By Terver Orbunde

A public health business model defines how organizations create, deliver, capture and sustain value to achieve population health outcomes. For decades, these models in low- and middle-income countries (LMICs) evolved slowly. Innovation followed a largely linear, donor-driven path. It took 20–30 years for major interventions like expanded immunization programs to scale widely across LMICs. Even digital health adoption remained gradual. By 2019, only about 58% of countries had national digital health strategies, with many fragmented in implementation (World Health Organization [WHO], 2021).

That world no longer exists.

In less than a decade, public health systems have been hit by two overlapping forces: the acute shock of COVID-19 and the accelerating transformation driven by Artificial Intelligence. Together, they mark a shift from slow, predictable change to compressed, now-compounding disruption. What makes this moment unique is not just disruption, but speed.

• 1970s–2010s: slow, donor-driven diffusion of innovation
• 2020–2022: shock-driven acceleration during COVID-19
• 2023–present: continuous, compounding change driven by AI

Systems that once adapted over decades are now expected to evolve within years, sometimes months. This compression is where most current models begin to fail.

As such, the question is no longer whether these waves will change public health delivery. The real question is whether our business models are prepared not just to survive, but to thrive through both.

Dual-Wave of Creative Destruction in Low- and Middle-Income Countries

Creative destruction, a concept popularized by economist Joseph Schumpeter, describes how innovative technologies, products, and business models continuously destroy outdated, less efficient ones to make way for superior alternatives. This often painful process of “industrial mutation” drives progress by replacing obsolete systems with more effective ones.

Never in recent history has the public health sector in low- and middle-income countries (LMICs) faced two such major waves of creative destruction in such a short span, namely:

• The First Wave: COVID-19 and the Compulsory Reboot – The pandemic did not create weaknesses in LMIC health systems. It revealed them. At the height of the crisis, over 25 million children missed routine vaccinations in 2021, up from 19 million pre-pandemic (WHO & UNICEF, 2022). This was more than a marginal disruption, it was a breakdown of core service delivery. Financial structures proved equally fragile. In many Sub-Saharan African countries, 20–40% of health expenditure is donor-funded, leaving systems highly exposed when global priorities shift (World Bank, 2023). As funding was redirected during COVID-19, public health models (service delivery, business, etc.) were forced into reactive adjustments rather than sustainable redesign. Operationally, supply chains became critical failure points. Stockouts increased, procurement slowed, and heavy reliance on imports and centralized systems became visible risks (Sharma & Osoro, 2023). In spite of this, adaptation did occur. Telemedicine use increased significantly across many LMICs during the pandemic, reflecting rapid but uneven digital adoption (GSMA, 2021). Yet most of these changes were temporary. Once emergency funding declined, systems struggled to sustain them. The uncomfortable conclusion was clear: the problem was not just the shock. It was the system’s design itself.

• The Second Wave: Artificial Intelligence as Structural Pressure: If COVID-19 exposed fragility, Artificial Intelligence is now testing whether public health delivery systems and models can evolve at scale. Unlike the pandemic, AI is not a temporary disruption. It is a general-purpose technology reshaping how value is created, delivered, and captured. Across LMICs, over 60% of countries now report having national digital health strategies, yet far fewer have the infrastructure, governance frameworks, or workforce capacity to implement AI at scale (WHO, 2021). Early applications show promise. AI-supported diagnostics have demonstrated significant improvements in accuracy in controlled settings (Topol, 2019), while mobile-first tools are expanding access to care in low-connectivity environments. But most of these gains remain at the pilot stage. The deeper disruption lies not in the technology itself, but in business models. The shift is evident in examples such as Zipline, where AI-enabled logistics are paired with pay-for-performance models, replacing grant-dependent systems with outcome-based revenue structures (U.S. State Department, 2025). This is creative destruction in practice: not just improving efficiency but redefining how systems are funded and sustained (Callegari & Feder, 2022). This is creative destruction in action: destroying slow, grant-dependent logistics while creating efficient, locally paid, tech-enabled systems.

So, What Does This Mean?

This dual wave of creative destruction reveals that public health business models in low- and middle-income countries have entered an era in which successive disruptions are no longer exceptional events but the new normal. Models that barely recovered from the COVID-19 shock now face the deeper, structural force of Artificial Intelligence at a time of shrinking donor funding and persistent insecurity in fragile settings. Ultimately, one thing is clear, technology alone is not enough. If public health leaders focus solely on adopting AI tools without fundamentally redesigning revenue streams, governance structures, equity safeguards, and human-centric processes, we risk repeating the same cycle seen during the pandemic: short-lived pilots, growing inequities, and eventual collapse once external support fades. At the same time, this moment also presents a historic opportunity.

For Nigeria and other LMICs, the implications are urgent. The future of public health will be determined not by how quickly we adopt new technologies but by how deliberately we redesign our business models to be resilient, equitable, and locally owned. Failure to act decisively risks widening the health equity gap. Success, however, could enable a genuine leapfrog toward stronger, more sustainable, and more inclusive health systems that can withstand future shocks while delivering meaningful Universal Health Coverage.

At Sydani Group, this is where the real opportunity lies. Across work in health systems strengthening, data systems, and supply chain transformation, our focus is increasingly on helping institutions move from reactive resilience to adaptive capability. Not just delivering programs, but designing systems that can evolve with changing demands, technologies, and constraints.

Towards Antifragile Public Health Business Models

The waves have arrived. The only question remaining is whether we will allow them to destroy outdated systems only to rebuild the same vulnerabilities or whether we will harness them to build antifragile public health models fit for the next decade and beyond. To prepare for this dual-wave reality, we need more than resilient models that merely survive disruption. We need antifragile public health business models – systems that become stronger, smarter, and more effective because of shocks and volatility. Think of how modern AI systems work. Large Language Models (LLMs) improve through exposure to vast amounts of data and real-world feedback. Retrieval-Augmented Generation (RAG) takes this further: instead of relying only on pre-trained knowledge, the system actively retrieves the most relevant, up-to-date information from trusted sources before generating responses. The more it encounters new challenges and diverse data, the more accurate and useful it becomes. Public health business models should operate with the same anti-fragile logic. Rather than fearing the next shock – whether another pandemic, funding cut, or technological leap – these models should be deliberately designed to learn, adapt, and improve from each wave. They should treat disruption not as a threat to endure, but as valuable feedback that strengthens their core capabilities.

Conclusion: Embracing Dual Waves for Antifragile Health Futures

Public health business models in Nigeria and LMICs stand at a crossroads amid dual waves of creative destruction – COVID-19’s brutal reboot and AI’s compounding reinvention. While the pandemic exposed the fragility of grants and siloed systems, AI promises predictive power and efficiency, as seen in tools like AwaDoc and Zipline’s pay-for-performance drones. Yet preparation remains uneven: many models limp from post-COVID funding cliffs, ill-equipped for AI’s demands on infrastructure, skills, and ethics.

Critically, antifragility demands more than tech hype. Over-reliance on imported AI risks deepening digital divides and biases, sidelining CHWs and local contexts. True progress hinges on balanced redesign: blending revenue streams, sovereign data governance, and human-centered integration to turn shocks into strength, much like RAG systems that evolve through real-world feedback.

The path forward requires more work: leaders must deploy resources and commitment to developing practical frameworks, like Dual-Wave Preparedness, grounded in Return on Impact, to stress-test and upgrade models now. In fragile settings, this isn’t optional, it’s survival. By harnessing these waves, Nigeria and LMICs can pioneer equitable, sustainable health systems that not only endure but redefine Africa’s future.

Key References:

1. Callegari, B., & Feder, C. (2022). Entrepreneurship and the systemic consequences of epidemics. International Entrepreneurship and Management Journal, 18(4), 1653–1684.
2. (2021). The Mobile Economy Sub-Saharan Africa 2021.
3. Sharma, A., & Osoro, I. (2023). Public health progression and related challenges. Global Health Journal, 7(1), 1–2.
4. Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House.
5. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
6. S. State Department. (2025). Public-Private Partnerships in Health Logistics (Zipline Expansion).
7. World Bank. (2023). World Development Indicators: Health Expenditure.
8. World Health Organization (WHO). (2021). Global Strategy on Digital Health 2020–2025.
9. WHO & UNICEF. (2022). Progress and Challenges with Achieving Universal Immunization Coverage.