Here is something worth sitting with: The companies that lose during major technological shifts rarely fail because they ignore the technology.
They fail because they misjudge it.
That distinction matters more than most business owners realize — especially right now.
Borders, Kodak, and Blockbuster were not run by incompetent leaders. They were established, profitable, and operationally sophisticated companies. Their leaders were smart, experienced, and deeply familiar with their industries. And yet each of them made a version of the same mistake: they looked at a structural shift in their market and concluded their existing model was too strong to be threatened.
If that sounds familiar, it should. Because right now, a similar conversation is happening in boardrooms and strategy sessions across every industry. And the technology at the center of it is AI.
The Pattern No One Wants to Recognize
Before we examine the case studies, it is worth naming the pattern that connects them — because disruption does not arrive as an obvious threat. It arrives as a niche curiosity.
The pattern looks like this:
A new technology emerges. Incumbents dismiss it as immature, overhyped, or irrelevant to their core customer. Early adopters experiment and iterate. Customer behavior begins to shift — subtly at first, then rapidly. The industry structure transforms. By the time incumbents react, the competitive advantage has already moved.
The dangerous moment in this sequence is not the final collapse. It is the middle — when the technology is real but not yet dominant, and leaders are forced to make a structural bet with incomplete information.
That is exactly where we are with AI today.
“The innovator’s dilemma is not that companies don’t see change coming. It’s that they are rationally optimizing for today while the future is being built around them.”
— Clayton Christensen
The Borders Lesson: Don’t Give Away the Learning Curve
In the early 2000s, Borders made a decision that seemed entirely rational at the time. Rather than building and managing its own e-commerce platform, it outsourced online book sales to Amazon.
The logic was sound on the surface. Amazon was good at logistics. Borders was good at retail experience. Why not let each company do what it does best?
What Borders gave away was not just revenue. It gave away the learning curve.
Every customer transaction Amazon handled on Borders’ behalf was a data point Amazon used to understand buying behavior, inventory, personalization, and fulfillment. Amazon was building structural capability while Borders was maintaining physical infrastructure.
By the time Borders recognized the platform shift, Amazon had become the platform.
The AI Parallel: Many businesses today are using AI tools built and controlled by large platforms — without building any internal capability of their own. There is nothing inherently wrong with using third-party AI tools. But if we are not learning from that use, not systematizing the insights, not building internal knowledge around how AI serves our specific business and customers — we are making a version of the Borders mistake. We are letting someone else own the learning curve.
This connects directly to one of the most persistent structural problems we see in established businesses: expertise that lives in people’s heads rather than in systems. If that resonates, read The Hidden Cost of Being the Bottleneck — it is the same dynamic playing out at the operational level.
The Kodak Lesson: Protecting Yesterday’s Revenue Is Not a Strategy
This one is the most painful, because Kodak did not miss the technology. Kodak invented digital photography — in 1975. A Kodak engineer named Steve Sasson built the first digital camera as an internal project. When he presented it to executives, the response was telling: management called it “a cute toy” and told Sasson not to talk about it publicly.
The response from Kodak leadership was not dismissal. It was something more dangerous: concern.
Digital photography, if successful, would cannibalize the film business. And film was enormously profitable. So Kodak made a calculated decision to delay aggressive investment in digital while protecting the margins of its existing model.
The strategic logic was defensible. The outcome was catastrophic.
When digital adoption accelerated — driven by mobile phones, not just cameras — Kodak’s deliberate hesitation had become a structural deficit. The company that invented the technology filed for Chapter 11 bankruptcy protection in January 2012.
The AI Parallel: Some business owners are aware of what AI can do but are deliberately slow to move because it may disrupt their current operational model — how their team works, how their services are delivered, how their expertise is packaged. That is a reasonable short-term concern. It is also the exact logic Kodak used. Protecting current revenue is not the same as building future capability. These are not the same decision, and treating them as equivalent is a structural mistake.
For a deeper look at how to evaluate your business model before AI forces the issue, see Evaluate and Refine Your Business Model: Stay Sharp, Competitive, and Future-Ready in an AI-Accelerated World.
The Blockbuster Lesson: Never Underestimate What Customers Will Trade for Convenience
Blockbuster had multiple chances to acquire Netflix. In September 2000, Netflix co-founders Reed Hastings and Marc Randolph flew to Blockbuster’s Dallas headquarters with an offer to sell the company for $50 million. Blockbuster CEO John Antioco reportedly laughed them out of the room, calling Netflix a niche business.
The reason is instructive. Blockbuster leadership evaluated Netflix against their existing model — late fees, physical stores, weekend traffic — and concluded Netflix was too niche, too limited, and not competitive with the in-store experience.
What they failed to model was not just streaming technology. It was how dramatically customer behavior would shift when convenience became available at scale.
Streaming did not just change how movies were distributed. It changed how customers thought about access, control, and friction. Blockbuster’s entire operational model — built around a physical destination experience — became a liability almost overnight.
“Companies fail not because they do something wrong but because they keep doing what used to be right for too long.”
— Don Sull
The AI Parallel: AI is changing customer expectations in ways that are not yet fully visible — but the direction is clear. Customers are beginning to expect faster responses, more personalized service, and lower friction across every interaction. The businesses that build AI-enabled capability into their operations now will set a new standard. Those that maintain the current service model without adaptation may find themselves in Blockbuster’s position: not outcompeted on quality, but outpaced on expectation.
If your business is still dependent on manual processes and tribal knowledge to deliver your customer experience, The Hard Truth About Growth (And Why AI Is Calling It Out) is worth your next 10 minutes.
The Real Question Every Business Owner Needs to Ask
Most conversations about AI in business circles focus on the wrong question.
The question most often asked: “Should we use AI tools?”
That question, while practical, is structurally insufficient. Of course we should be experimenting with AI tools. That is table stakes.
The question that actually matters is this: “How might AI change the structure of our industry?”
Not the tools we use. The structure. The competitive dynamics. The customer expectations. The knowledge leverage. The cost model. The delivery model.
According to McKinsey’s Technology Trends Outlook, AI is a widely applicable, general-purpose technology with use cases in every industry and business function — and it is scaling rapidly across the business landscape. General-purpose technologies do not just improve how work gets done. They reorganize how industries are structured.
The businesses that recognize this early and begin building internal capability — in systems, in process design, in team knowledge — will build a structural advantage that compounds over time.
Building that internal capability starts with systems. If your business doesn’t have documented processes AI can actually amplify, start here: The Hidden Secret to Scaling Smoothly? SOPs the Team Will Actually Use.
AI Strategic Self-Assessment
Here are four questions worth asking with your leadership team:
- Where in our business could AI fundamentally change how work gets done? Not just speed it up — but change the nature of the work, the role of people, or the output quality.
- What internal knowledge or processes should we begin systematizing now? AI amplifies what is already structured. If our expertise lives in people’s heads rather than in documented systems, we are not AI-ready. (See also: 10 Essential Analytical Skills Every Business Owner Needs to Make Smarter Decisions (Especially with AI))
- How might AI change customer expectations in our industry? Where is friction today that customers currently tolerate — and how quickly could that tolerance disappear?
- Who in our organization is responsible for understanding the strategic implications of AI? If the answer is “no one specifically,” that is a leadership gap worth closing. (Related: AI Is Not Your CEO: How to Use AI Without Losing Judgment)
The Structural Bet
Borders, Kodak, and Blockbuster each made a bet — not that the technology was irrelevant, but that their existing model was resilient enough to absorb the change at their own pace.
They were wrong. Not because the technology moved faster than predicted, but because customer behavior moved faster than their models anticipated.
The businesses that navigate major technological shifts successfully do not necessarily move first. But they move intentionally. They study the structural implications. They build internal capability. They do not confuse tool adoption with strategic readiness.
McKinsey’s research shows that 65% of organizations are now regularly using generative AI — nearly double the percentage from just ten months prior. The window to build a deliberate, structural advantage before AI becomes baseline expectation is open — but it will not stay open indefinitely.
AI is not going to wait for our operational calendar to clear.
The question is not whether to engage with it. The question is whether we are engaging with it strategically — or just tactically.
The leaders who ask the deeper question now will not have to answer the harder question later.
Want to know if your business has the structural foundation to leverage AI — or if the gaps will hold you back? Start with the Structural Independence Assessment™ — a 10-minute diagnostic that shows exactly where your business still depends on you.
Ready to think through the structural implications of AI for your specific business?
Book a Strategy Call and let’s map where AI creates risk and opportunity in your model — before your competitors do.
Or if you’d prefer to start with a self-guided deep dive, the RAD Business Success Method™ gives you the full framework for building a business structured for this era.
