zone.

And this is the first critical principle: Your job background creates mental filters that determine what opportunities you can even see.

And this pattern is happening in your company right now. Your finance team evaluates platform investments using metrics designed for traditional products. Your marketing team rejects concepts they can't explain with existing frameworks. Your engineers dismiss breakthrough ideas that don't fit current technical roadmaps.

The pattern is always identical: intelligent people using the wrong thinking frameworks to evaluate breakthrough technology. Let me show you exactly how this destroys innovation value.

WebOS died because of five predictable cognitive errors that occur when smart people evaluate breakthrough technology under pressure. These aren't unique to HP—I've seen identical patterns destroy innovation value across multiple industries.

The most dangerous mistake happens before you evaluate any options: framing the wrong decision question.

Apotheker was asking “How do I transform HP into a software company?” when the strategic question was “How do we build competitive advantage in mobile computing platforms?” When you optimize solutions for the wrong problem, you get excellent answers that destroy strategic value.

The Warning Sign: Your team jumps straight to evaluating options without questioning whether you're solving the right challenge.

Your professional background creates systematic blind spots about breakthrough opportunities.

Software executives see software solutions. Hardware leaders focus on hardware opportunities. Financial experts optimize for traditional metrics. This cognitive filtering happens automatically and distorts how you evaluate platform technologies that don't fit conventional categories.

The Warning Sign: Your evaluation team all have similar backgrounds and reach the same conclusions about breakthrough technology.

When executives become obsessed with major initiatives, everything else feels like a distraction.

Apotheker became obsessed with acquiring Autonomy, a software company that fit his transformation vision. This tunnel vision made everything else—including breakthrough mobile technology—feel like a distraction from his primary goal.

The Warning Sign: Leadership dismisses promising innovations because they don't support the current primary initiative.

Platform technologies require different evaluation timeframes than traditional products.

Forty-nine days isn't enough time to build developer ecosystems, establish retail partnerships, or demonstrate platform traction. But pressure to show decisive leadership compressed HP's decision timeline artificially, creating the illusion of strong leadership while increasing the probability of strategic errors.

The Warning Sign: Your team is evaluating breakthrough technology using the same timelines as conventional product launches.

Innovation decisions require fundamentally different success metrics than traditional business evaluation.

HP focused on TouchPad sales numbers instead of developer adoption rates, user engagement patterns, or platform differentiation sustainability. They used product metrics to evaluate platform potential, which guaranteed they would see failure instead of recognizing early-stage ecosystem development.

The Warning Sign: You're applying traditional business metrics to evaluate breakthrough technology investments.

Here's what makes these errors so dangerous: they're invisible to the people making them. Smart teams use these flawed frameworks and feel confident they're making data-driven decisions while systematically destroying innovation value.

But these patterns are preventable. After analyzing hundreds of similar disasters, I developed a systematic framework specifically designed to avoid these thinking traps.

The DECIDE framework addresses each cognitive vulnerability that consistently traps intelligent leaders in innovation contexts. Let me show you exactly how it works and why it would have saved WebOS.

Most innovation failures begin with teams optimizing excellent solutions for poorly defined problems.

The Tool: Reframe your decision question three different ways. If all three point to the same choice, you're probably asking the right question. If they point to different choices, you need to determine which frame captures the real strategic challenge.

Examples of Different Frames:

HP's Application:

What This Reveals: The reframes show TouchPad was one product in a larger platform opportunity that deserved different evaluation criteria entirely.

Your professional background creates invisible filters that can systematically distort how you interpret breakthrough opportunities.

The Tool: If you hired someone with completely different expertise to make this decision, what would they choose? When the gap is huge, you need outside perspectives with different cognitive frameworks.

HP's Gap: Enterprise software CEO versus consumer platform strategy requirements. They needed mobile platform thinking, not enterprise software optimization, but never brought that expertise into the decision process.

The most dangerous assumptions feel like established facts and shape your entire analysis without being examined.

The Tool: What would have to be true for your least favorite option to actually be the right choice? This forces you to consider alternative interpretations of the same evidence.

HP's Assumptions: Platform businesses need immediate profitability, mobile computing won't dominate, differentiated operating systems can't compete with Apple and Google. All of these assumptions were provably false by 2011, but they drove the evaluation process.

Different types of decisions trigger predictable cognitive biases that distort evaluation in systematic ways.

The Tool: Which specific biases is your decision most vulnerable to? Create explicit countermeasures for each identified bias.

Common Innovation Decision Biases:

HP's Specific Traps:

Most innovation failures result from evaluating limited options well rather than evaluating good options poorly.

The Tool: Generate five genuinely different approaches before evaluating any of them. Breakthrough solutions often emerge from non-obvious alternatives.

HP's Missing Options: License WebOS to manufacturers, integrate into PC ecosystem, pivot to enterprise mobile, create hybrid hardware-software strategy. All had genuine potential but were never seriously considered.

Platform technologies require fundamentally different success metrics than traditional product evaluation.

The Tool: What evidence would predict success for this specific type of innovation? Use frameworks appropriate for breakthrough technology, not conventional business metrics.

HP's Error: They used quarterly sales performance and immediate profitability to evaluate platform potential. Platform businesses lose money initially while building network effects that create sustainable advantages later.

Let me show you how to use this framework with your current innovation decisions.

Step One: Identify Your Highest-Stakes Innovation Decision

What breakthrough technology, platform investment, or disruptive opportunity is your team evaluating right now? This framework applies to any decision where traditional business metrics might mislead about innovation potential.

Step Two: Run the Decision Question Test

Before evaluating any options, reframe your decision question three different ways. Are you asking “How do we minimize risk?” or “How do we maximize strategic opportunity?” The frame determines the solutions you'll even consider.

Step Three: Audit Your Evaluation Team

Who's making this decision? What cognitive filters might their backgrounds create? Do you need advisors with different expertise to see opportunities your current team might miss?

Step Four: Challenge Your Obvious Assumptions

What would have to be true for the option you least prefer to actually be right? Those conditions might exist or be emerging faster than you realize.

Step Five: Identify Your Decision Traps

Is your team vulnerable to loss aversion? Anchoring on early data? Tunnel vision around other initiatives? Create specific countermeasures for each identified bias.

Step Six: Generate Multiple Approaches

Push beyond obvious choices. What would someone from a completely different industry do? What creative alternatives combine elements from different options?

Step Seven: Use Appropriate Evidence

Are you evaluating platform potential with product metrics? Breakthrough technology with conventional criteria? Innovation investments with traditional business frameworks? Match your evidence to your innovation type.

Download DECIDE Framework Toolkit

The DECIDE framework works because it addresses the specific cognitive vulnerabilities that consistently trap intelligent people in innovation contexts.

Traditional decision-making assumes you know the right questions to ask, can see opportunities clearly, and will use appropriate evaluation criteria. Innovation decisions violate all these assumptions. Breakthrough technologies don't fit existing categories. Platform investments don't follow traditional timelines. Disruptive opportunities can't be evaluated with conventional metrics.

The companies that consistently succeed at innovation aren't smarter—they use systematic frameworks designed for uncertainty, breakthrough potential, and non-obvious opportunities.

Three Companies Getting This Right:

Amazon evaluates platform investments with different metrics than product launches. They expected Kindle, AWS, and Prime to lose money initially while building long-term competitive advantages.

Google uses systematic frameworks to avoid identity bias in breakthrough technology evaluation. Android didn't fit their search advertising identity, but they evaluated it with platform-appropriate criteria.

Apple applies different decision frameworks to breakthrough products versus incremental improvements. They gave iPhone multiple years to build ecosystem momentum instead of expecting immediate profitability.

These companies avoid the systematic thinking errors that destroyed WebOS because they use decision frameworks designed for innovation uncertainty.

Here's the reality: this challenge isn't going away. Breakthrough technologies will continue emerging faster than traditional business frameworks can evaluate them. The companies that develop systematic innovation decision capabilities will capture enormous value. Those that rely on conventional thinking will consistently destroy breakthrough opportunities.

Your Three Action Steps:

First: Download the DECIDE Framework toolkit and apply it to your current highest-stakes innovation decision before evaluating any options.

Second: Audit your innovation evaluation processes. Are you using traditional business metrics to evaluate breakthrough technology? Conventional timelines for platform investments? Identity-driven thinking for disruptive opportunities?

Third: Build systematic innovation decision capabilities into your organization. Train your team to recognize cognitive biases, use appropriate evidence frameworks, and generate multiple creative alternatives.

Questions to Consider:

But here's the final piece of this story that shows just how costly these thinking errors can be: Leo Apotheker was fired on September 22, 2011—just 35 days after shutting down WebOS and eleven months after taking over as CEO. The board finally recognized the systematic thinking errors that had destroyed billions in value, but it was too late for WebOS.

The human cost of these decisions goes beyond stock prices and quarterly reports. There are real people who believed in breakthrough technology, fought for innovation, and had to watch it get destroyed by preventable thinking errors.

The complete personal story of watching this disaster unfold—including details about the brutal aftermath and why I still believe in HP despite everything—is in this week's Studio Notes over on Substack. 

Read Studio Notes on Substack

Remember: when you have breakthrough technology in your hands, the quality of your decision-making process matters more than the quality of your technology. Intelligence and good intentions aren't enough. You need systematic frameworks for thinking clearly about innovation under uncertainty.

The tools exist to prevent these disasters. The question is whether you'll implement them before your next WebOS moment.

Remember—in a world where billion-dollar innovations can be killed in 49 days, systematic decision frameworks might be your most valuable competitive advantage.

If you found this week's episode valuable, subscribe to the podcast or watch on the YouTube channel.

To learn more about HP's WebOS failure and the innovation decision-making framework, listen to this week's show: The $1.2 Billion Innovation Disaster: 5 Decision Mistakes That Kill Breakthrough Technology (HP WebOS Case Study).

University of Washington researchers discovered something that should concern every parent: children who use AI to create can no longer create without it. And here's the concerning part: most parents have absolutely no idea it's happening.

If you've been following our series on Creative Thinking in the AI Age, you know I've been tracking how artificial intelligence is rewiring human creativity. We've explored the 30% decline in creative thinking among adults, the science of neuroplasticity, and practical exercises to rebuild our creative capabilities.

But today's episode is different. Today, we're talking about your child's developing brain. And I need to be direct with you—the next 30 minutes might be the most important parenting conversation you have this year.

Because while we've been worried about AI taking our jobs, it's already changing our children's minds. Unlike us adults, who developed our creative thinking before AI existed, our kids are growing up with artificial intelligence as their creative co-pilot from the very beginning.

Here's my promise to you: by the end of this episode, you'll know exactly how to tell if your child is developing AI dependency, you'll understand why their developing brain is more vulnerable than yours, and you'll have an assessment tool to evaluate your family's situation—plus immediate strategies you can start using today.

But first, let me show you what's happening in homes just like yours—and why this is both preventable and completely reversible.

A few weeks ago, a mother shared a story that stopped me in my tracks. Her 10-year-old daughter used to spend hours drawing elaborate fantasy worlds, completely absorbed in her creative process. Now, when her mother suggests drawing something, the daughter responds, ‘Can I just use AI to make it look better?' At first, this seemed like smart efficiency—why not use available tools? However, when the mother asked her daughter to draw a simple picture with no digital help, something alarming occurred. The child just stared at the blank paper and started crying, unable to create anything on her own.

This story isn't unique. It's happening everywhere, and parents are missing it because the signs look like success.

Before we go further, let me be clear: this isn't your fault. AI dependency developed gradually, and most parents missed the early signs because they actually looked positive.

Think about your own child for a moment. Has their homework gotten easier? Do they finish writing assignments faster than they used to? Are their projects suddenly more polished? If you answered yes, you might be looking at what I call the “homework mirage.”

Here's what the homework mirage looks like: Your child sits down to write a story for English class. Instead of staring at the blank page like kids have done for generations, they open ChatGPT. They type: “Write me a story about a brave knight.” In thirty seconds, they have three paragraphs that would have taken them an hour to write.

You see the finished assignment. It's well-written, grammatically correct, and creative. You think, “Great! They're learning to use technology efficiently.” But here's what you don't see: your child's brain just missed a crucial workout.

Remember in our first episode when we talked about brain pathways being like muscles? When we don't use them, they weaken. This is happening to children at a speed that concerns researchers worldwide. (Reference: Newman, M. et al., 2024, “I want it to talk like Darth Vader: Helping Children Construct Creative Self-Efficacy with Generative AI,” University of Washington)

Dr. Ying Xu from Harvard put it perfectly when she asked the critical question: “Are they actually engaging in the learning process, or are they bypassing it by getting an easy answer from the AI?”

And here's the concerning part—kids who use AI to complete tasks do produce higher quality work in the short term. But when you take the AI away, their abilities are worse than before they started using it.

But this goes way beyond homework. Children are experiencing what experts call the “Creative Confidence Crisis.” Kids who used to love making art now say, “I'm not good enough” when they see AI-generated images. Children ask AI to help with simple creative tasks, such as making up games or telling stories.

The scale of this problem is significant. Recent research shows that 31% of teenagers are already using AI to create pictures and images. Sixteen percent are using it to make music. And parents? Most have no idea how much their children are depending on these tools.

As one researcher told me, “Parents and teachers are pretty much out of the loop, so young people are using AI platforms with virtually no guidance.”

This brings us to a crucial question: Why are children more vulnerable to this than adults?

In our second episode, we explored neuroplasticity—your brain's ability to reorganize itself throughout your life. But children's brains aren't just plastic; they're in active construction mode.

Think of an adult brain like a well-established city with roads and infrastructure already built. A child's brain is more like a city being built from scratch. The roads they travel most frequently become the highways of their adult thinking.

This is why the creative pathways your child develops now will determine their innovative capabilities for life. While AI can already outperform humans at data analysis, writing, and even coding, it cannot replicate the uniquely human ability to make unexpected connections, challenge assumptions, and imagine what doesn't yet exist. 

The children who develop strong creative thinking skills today will be the ones who thrive in tomorrow's AI-dominated world—they'll be the innovators, entrepreneurs, and problem-solvers who can work with artificial intelligence without being replaced by it.

These future-critical abilities depend on four specific creative thinking systems that are strengthened or weakened based on how children use them. When children become AI-dependent, these four systems are at risk:

Cognitive Flexibility—your child's ability to switch between different thinking modes. This is what allows them to see a cardboard box as a spaceship, then a house, and then a robot costume. When children always ask AI, “What should I make?” instead of experimenting, this flexibility is weakened.

Associative Thinking—connecting seemingly unrelated ideas. This is how kids come up with wild and wonderful ideas, like “What if cars could swim?” When AI provides ready-made connections, children stop making their own unique associations.

Divergent Thinking—generating multiple solutions to open-ended problems. AI excels at convergent thinking—identifying the best answer. But human creativity thrives on divergent thinking—exploring all possible answers.

Constraint Breaking—the ability to identify and overcome hidden assumptions limiting their thinking. This is what lets children question rules like “stories have to make sense” or “art has to look realistic.” When AI always provides solutions within conventional parameters, children stop challenging the boundaries of what's possible.

When these systems weaken, children develop what is called “Creative Bypass Syndrome.” They learn to jump straight to AI whenever they encounter creative challenges. Their brains literally rewire to avoid the hard work of original thinking.

But there's another crucial element that supports all four of these systems: frustration tolerance—your child's ability to persist through difficult problems without immediate relief. This is where the real creative magic happens. Those moments when your child sits with a problem, feels stuck, pushes through the discomfort, and discovers something unexpected. AI eliminates this essential struggle by providing instant solutions.

Think about the last time you watched your child work through a challenging puzzle or try to build something that kept falling down. That frustration they felt? That's their brain building resilience and creative persistence. When children can immediately turn to AI for answers, they miss these crucial mental workouts.

But here's the encouraging news: because children's brains are so adaptable, they can also recover faster than adults. The creative pathways that have weakened can be rebuilt. The confidence that's been lost can be restored.

Now, before we talk solutions, you need to know where your child stands right now.

I've developed a simple test that you can do at home to evaluate your child's creative resilience. You can download the complete assessment tool from our website, but let me walk you through the key elements right now.

Important setup instructions: Set aside 15 uninterrupted minutes for this assessment. Choose a time when your child is relaxed, not rushed or hungry. Find a quiet space—the kitchen table works perfectly. Have other siblings play elsewhere during the test. If your child resists or asks “why,” simply say “I'm curious about something” and keep it light.

The assessment is based on one fundamental principle: creative confidence shows up in how children respond to open-ended challenges with no right answer.

For ages 5 to 8, try what I call the “Magic Box Challenge.” Give your child an empty cardboard box—a shoe box works perfectly. Tell them: “This is a magic box that can become anything you want. Show me what you'd like it to be.”

Then step back and observe. Don't give suggestions. Don't offer help. Don't provide materials unless they specifically ask. Just watch how they respond.

For ages 9 to 12, try the “Problem Inventor Challenge.” Ask your child to invent a problem that needs solving, then solve it. Give them exactly ten minutes. No devices, no external input. Say: “Pretend you're an inventor. What problem would you want to solve, and how would you solve it?”

For teenagers, ages 13 to 17, use the “Original Idea Test.” Ask them to come up with an original, creative project idea in any medium—art, writing, music, video, anything. They need to explain why this idea interests them personally. Give them up to ten minutes and say: “If you could create anything right now—no limits on time or resources—what would you make and why?”

Now, let me show you how to score what you observe. As you watch your child during their assessment, use this scoring guide to identify which traits they exhibit. You can circle or check off the behaviors you notice, then see which zone has the most matches.

Beyond these specific tests, there are daily warning signs every parent should recognize:

Watch for homework that suddenly takes much less time than usual. Notice if their writing voice changes dramatically—AI-generated text often sounds more sophisticated than your child's natural voice, but it lacks their personality. Pay attention to decreased tolerance for boredom. Listen for language changes—are they asking “How do I…” instead of experimenting?

This scoring guide will help you determine if your child is in the Green Zone (creative confidence intact), Yellow Zone (some AI dependence developing), or Red Zone (significant creative confidence impact).

If you discover your child is in the Yellow or Red Zone, take a deep breath. Remember, children's brains are remarkably adaptable—this can absolutely be improved. Let me show you how.

If your assessment revealed concerning signs, here's your “start here” action plan: If you do nothing else this week, try the assessment. If your child is in Yellow or Red Zone, then implement the 24-hour creative pause I'm about to describe.

I want you to try what I call the “24-Hour Creative Independence Check.” This is a gentle but effective way to help your child reconnect with their natural creative abilities.

Step One is the Creative Pause Period. For twenty-four hours, your child takes a break from AI assistance for any creative task. No ChatGPT for writing. No AI image generators for art projects. No “looking up” ideas or solutions online for creative work.

Think of this like giving their creative muscles a chance to stretch and remember what they can do on their own. During this period, your child will likely experience some frustration. This is normal and actually beneficial—it's their brain remembering how to work through challenges independently.

Step Two is building a Support System. Your job isn't to eliminate their frustration but to support them through it. Instead of saying “Let me help you,” try “This seems challenging. What are you thinking?” Instead of “Maybe look it up,” try “What would happen if you just tried something?”

The most important thing you can do is manage your own discomfort with their struggle. When you see your child working through a creative challenge, every parenting instinct tells you to help. But supporting them through creative problem-solving builds the brain resilience they need.

Step Three is celebrating effort over outcome. When your child creates something during this pause period, focus your praise on their thinking and persistence, not the result. Say “I love how you kept trying different approaches” instead of “That's beautiful.”

After the 24-hour pause, establish ongoing creative strength training. Think of these as daily workouts for your child's creative brain:

For younger kids: Try “Daily Wonder Questions”—five minutes of “what if” questions with no right answers. “What if gravity worked backwards on Tuesdays?”

For middle schoolers: Use the “Daily Assumption Challenge”—question one obvious assumption each day. “Why do schools have to happen in buildings?”

For teenagers: Try the “Creative Perspective Shift”—describe any current event from three completely different viewpoints.

For more detailed creative exercises tailored to different ages and situations, check out our Episode 3, “Creative Thinking Exercises: 10-Minute Daily Brain Workout to Boost Innovation.”

Your role as a parent is crucial. You're not trying to become your child's creative manager. Instead, you're becoming what researchers call a “creative support system.”

Model working through challenges yourself. Let your children see you tackling problems, trying new approaches, and persisting when things don't work immediately. Create a family culture that values thinking over knowing. Celebrate questions as much as answers.

The strategies I've shared will help immediately, but developing strong creative independence requires a systematic approach. I recommend a two-phase development plan:

Phase One, weeks 1 to 2, is Assess and Adjust. Begin with the creative independence assessment while gently reducing AI dependence. This is when you implement the 24-hour pause and begin daily creative exercises.

Phase Two, weeks 3 to 6, is Develop and Strengthen. Increase daily creative exercises and build persistence with challenging tasks. This is when you'll start seeing real improvements in your child's creative confidence and independence.

There's also a Phase Three—learning strategic AI use that enhances rather than replaces creativity. However, this assumes you've first mastered the strategic use of AI yourself. If you want to learn these skills, watch our episode “The AI Creativity Multiplier: 5 Steps to Amplify Your Innovative Thinking.“

Let me be realistic about expectations. During the first week or two, you'll likely see some resistance. Your child might complain that creative tasks feel more challenging without AI assistance.

This is normal and temporary. You're asking them to use creative abilities that have been getting less exercise. Signs of improvement include increased creative attempts, less anxiety about “not knowing” the right answer, and willingness to experiment with ideas.

You should see positive changes within two to three weeks of consistent practice. For most families, this systematic approach will not only restore your child's natural creative abilities—it will make them stronger creative thinkers than they were before.

You're at a choice point right now. You can assume this will work itself out as children “figure out” the right balance with AI. Or you can take action today to protect and strengthen your child's creative brain.

Here's the reality: this challenge will only grow as AI tools become more sophisticated and appealing to children. The longer you wait, the deeper these patterns become.

But the encouraging news is that you have everything you need to start making a difference today.

Your specific next step: Download the complete Family Creative Assessment from our website. This includes detailed instructions for the Challenges, plus additional age-appropriate creative challenges, a parent observation checklist, progress tracking sheets, and the complete 24-hour creative pause protocol.

Download The Family Creativity Assessment and Tools

Try one of the assessment challenges with your child this week—start with whichever one matches their age. Then post a comment, and share one specific thing you discovered about your child's creative independence.

Based on your comments on this episode, I'll know whether to create more detailed, age-specific guides for families who want to build their child's creative muscle.

Remember, the children who develop creative resilience today will be the innovators, problem-solvers, and leaders of tomorrow. They'll be the humans who can work with AI without losing what makes them uniquely, authentically human.

Your child's creative brain is waiting for your guidance. The question is: Are you ready to take that first step?

Until next time, I'm Phil McKinney, and remember—in an age of artificial intelligence, your child's creative thinking has never been more important.

Download the Complete Family Creative Assessment: https://open.substack.com/pub/philmckinney/p/download-your-guide-for-protecting

To learn more about AI dependency, listen to this week's show: Human-AI Creative Partnership: How AI Dependency Is Rewiring Your Child's Creative Brain (And What Parents Can Do About It).

The most innovative creators don't use AI as a replacement – they use it as a strategic partner in a carefully choreographed dance of human and machine intelligence.

Welcome to Part 4 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement.

In Part 1, we explored the alarming decline in creative thinking as we've grown dependent on AI. In Part 2, we discovered how neuroplasticity allows us to rebuild and enhance our creative capabilities. And in Part 3, I gave you a practical 10-minute daily workout to strengthen the neural pathways essential for innovative thinking.

Today, we're bringing it all together with something immediately actionable: a framework for creating productive partnerships with AI that enhance rather than diminish your creative capabilities.

This isn't about rejecting AI – it's about using it strategically to amplify your uniquely human abilities. When used properly, AI can handle routine cognitive tasks while freeing your mind for the breakthrough thinking that algorithms simply cannot replicate.

Let me start by clarifying the fundamental difference between human and machine intelligence that drives this partnership:

Convergent thinking is the process of analyzing existing data to find optimal solutions within defined parameters. This is what AI excels at – processing vast amounts of information to identify patterns and generate options based on probability distributions of what has worked before.

Divergent thinking is the ability to generate novel ideas by making unexpected connections, breaking conventional patterns, and imagining what doesn't yet exist. This is where humans uniquely excel – our capacity for intuitive leaps, metaphorical thinking, and insight that transcends existing data.

The most powerful creative partnerships leverage both: AI's computational strength and the human capacity for originality. Let me demonstrate with a simple example.

If I asked an AI to design a chair, it would analyze thousands of existing chair designs and generate variations based on established patterns. The results would be functional but predictable.

But what if I first engaged in divergent thinking by questioning the very concept of sitting? What if I reimagined a chair as something that supports the body in motion rather than at rest? This human insight – this conceptual leap – changes everything about how we might approach the design.

Now when I engage AI, I'm not asking it to “design a chair” but to help explore a completely new approach to supporting the human body. The AI becomes a tool for expanding and refining my original insight rather than a replacement for it.

This is the heart of creative partnership: human divergent thinking provides the spark of originality, while AI convergent thinking helps develop and refine that spark into something practical.

Before we dive into our five-step framework, let's talk about what makes an effective AI prompt for creative work. The way you communicate with AI dramatically impacts the quality and originality of what you receive in return.

Throughout this episode, I've included actual prompts formatted in code blocks that you can copy, edit, and paste directly into your favorite AI tool – whether that's ChatGPT, Claude, or others. These aren't theoretical; they're battle-tested approaches I've used with innovation teams.

The most powerful creative prompts share three key characteristics:

When using the example prompts throughout this episode, customize them to your specific challenge, but maintain these structural elements that encourage exploration rather than premature convergence. The goal is to shape AI responses that serve as thought-provoking material for your own creative thinking, not as final answers.

Here's a quick formula for effective prompts:

Now, let's explore our five-step framework for forming creative partnerships with AI that enhance rather than diminish your creative capabilities.

The most common mistake I see is turning to AI too early in the creative process. This typically happens because facing a blank page is uncomfortable – we're seeking the path of least resistance.

But this short-circuits your brain's ability to make original connections. Instead, I recommend priming your brain before engaging any AI tools.

Here's how:

This priming step activates your associative thinking networks – the neural pathways that connect seemingly unrelated concepts. When you later engage AI, you'll do so with your creative faculties already warmed up and ready to evaluate AI outputs critically.

How you engage with AI fundamentally shapes what you get from it. The key is to position AI as a thought partner exploring a problem space rather than a solution generator.

Instead of asking: “Generate ideas for a new water bottle design”

Try: “What are the unsolved problems in how people stay hydrated throughout the day?”

The first prompt tells AI to generate variations on a water bottle – convergent thinking within established parameters. The second prompt opens a problem space that invites exploration of the underlying challenge.

Similarly, rather than asking AI to “write a marketing campaign,” ask it to “identify emotional tensions between consumers and existing products in this category.”

This framing preserves your role in the most crucial part of creativity – defining the right problem. It positions AI as an explorer rather than a solver, helping you see facets of the challenge you might otherwise miss.

Example Problem-Framing Prompts:

Example 1:

Example 2:

While AI excels at convergent thinking, we can strategically use it to accelerate certain aspects of divergent thinking as well. The key is to use AI to generate raw material that you then transform through your human creativity.

Here's the technique:

For example, if you're designing a new learning app, you might ask AI: “How do master chefs structure the process of teaching complex skills?” or “What principles do video game designers use to maintain engagement during difficult challenges?”

The AI responses become raw material for your own divergent thinking process. You aren't adopting the AI's suggestions directly – you're using them to trigger new neural connections in your own thinking.

This approach leverages AI's knowledge breadth while preserving your uniquely human ability to make unexpected connections across domains.

Example Divergence Acceleration Prompts:

Example 1:

Example 2:

Once you've generated truly original directions through divergent thinking, AI becomes extraordinarily valuable for convergent activities – developing, refining, and optimizing your creative insights.

This is where many people go wrong – they either overuse AI (surrendering the creative process entirely) or underuse it (ignoring its analytical strengths).

Here are specific convergent tasks ideally suited for AI delegation:

The key principle: Use AI for expansion and refinement of ideas that originated from your divergent thinking, not as the source of the original insight itself.

For example, if you've conceptualized a novel approach to remote team collaboration, you might ask AI to:

This leverages AI's analytical power while preserving your role in the creative breakthrough.

Example Convergence Delegation Prompts:

Example 1:

Example 2:

The final step is perhaps the most important: consciously maintaining your creative authority throughout the process.

AI tools are designed to be persuasive – they present information confidently and comprehensively. This creates what psychologists call the “authority bias” – our tendency to accept information from perceived authorities without sufficient scrutiny.

To maintain creative authority:

Remember, the goal isn't to reject AI's contributions but to engage with them critically and creatively. Your unique human perspective – your lived experience, intuition, and values – should always remain the guiding force.

Example Creative Authority Prompts:

Example 1:

Example 2:

Download Your Guide/Prompts for Turning AI Into a Creativity Multiplier

Let me share how this framework transformed the product development process at a consumer electronics company I worked with recently.

Their team had been using AI tools extensively, but primarily as idea generators – essentially asking the AI to design new products directly. The results were predictably mediocre – variations on existing products with marginal improvements.

We implemented the five-step framework, beginning with creative priming exercises before any AI engagement. Then, instead of asking the AI to generate product concepts, we asked it to explore unresolved tensions in how people interact with technology in their homes.

This exploration revealed something fascinating – people were increasingly concerned about technology fragmenting family attention rather than enhancing connection. This human-centered insight came not from the AI directly, but from the team's analysis of the problem space with AI assistance.

This led to a breakthrough concept: a family gaming system designed specifically for collaborative rather than competitive or individual play, with features that actively encouraged rich social interaction rather than isolated immersion.

Once this novel direction was established through human divergent thinking, the team then used AI extensively for convergent tasks – researching existing collaborative technologies, identifying potential technical challenges, and developing implementation variations.

The result was a genuinely innovative product that addressed deeply human needs in ways that AI alone could never have conceptualized. The product has since become one of their most successful launches, precisely because it originated from human insight about social connection rather than algorithmic prediction.

We've now completed our five-step framework for creative partnerships with AI: prime your brain first, frame challenges not solutions, use AI for divergence acceleration, delegate convergence, and maintain creative authority.

Each step is designed to leverage both human and machine intelligence in their respective domains of strength – your divergent thinking and AI's convergent capabilities.

This approach represents a middle path between two extremes. On one side is complete AI dependency – surrendering our creative faculties to algorithms and experiencing the cognitive atrophy we discussed in earlier episodes. On the other side is AI rejection – ignoring powerful tools that could genuinely enhance our creative capabilities when used properly.

The creative partnership I've outlined offers something better: a complementary relationship that amplifies your uniquely human creativity while leveraging AI's computational power.

Remember the key principles we've explored throughout this series:

As we move deeper into the AI age, the ability to form these productive partnerships will increasingly distinguish those who merely execute from those who truly innovate. By understanding the complementary relationship between human and machine intelligence, you can develop creativity that no algorithm can replicate.

Download the FREE guide, with the AI prompts, that will help you use AI as your creativity multiplier.

Download Your Guide for Turning AI Into a Creativity Multiplier

Join me next time for “Your Child's Creative Brain on AI” We'll explore how to assess your creative development and build systems that continuously enhance your innovative thinking. Your child's creative brain is waiting for your guidance. The question is: Are you ready to take that first step?

Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, the most valuable thinking happens at the intersection of human insight and computational power. That intersection exists in only one place: your creatively engaged mind.

Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a paid subscriber on Substack.

Your support helps make this content possible.

To learn more about harnessing AI, listen to this week's show: Human-AI Creative Partnership: How to Harness AI While Preserving Your Innovative Edge.