Your AI Strategy Is Either Unreplicable — or Irrelevant
As AI becomes the requirement to thrive across every industry, the real question isn’t whether your company is using it; rather, it’s how they’re using it. The companies pulling ahead aren’t the ones cutting costs the fastest. They’re the ones that recognized that their everyday operations were quietly generating something that their competitors can never replicate.
Hear why “Your AI Strategy Is Either Unreplicable or Irrelevant” Deep Dive
By implementing AI, your company is either distancing itself from your competitors or just staying in place. Which one is your AI strategy actually doing?
According to McKinsey’s 2025 State of AI report, 88% of companies are now regularly using AI in at least one business function. But here’s the problem: nearly two-thirds of those companies have not yet begun scaling AI across the enterprise. The rest are still running pilot programs or in the early stages of figuring out where AI can actually make a difference.
Which brings me back to my earlier question: Is your AI being used as a true competitive advantage by creating a new product, offering, or solution that none of your competitors can replicate? Or is it the same tool, applied the same way, that’s already been commoditized across your industry?
The Ford and Sloan Lesson Nobody Talks About Enough
That question has a historical parallel worth understanding. In the early 1920s, Henry Ford, the founder and leader of the Ford Motor Company, believed that being first to market with the lowest price made customer preference irrelevant. Why give people options—like the color of the car—when you already had the best product at the best price? Alfred Sloan, who was running General Motors at the time, saw it differently. Sloan recognized what customers wanted, as well as what they aspired to. So, he built a ladder of brands at various price points that would keep customers loyal to GM for life. As their financial situations improved, these customers moved up from Chevrolet to Pontiac to Oldsmobile to Buick to Cadillac, but they never left GM.
Sloan recognized that the competitive advantage was inherent in customer behavior, not sameness. For companies to succeed with AI today, they need to look inward at what makes them unique in the first place. That comes down to using insights to solve a real problem. Speed and scale with AI have become features that everyone is offering. The companies pulling ahead are the ones using AI to solve problems specific to their customers, their industry and their operations—and in doing so, discovering opportunities their competitors can’t see.
So, ask yourself, if all of your competitors are using the same AI tools as you and achieving the same results, what advantage have you actually gained?
In The Great AI Contraction, Rob May calls this the Consumer Surplus Trap. If every company uses AI to cut costs by 30%, competition forces prices down by 30%. You haven’t created margin; you’ve created consumer surplus. The savings don’t flow to your bottom line; they flow to your customer. Ford built the most efficient car company in the world. Sloan built the one people actually wanted to buy from. Efficiency was never the point; rather, having several options to choose from was.
Your Biggest AI Asset Is Already Hiding in Plain Sight
What if the most defensible AI advantage doesn’t come from a new initiative, but from something you’re already doing every day? You’re not reinventing your business; you’re just looking at it from a different angle. What customer problem are you uniquely positioned to solve? What everyday activities across your business are quietly generating data that nobody else has access to—data that’s been right in front of you all along?
Here’s what that actually looks like in practice:
Halter: Halter uses an AI cattle collar to collect behavioral and location data on cattle that nobody else has. Their technology creates a virtual fence that allows farmers to monitor their animals’ location and health indicators through an app. The farmer can now herd the cows using vibrations and audio cues from a solar-powered collar. And the business model is just as smart as the technology: Halter’s proprietary Cowgorithm system generates a recurring revenue stream built entirely on data that no competitor can replicate.
Pokémon Go: To train a fleet of delivery robots to navigate sidewalks and street corners, a software partner is repurposing over 30 billion crowdsourced images captured through the Pokémon Go app, and using them to improve how delivery bots manage busy streets by filling in the gaps GPS can’t identify.
DoorDash: DoorDash launched a standalone app that uses its delivery workforce as a proprietary data supply chain by tasking them with recording audio and video with which to train humanoid robotics and AI models. This is data that bridges the gap in physical AI training that can’t be replicated in a lab simulation. The everyday act of making a delivery is now building something far more valuable than the delivery itself.
UPS ORION: After nearly two decades of quietly turning their own route, driver and package data into a proprietary asset that keeps getting smarter with every delivery, no competitor can replicate what UPS’s data flywheel has already built.
“So, ask yourself, if all of your competitors are using the same AI tools as you and achieving the same results, what advantage have you actually gained?”
This Isn’t a New Idea; It’s the Next Chapter
Just over two years ago, I wrote about how companies were using digital twins—i.e., AI-driven simulations of their real-world environments—to train, predict and protect their businesses. McKinsey projected that by 2026, investments in digital twins would exceed $48 billion. BMW was already using the technology in its smart manufacturing plants to simulate production processes in real time, identifying bottlenecks and optimizing operations before they became problems.
What Halter, UPS and DoorDash are doing is the natural evolution of that strategy—and it’s a significant one. While digital twins showed companies how to simulate their world, these companies went a step further. They used AI to expose unrealized opportunities hiding inside their everyday operations, and in doing so, created a wedge between themselves and their competition.
This wasn’t a white-label solution bought off the shelf; rather, it was built on their own IP, their own data and their own understanding of a problem only they were positioned to solve. That’s what makes it unreplicable.
The Questions Your Business Should Be Asking
What would make you indispensable to your customer, or better yet, enable you to drive recurring revenue with them? What’s the one thing that, with the right data, would allow you to leapfrog your competition?
The answer doesn’t lie in building something new; rather, it lies in using AI to look at an existing problem differently.
Halter built their business on a principle most of us already know—the invisible fence, which is a product designed to keep your dog from wandering off your property—paired with a monthly GPS service fee. Halter reimagined how that same solution could work for the cattle industry, but with far richer insights into each animal’s location and health. At $5 to $8 per animal monthly, and with the average cattle herd consisting of 43 to 47 cows, that’s $215 a month, or $2,580 per year per rancher. Built on proprietary data. Built on a problem only they were positioned to solve.
That’s the real lesson here. The most unreplicable advantages aren’t invented from scratch; rather, they’re borrowed from an adjacent idea and applied to a problem nobody else in your industry thought to solve.
Try looking across or outside your industry and ask how that idea can be tailored to what you do. What’s your version of a delivery route, a supply chain, or a customer journey that doubles as a data asset?
Unreplicable or Irrelevant: There’s No Middle Ground
No one can dispute the efficiency and operational gains AI provides. But companies using AI purely to cut costs risk falling into the Consumer Surplus Trap, where you risk becoming irrelevant and your customers benefit instead of you.
The real competitive advantage comes from asking better questions—e.g., using AI as researcher, interpreter, thought partner, simulator and communicator to translate your own proprietary intelligence into a competitive asset nobody else has. That’s what the Pokémon Go partnership did with billions of crowdsourced images. That’s what UPS ORION did with two decades of driver and delivery data. And that’s what Alfred Sloan did a century ago when he recognized the void Ford created by treating price as a competitive differentiator.
AI is exceptional at facilitating insights, deconstructing scenarios across multiple levels of exploration, and asking deductive questions at a speed and scale no human team can match. What it can’t do is think outside the box, look across industries, or ask “what if we did it this way instead?” That’s still your responsibility.
So, which approach will you take? Using AI to uncover the one thing your competitors can’t replicate? Or risking commoditizing what you thought was a game-changer—only to find several competitors offering something just like it?
Most individuals and companies I speak with aren’t struggling with AI adoption; rather, they’re struggling with AI direction.
If you’re not sure whether your current strategy is building something unreplicable or just keeping you in place, that’s worth a conversation. I’d love to learn more about your thoughts on this topic. Email me here.
