Kroger Copied HP's Innovation Playbook Perfectly. It Failed Anyway.

The invisible reasoning error that cost 18 months—and why you're probably making it right now

John Osborne told me to meet him at the Starbucks inside the Kroger store in Northern Kentucky.

I ordered my hot chocolate and waited. John arrived, we grabbed our drinks, and then he did something unexpected: we walked out of the Kroger and headed down the strip mall. Past a few storefronts. Nothing remarkable—just typical suburban retail.

Then John stopped at what looked like any other retail space you’d find in a strip mall. He pulled out his badge and swiped it at the door.

Inside, behind whited-out windows, was the lab. Workspace for the Kroger Innovation team. Hidden in plain sight, a few doors down from a real Kroger store that served as their test bed.

I hadn’t known they had a store dedicated to innovation. Or that they’d set up shop in an anonymous storefront where shoppers walked past every day without a second thought.

A 142-year-old company, founded in 1883 in Cincinnati, had made a serious bet on innovation. They’d given John and his team resources, autonomy, an entire store, and this hidden lab to prove what was possible.

And eighteen months in, something wasn’t working.

I’d been invited to help. Not just because I’d warned them this would happen, but because this was personal. I’m from Cincinnati. My aunt and uncle both worked at Kroger for over 35 years each, retiring from their roles. This wasn’t just about innovation—it was about family.

John, who led the innovation team under Brett Bonner (Kroger’s VP of Research and Development), walked me through what they were facing. The methodology was sound. The resources were there. Management believed in what they were trying to do. But the organization kept pushing back on what they were building.

Eighteen Months Earlier: The Email

The email had arrived with that tone of professional courtesy mixed with barely concealed excitement. A Kroger executive had been listening to my podcast, and they’d done their homework—HP had been named one of Fast Company’s “50 Most Innovative” three years running, with case studies from Stanford and Harvard. And they knew I’d shared the methodology under Creative Commons.

The email wasn’t really asking permission. It read more like: “We’re going to use what you did at HP. Since it’s Creative Commons, we’re good, right? Any implementation tips?”

Their logic felt iron-clad. HP achieved sustained innovation success using this methodology. The methodology was documented and teachable. Therefore, implement the methodology and get the results.

I sent back one line: “It won’t work as is.”

The Warning They Couldn’t Hear

I wasn’t being protective. I was warning them about a mistake I’d already made.

The innovation methodology didn’t come from HP. I’d built it over my career—a personal framework tested through years of iteration across different contexts. Teligent was one of the first places I tried to make it organizational, taking something personal and turning it into something a whole company could use. Teligent was a startup that went public, with all the risk tolerance that comes with survival mode, but a weak culture that didn’t embrace innovation efforts.

Then I came to HP. Same framework, but I had to completely retranslate it. HP’s culture—built by Bill Hewlett and David Packard—had some immunity to corporate antibodies, which meant I could push harder and take more extreme positions. But HP was deeply anti-risk in ways Teligent never was.

What worked at Teligent wouldn’t work at HP without changing the language, the processes, the approach to risk, and the way we talked about customers. Same framework, different adaptation.

But I understood why Kroger didn’t hear my warning. Their reasoning felt bulletproof. IF this methodology produces innovation success at HP, AND we implement this methodology, THEN we will achieve innovation success. It’s the kind of logic that gets funding, executive buy-in, and careers attached to it.

The error was invisible: they’d taken what they observed—”HP succeeded while using this approach”—and treated it like a universal law—”This approach causes success.” Pattern became principle. Correlation became causation.

That confusion was about to cost them 18 months.

When Process Meets Reality

They implemented what I’d built at HP. The way we made funding decisions. Our innovation pipeline review process. The governance structures. The stage gates. The decision architectures. It was HP’s framework, adapted over the years to HP’s culture, HP’s risk tolerance, and HP’s organizational structure, being applied directly to Kroger’s 142-year-old retail operation.

And that’s where the friction started.

At Kroger, for any innovation to succeed, it eventually needs to be deployed. And deployment is controlled by the store teams—the people running actual stores with actual customers and actual P&Ls. Those teams operate on a completely different set of metrics, metrics unrelated to testing and experimentation. The margin in grocery retail is razor-thin. Efficiency and cost control aren’t optional; they’re survival. Innovation injects chaos into a system built for consistency.

The resistance John’s team heard sounded rational enough:

“Our customer likes it this way.”
“You’ll never get approval.”
“It won’t fit our operation.”
“Who is going to do it?”

But underneath every objection was the same message: These processes don’t fit how we actually work. And they threaten the metrics we’re measured on.

The innovation team became isolated. The lab behind the whited-out windows. The group with the fancy test store and processes that didn’t sound like Kroger, using frameworks that didn’t map to how store teams actually operated. With every meeting where the language didn’t land, every presentation met with skeptical questions, the team’s internal capital drained away.

The cost wasn’t just 18 months of calendar time. It was credibility. Reputation. The slow transformation into “that team with the thing that isn’t working.”

They copied what worked at HP without understanding why it worked. That doesn’t just cost time—it costs the people championing it.

But John and his team didn’t give up. They got humble.

The Comeback: Show, Don’t Tell

After 18 months of struggle, John’s team did something different. They stopped presenting and started showing. They walked management and store teams through the test store. Let them see it. Experience it. Understand what was possible when you had a different set of metrics—ones designed for experimentation, not just efficiency.

“Show, don’t tell” became their innovation story.

The test store addressed the anti-risk antibody response directly. Here’s what we tested. Here’s the measurable impact. Here’s why it works. Now let’s translate it so it works for your store, with your metrics, in your operation. They stopped trying to import HP’s framework and started translating it—innovation terminology into Kroger’s existing vocabulary, customer discovery into merchant and category management language, new processes built into Kroger’s existing project management instead of replacing it, HP’s operational frameworks adapted to Kroger’s 142-year-old retail culture.

The methodology didn’t change. The translation did.

What the Rough Prototype Became

On my first visit to that hidden lab, John showed me something they were calling a “scanning tunnel.” It was early, rough, and unpolished. But the concept was clear: what if checkout didn’t require perfect product placement? What if you could just toss your groceries in and let the technology handle the rest?

When I saw the deployed version later, it looked like exactly what it sounds like: a tunnel. You toss your groceries on one end—it doesn’t matter if items are upside down, piled on each other, or produce that needs to be weighed. Everything zips through with multiple cameras and scanners reading barcodes and package identification from all angles, and out comes your total.

Fast. Efficient. And critically, it addressed the speed issue the entire industry was facing with the growing footprint of self-service checkout.

The “Advantage Checkout” scanning tunnel received recognition as a top innovation at the National Retail Federation’s annual “Big Show” in 2011. Kroger had achieved their equivalent of HP being named “Most Innovative.”

They also embedded what they called the “Killer Question Mentality”—a way of framing innovation challenges that fit Kroger’s culture of operational excellence and customer focus. The team went from organizational outsiders to creating technology that could transform their industry.

The Pipeline That Wouldn’t Stop

I stayed in contact with John over the years. Even after his retirement from Kroger, we’d stay connected. On follow-up trips to the test store, John showed me what the adapted innovation framework had unlocked.

IoT temperature sensors for refrigeration and freezer areas—improving product quality, reducing spoilage, and improving safety. Kroger became one of the most “wired” retailers in the world, with different wireless technologies like Zigbee allowing for rapid prototyping and testing. Not just one innovation. A non-stop pipeline of ideas.

The framework was always sound. But it only worked once they stopped treating it like a universal law and started treating it like a pattern that required translation. In their own words, they “customized beyond the obvious”—adapting to Kroger’s unique culture, customer base, and operational realities.

The 18 months weren’t wasted. They were the tuition for a deeper lesson: success isn’t copying what works elsewhere. It’s understanding why it works, then rebuilding it for your context.

Why Smart People Keep Making This Error

Here’s what makes Kroger’s story worth understanding: they weren’t naive. They gave John’s team everything—resources, a real store, management support, belief, and a hidden lab to work without distraction. And they still stumbled for 18 months.

Not because they were stupid. The reasoning error they made is completely invisible until it costs you.

Watch how it works. We observe a correlation: “This successful company does X.” We treat it as causation: “X causes success.” Then we apply it deductively: “If I do X, I will succeed.” We skip over context, culture, the organizational immune system, and all the translation work required to move from pattern observation to actual implementation.

This is inductive reasoning—building general principles from specific observations—dressed up as deductive certainty. And once you see it, you’ll notice it everywhere.

I’ve watched this pattern repeat. Not just at Kroger or the US Department of Education, but in startups copying Google’s flat hierarchy without Google’s resources or maturity, leaders adopting Bezos’s principles without Amazon’s context, individuals copying morning routines and wondering why nothing changes.

After seeing it enough times, I wrote an entire chapter about it in my book Beyond the Obvious. Chapter 10: “Adopt and Adapt.” Not “Adopt.” Not “Adapt.” Both. The key line I use with organizations now:

“Don’t try to shoehorn your needs into this approach. Adopt what works, and adapt what doesn’t.”

Because inductive reasoning—building principles from observations—doesn’t give you certainty. It gives you probabilities. And probabilities require adaptation.

The Difference That Changes Everything

John Osborne’s team wasn’t making a dumb mistake. Neither are you when you see success somewhere else and want to copy exactly what they did.

You’re just confusing two fundamental types of logical reasoning—and that confusion stays invisible until it costs you 18 months and your credibility.

Deductive reasoning starts with universal principles and applies them to specific cases: “All humans are mortal. Socrates is human. Therefore, Socrates is mortal.”

Inductive reasoning works the opposite way, observing specific cases and building toward general principles: “I’ve seen 100 swans. All were white. Therefore, all swans are white.”

We constantly mix them up. We take inductive observations—”HP succeeded with this approach”—and treat them like deductive principles—”This approach causes success.” We build strategies on correlation and execute with the certainty of causation. We mistake “I’ve seen this pattern” for “I understand the mechanism.”

Until you can see the difference—until you can recognize which type of reasoning a situation actually demands—you’ll keep making expensive mistakes with absolute confidence.

That’s what Episode 2 of THINKING SKILLS 101 breaks down: how to recognize in real-time when you’re pattern-matching versus applying universal laws. When correlation requires adaptation versus when principles actually transfer directly. When your “logical” conclusion is actually just probability dressed up as certainty.

Watch Episode 2: Logical Reasoning - Deduction vs. Induction → Released Weds

Because the next time you see success and want to copy it—the next time you observe what works somewhere else—you’ll know the real question isn’t “Should I do what they did?”

The real question is: “Am I confusing observation with causation—and what’s that confusion about to cost me?”

John Osborne and his team discovered the truth. It cost them 18 months, but they learned, adapted, and built breakthrough innovations. A scanning tunnel that won industry recognition. A pipeline of IoT innovations. One of the most wired retailers in the world.

All because they learned to see the difference between observation and causation.

How much will it cost you before you learn to see it?