The Next 10 Years and What These Industries Will Actually Become - Part 1

Most leaders focus on improving, higher margins, implementing smarter systems, and fixating on smoother operations, however, there are underlying forces that are reshaping industries in ways that incremental improvement alone cannot address. In the early 2000s, Blockbluster was the dominant force in home entertainment, with thousands of stores, strong revenue, and a familiar operating model. Leadership focused on optimising that model streamlining in store experiences, improving inventory systems, and competing with emerging rental kiosks. 

Meanwhile, Netflix was emerging and reshaping the rules of the game with a subscription model, data‑driven content decisions, and a distribution method that did not depend on physical real estate. Blockbuster had the opportunity to buy Netflix in 2000 for $50 million but declined, famously laughing at the proposal. In 2000, Netflix CEO Reed Hastings and co-founder Marc Randolph met with Blockbuster CEO John Antioco in Dallas to propose that Netflix become Blockbuster's online division. Blockbuster, then a $6 billion company with thousands of stores, dismissed the offer because Netflix was losing money. Blockbuster executives viewed the internet as a passing trend and the price too high, ultimately failing to see the potential of digital disruption. 

Within a decade, Netflix grew to dominate home entertainment, while Blockbuster filed for bankruptcy in 2010. Today, Netflix is not merely a streaming platform; it is a vertically integrated entertainment company that combines distribution, data intelligence, and original content production within a single operating model.

Blockbuster, by contrast, remained anchored to its operational strengths in physical retail and inventory management. Its competitive advantage was real, but it was tied to an ageing structure. The company continued optimising store performance and late-fee economics while the industry’s economics were being rewritten. In doing so, leadership remained committed to refining yesterday’s assumptions rather than confronting the structural shift underway and that misalignment ultimately proved fatal.

History offers a useful reminder here for all of us and as Hannah Arendt, a German-born American historian and philosopher who lived through the rise of Nazism and the collapse of European institutions, observed, the most profound transformations rarely announce themselves as revolutions. They arrive disguised as improbable, more efficient, new, a movement and it is only later do we realise the change it ushers in whether good, bad or ugly. 

Arendt’s research was important because she showed how changes in the organising logic of institutions can unassumingly reshape power, responsibility, and human judgment. The next decade will feel like that for several core industries, such as healthcare, education and agriculture. The question leaders in these and other industries need to ask themselves is not whether these shifts will occur, but how consciously, ethically and responsibly they will navigate them. 

Over the coming four weeks, this series will examine what the next decade is likely to mean for healthcare, education, vocational training, and agriculture industries fundamental to national stability and prosperity. We begin this week with healthcare.

Healthcare Is Moving From a Treatment System to a Risk Management System

Healthcare today remains largely reactive, with hospitals, specialists, diagnostics, and procedural interventions forming the operational centre of the system. Funding models, whether public, private, or blended, are still predominantly structured around episodes of care, activity-based reimbursement, and treatment once illness is established, rather than sustained investment in prevention and upstream risk reduction. Even where preventive medicine has advanced, it is typically positioned after risk has already emerged, rather than embedded upstream in the design of health systems themselves.

Across advanced economies, the imbalance is consistent and the overwhelming share of health expenditure is directed toward managing established illness, while only a marginal proportion is allocated to public health and early intervention. In high-income countries, preventive care routinely accounts for less than a tenth of total health spending, despite its disproportionate potential to reduce long-term disease burden.

In the United States, healthcare spending approaches 17–18% of GDP, and investment is concentrated in inpatient, outpatient, and specialist care. European healthcare systems spend roughly 10% of GDP. Australia follows the same trajectory where public health and preventive services represent a small fraction of total expenditure, historically around 2% or less. Collectively, these patterns reveal a deeply entrenched model that continues to prioritise response over anticipation, despite mounting evidence that earlier intervention would yield greater system resilience and better outcomes.

The Rise of Predictive Systems

Despite the current focus, the landscape is shifting into further unchartered waters as artificial intelligence increasingly shapes how health systems act on continuous data and probabilistic indicators. Advocates suggest that predictive analytics, pattern recognition, and real-time data integration will enable earlier intervention and more personalised care. Critics, however, warn that overreliance on algorithmic outputs could mask deeper systemic challenges, including patterns of misdiagnosis, growing dependence on AI, pharmacological, and surgical interventions as first-line responses (if not a trauma event), gaps in clinical training and professional judgment, and entrenched cultural issues within healthcare organisations.

As AI-driven predictive models mature, healthcare decision-making is moving upstream, focusing not only on treatment and delivery, but on anticipating risk. These algorithms analyse vast, continuous data streams; risk scores, behavioural patterns, genetic markers, medical history, and longitudinal health metrics to estimate who is likely to become unwell, when, and why.

Imagine a middle-aged patient who has not been diagnosed with anything serious. Their genomic profile shows elevated cardiovascular risk. Wearable data reveals declining sleep quality and reduced activity over six months. Insurance data shows rising medication non-adherence. No single signal is alarming on its own. Together, an AI model flags elevated probability of a cardiac event within five years.

All these implications extend beyond care, they will affect areas such as insurance premiums and public health planning. Here is another scenario, in a traditional model, a patient develops type 2 diabetes before treatment begins, often once blood sugar is persistently elevated or complications emerge. Predictive AI, by contrast, can identify high-risk individuals years earlier by integrating factors such as diet, subtle lab changes, sleep and activity data from wearables, family history, medication adherence, and socioeconomic indicators. Early interventions can then be initiated: lifestyle coaching, medication adjustments, and more frequent monitoring, all before a formal diagnosis.

The Catch-22 of Predictive Healthcare

There are also several emerging catch-22s. One relates to the cost of engaging with predictive technologies, advanced diagnostics, insurance structures, and the compliance requirements that accompany them. The more systems invest in predictive capability, the more complex and expensive participation can become. At the same time, clinician knowledge gaps often intersect with varying levels of confidence in treatment decisions, creating tension between what technology suggests, what practitioners fully understand, and how decisively they are prepared to act.

In the UK, an NHS AI tool generated a false diagnosis for a patient, incorrectly indicating he had diabetes and suspected heart disease, which led to him being invited to unnecessary diabetic screening. The AI-generated medical record included fabricated details such as an invented diagnosis, symptoms, medications, and even a fictional hospital address. Although NHS officials characterised the error as a rare one-off human oversight, the incident highlights how inaccurate AI outputs in healthcare records can lead to erroneous clinical actions and patient distress.

In the U.S, an AI-enhanced surgical navigation system used in sinus surgery has been associated with numerous adverse events. Since AI integration, the FDA has received reports of at least 100 device malfunctions and adverse events, including cases where the system misled surgeons about instrument positioning, leading to injuries such as cerebrospinal fluid leaks, skull base punctures, and strokes in some patients. Lawsuits filed in Texas allege that the AI contributed to these injuries by directing surgeons incorrectly during procedures highlighting how algorithm-assisted tools intended to improve precision can, in practice, cause serious harm when the technology errs under real clinical conditions.

Where AI Performs Well

It has been shown that AI performs particularly well in more predictable domains such as cardiovascular screening where analysing routine ECG data has identified subtle electrical irregularities associated with future atrial fibrillation years before a clinical event. In several documented cases, individuals with no symptoms were flagged through algorithmic pattern recognition, underwent confirmatory testing, and commenced early lifestyle management that significantly reduced their stroke risk. Similarly, machine-learning tools applied to retinal imaging have detected early vascular changes linked not only to diabetic complications but to broader cardiometabolic instability, prompting earlier lifestyle and therapeutic intervention.

Be that as it may, expanded diagnostic capability also introduces greater complexity, including the risk of identifying abnormalities that are clinically insignificant or unlikely to affect the patient’s long-term health. Advanced functional panels such as the Dutch Test, comprehensive genomic screening, or continuous wearable monitoring already generate detailed biological data. When layered with AI interpretation, this depth of information can reveal variations, borderline markers, or statistical risks that may never translate into disease for a specific individual. Because physiology is highly individualised, what appears abnormal in aggregate data may represent benign variation in context.

The danger lies not in detection itself, but in how findings are interpreted and acted upon. Overly aggressive responses can lead to cascades of follow-up testing, medication, or procedural intervention that may not ultimately improve long-term outcomes.

AI-assisted diagnostics therefore sit at a pivotal intersection: capable of preventing serious illness through earlier recognition, yet equally capable of amplifying overdiagnosis if not anchored in sound clinical reasoning and disciplined judgment.

Structural and Ethical Implications

This current shift is subtle as care moves from episodic treatment to continuous risk management, and the role of hospitals and specialists will also evolve; they become escalation points in a system increasingly organised around prediction, prevention, and real-time management embedded into daily life.

A significant proportion of the population rarely needs a doctor or experiences serious health issues. Nonetheless, as predictive AI models analyse continuous health and lifestyle data across millions of people, questions are arising about fairness and incentives. Will individuals who are generally healthy face higher insurance premiums or reduced access to certain services simply because the algorithms flag potential future risk? Could the emphasis on predictive or genetic risk inadvertently penalise those who already live responsibly and rarely require medical intervention?

The shift toward data-driven prevention introduces ethical and social considerations. Systems designed to act on likelihood of illness may inadvertently reinforce inequities, especially if socioeconomic factors, lifestyle choices, or genetic predispositions influence risk scores. Without careful regulation and transparency, those who seldom engage with the healthcare system could be treated as higher-risk by default, even if they are currently healthy.

Ultimately, as predictive healthcare scales, organisations, insurers, and policymakers must balance the power of early intervention with fairness, ensuring that the benefits of AI-driven prevention do not come at the expense of penalising the majority who maintain good health. What changes next is scale and integration. Genomics, insurance data, behavioural signals, and clinical protocols begin to converge into unified risk-management frameworks. Instead of sitting in separate silos, these systems talk to each other.

The Changing Nature of Clinical Authority

The role of the clinician will undoubtedly shift, and doctors will not disappear. Medicine and health has always lived with variation and two doctors can look at the same patient and make different decisions, and for decades that difference has been accepted as “clinical judgment.” Much of it is reasonable. Some of it hides error, bias, or simple human limitation. Research suggests that diagnostic errors affect around one in ten patients.

Medicine has been built around individual authority for generations. Doctors are trained to take responsibility, make judgment calls, and stand behind their decisions. That sense of ownership is deeply tied to professional pride and patient trust. Algorithmic systems quietly disrupt that. They compare performance, surface error rates, and question decisions that once went unquestioned.

At the same time, decision-support systems promise something medicine has struggled to achieve: consistency. They can highlight missed signals, compare cases at scale, and reduce the chance that fatigue, habit, or intuition alone shapes outcomes.

For example, in cancer care today, a doctor might have a preferred treatment approach based on experience. An AI system, pulling from thousands of similar cases and genetic profiles, may suggest a different option with better long-term outcomes. The clinician’s role shifts from “this is what we do” to “here’s why we’re choosing this path, and here’s how the data and your circumstances fit together.”

What is currently changing is the nature of expertise itself. Authority moves away from certainty and toward interpretation, communication, and responsibility in a system where decisions are increasingly shared with data, with models, and with patients themselves.

This is where things start to feel uncomfortable, safety pulls one way, autonomy pulls the other. Efficiency promises better outcomes, but human discretion exists for a reason. What many people miss is that this shift is not mainly about technology. It is about identity.

The system is not really ready for that because medicine was never designed for reduced individual authority paired with constant oversight. When a model suggests one path and a doctor chooses another, the tension is immediate: Who’s accountable? Who carries the risk? Those questions have not been clearly answered.

Therefore, the existential challenge ahead is trust particularly at a time when institutional confidence is already fragile. Trust that these systems are designed to support clinicians rather than silently audit or override them. Trust that doctors remain accountable professionals exercising judgment, not technicians executing algorithmic instructions. And trust, from patients, that their care is still anchored in the prevention of harm, careful diagnosis, dignity, understanding, and human discernment not reduced to probabilities generated on a screen.

From Treatment to Trust 

The next decade will determine whether predictive healthcare becomes a disciplined, ethical extension of clinical wisdom or an over-engineered system that confuses probability with certainty. The real transformation is not technological, but institutional and human.

Next week, in Part 2, I turn to education, universities and vocational systems where a similar structural change is underway. As healthcare moves from treatment to risk management, education is moving from knowledge transmission to capability formation and human capital architecture. The same questions of identity, authority, trust, and long-term societal impact are beginning to surface there as well.