Avoid the Blockbuster Trap: Adopt AI’s Netflix‑Style Model in Healthcare

Key Takeaways:

AI's main impact isn't job replacement but changing the entire healthcare system, like streaming changed entertainment.
Focusing only on individual AI skills ignores deeper organizational and business model threats.
Value from AI might shift to platform owners and data aggregators, not just existing providers.
Entire healthcare roles and workflows could become obsolete, not just faster.
Leaders must question their core business models and structures, not just adopt AI tools incrementally.

Heard the latest mantra? "AI won't take your job, but someone using AI will." Sounds empowering, right? Wrong. Dangerously wrong. Especially in healthcare.

That line is the business equivalent of telling Blockbuster executives in the early 2000s, "Don't worry about that quirky DVD-by-mail startup, just focus on optimizing your store layouts and maybe tweak the late fees!" It focuses you squarely on the wrong threat.

I've spent a decade challenging ossified management orthodoxies. And let me tell you, the biggest blind spot for leaders today isn't if their people use AI. It's whether their entire organization, their business model, their very reason for being — like Blockbuster's vast network of physical stores — will even be relevant in a world reshaped by this technology.

Stop optimizing the video store and start thinking about the streaming revolution. Here’s why that seemingly harmless phrase that someone who masters AI will take your job may set healthcare and other industry leaders up for strategic turmoil.

The Transformative Impact of AI in Healthcare: Beyond Tools

Alright, let's get brutally honest. We love simple narratives. We crave certainty. And that little phrase, "someone using AI will take your job," feels like a solvable problem. Learn a new skill, adopt a new tool and presto! You're safe. Your organization is safe.

It’s a comforting illusion. A dangerous opiate for the C-suite. Because AI in healthcare isn't just another tool like the electronic health record or a robotic surgery arm — improvements layered onto the existing system, like Blockbuster adding more candy aisles. No. AI has the potential to be a systemic disruptor. It’s not about making the old ways faster; it's about making them obsolete.

Think about Blockbuster. At its peak, it was a retail powerhouse. Efficient operations, strong brand, convenient locations. Utterly irrelevant when Netflix simply bypassed the physical store model entirely, first with DVDs by mail, then decisively with streaming, changing the logic of home entertainment distribution. Focusing on optimizing the stores (individual skills/tools) missed the strategic shift in delivery platforms (systemic change).

We're doing the same with AI in healthcare. We're fiddling with AI "tools" to optimize existing processes while the fundamental architecture of care delivery, diagnostics, drug discovery and patient relationships is being rewritten by algorithms and data flows — a shift as profound as the move from physical media to digital streaming.

Let’s dismantle some of the pervasive fallacies blinding leaders, drawing from sharp analysis like that dissecting the "AI won't take your job..." trope:

Fallacy 1: The Automation vs. Augmentation Mirage

We’re obsessed with whether AI will replace a radiologist or merely assist them. This completely misses the bigger picture. AI isn't just about automating tasks (reading scans) or augmenting capabilities (highlighting anomalies). It's about changing the system itself so dramatically that the need for that specific task, performed in that specific way, by that specific role, might simply evaporate.

Imagine AI-powered diagnostics moving from the hospital basement (the back room of the video store) to the patient's smartphone or a neighborhood pharmacy (direct-to-consumer streaming). Imagine preventative analytics identifying disease risk so early that traditional diagnostic pathways become less central. The question isn't if AI helps the radiologist; it's whether the centralized, specialist-driven model of radiology as we know it survives the AI revolution.

Real-World Glimpse: Look at companies like Digital Diagnostics (formerly IDx-DR), whose AI system received FDA clearance to detect diabetic retinopathy without requiring a clinician to interpret the images first. It's deployed in primary care settings, shifting where and how this diagnosis happens, bypassing traditional specialist workflows for initial screening — a new distribution channel.
Fact Check: AI algorithms are increasingly matching or surpassing human experts in diagnosing certain conditions from medical images, like specific cancers or eye diseases. The potential isn't just assistance; it's workflow transformation.

Fallacy 2: The Naive Productivity Gains Assumption

"AI will make us more productive!" Great. But who captures the value? History teaches us that productivity gains don't automatically flow to the workers doing the task or even the organizations employing them. Value often shifts to those who control the new system — the platform owners, the data aggregators and the coordinators of the AI-driven network. Remember how Netflix, not the movie studios initially, captured massive value by controlling the streaming platform?

If AI dramatically speeds up drug discovery, will that translate primarily into lower drug prices and better patient access? Or will it consolidate power and profits in the hands of the few companies that own the most potent AI discovery platforms and the vast datasets that fuel them? If AI streamlines hospital operations, do the savings get reinvested in patient care, or do they primarily benefit insurers or technology vendors orchestrating the new efficiencies?

Real-World Glimpse: Consider the rise of large tech companies entering the healthcare space. They aren't just selling software; they are building ecosystems (think Amazon Care, Google's health initiatives). Their strategic aim is often to control the platform, aggregate data and coordinate services — potentially capturing value previously distributed among traditional providers, much like streaming platforms aggregated content and viewers.
Fact Check: Global investment in healthcare AI startups reached billions annually, with significant funding directed towards platforms aiming to optimize or intermediate parts of the healthcare value chain.

Fallacy 3: The Static Job & Workflow Delusion

We tend to see jobs as fixed bundles of tasks. "What parts of a nurse's job can AI do?" Wrong question! AI enables us to reimagine entire workflows. Roles aren't static; they are organizational constructs that can be completely redefined or eliminated when the underlying system changes — just like the role of the video store clerk changed dramatically with streaming.

Instead of just making the existing patient intake process faster, AI might enable continuous, passive monitoring that changes the nature of intake altogether. Instead of AI helping a doctor choose the right treatment from a list, AI might manage chronic conditions predictively, reducing the need for certain episodic interventions. We need to ask: Will this workflow even exist in five years? Will this role, as currently configured, be necessary?

Real-World Glimpse: Remote patient monitoring (RPM) platforms powered by AI analytics are shifting chronic disease management from reactive clinic visits to proactive, continuous care in the patient's home. This changes the tasks and focus for nurses and care managers, moving them towards data interpretation and virtual intervention rather than purely in-person assessments — a new model of "delivery."
Fact Check: The global RPM market size is projected to grow substantially, driven by the need for cost-effective chronic care management and enabled by AI, IoT and telehealth technologies.

Fallacy 4: The Myopic 'Me vs. Them' Competition Focus

The popular phrase encourages you to worry about your colleague, Dr. Smith, learning to use an AI diagnostic tool better than you. That’s the Blockbuster thinking again — worrying if the store down the street has a slightly better selection! The real competitive threat isn't the person using the tool slightly better within the old system. The real threat is that the skill itself — or the profession built around it, or the entire business model like physical rental — becomes less valuable or even irrelevant because AI enables a fundamentally different, more efficient or more effective way of achieving the same outcome, perhaps delivered by an entirely new type of entity (like Netflix).

Are hospital systems worried enough about nimble, AI-native startups offering decentralized, specialized care at lower costs — the healthcare equivalent of streaming services challenging the cable bundle? Are pharmaceutical companies adequately prepared for AI-driven discovery platforms that could emerge from outside the traditional pharma giants? The competition isn't just peer vs. peer (Blockbuster vs. Hollywood Video); it's incumbent vs. insurgent, old model vs. new paradigm (Blockbuster vs. Netflix).

Real-World Glimpse: Companies specializing in AI for clinical trial optimization (like Medidata AI or Phesi) are changing how trials are designed and run. This doesn't just help existing pharma researchers; it shifts power and creates new competitive dynamics based on who can apply data and AI most effectively to reduce trial times and costs — a different competitive battlefield.
Fact Check: AI is estimated to potentially save the pharmaceutical industry billions annually by optimizing drug discovery and development processes, including clinical trial design and patient recruitment.

It's Not Just About Tools, It's About Power and Structure

Let’s dispense with the "Neutral Tool Fallacy." AI is not neutral. Algorithms embed assumptions, reflect biases in their training data and redistribute decision-making power. Who designs the AI systems used in your hospital? Whose values are embedded in the risk scores? How does AI change the influence of clinicians versus administrators versus technologists? Implementing AI isn't just installing software; it's rewiring the power dynamics of your organization.

Furthermore, clinging to the belief in "Workflow Continuity" — that AI will just make existing processes faster — is naive. Like streaming didn't just make getting movies faster but changed how we consume them, AI can obliterate entire workflows, not just optimize steps within them.

And the "Stable Salary" and "Stable Firm" fallacies are equally perilous. Expecting pay structures and organizational forms to remain unchanged when the very value of tasks and the viability of existing business models (like Blockbuster's retail footprint) are being questioned by AI is wishful thinking. AI will change which firms thrive and which wither. It will shift economic value between roles and professions. Ignoring this is like Blockbuster assuming its stock price was immune to the internet.

Real-World Glimpse: Studies have revealed significant racial bias in healthcare algorithms used to predict patient risk or allocate resources, demonstrating how AI systems are not neutral and can perpetuate inequities if not carefully designed and audited. This highlights the power embedded in AI design.
Fact Check: The healthcare sector faces significant pressure to evolve its business models. A report by Deloitte suggests that future health ecosystems will likely be more consumer-centric, data-driven and preventative, potentially disrupting traditional fee-for-service models — indicating firms and workflows must change.

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The Real Questions for Healthcare Leaders

So, ditch the comforting but misleading platitudes. Stop asking, "How can my people use AI to stay ahead?" Start asking the hard, strategic questions:

How is AI restructuring the entire healthcare delivery system, research and operations — like streaming restructured home entertainment?
Will our current organizational structure, roles and workflows — our "physical stores" — even exist or be relevant in the AI-enabled future?
Where is value migrating in this new landscape, and how do we position ourselves not just to survive, but to lead the transformation?
Are we just rearranging the aisles in our Blockbuster store, or are we developing the strategic foresight and organizational agility to build the next Netflix of healthcare?

This isn't about incremental improvement. It's about fundamental reinvention. It requires courage, imagination and a willingness to challenge deeply ingrained assumptions about how healthcare works. Anything less is just polishing the shelves in a store nobody visits anymore.

The challenge of AI in healthcare isn't technical; it's conceptual. It's strategic. It demands that leaders become architects of the future, not just guardians of the past. Are you ready to dismantle the old orthodoxies and build something fundamentally new, something truly fit for an AI-powered future? Or will you be caught optimizing a business model whose relevance is fading fast? The choice, and the consequences, are yours.

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