Digitalization and artificial intelligence are rapidly reshaping health systems, yet they have also become an arena in which knowledge, power and agenda-setting are increasingly concentrated among technology providers. Advanced AI capabilities, ranging from large-scale data infrastructure to model development and system integration, are largely owned, governed and iterated outside public health systems, further widening the gap and asymmetry of knowledge, capacity and bargaining power between the technology sector and health systems, particularly in low- and middle-income settings. Health authorities are often positioned as downstream adopters rather than co-designers of digital futures, while public goals such as equity, continuity of care and system resilience struggle to shape the direction of innovation.

These asymmetries matter most where markets fail to function well. While tertiary hospitals and well-resourced urban centers can often attract investment, talent and experimentation, primary care, rural and remote areas, and underserved populations remain structurally unattractive to market-driven AI innovation. It is precisely in these settings, where needs are greatest and alternatives are few, that existing public-private arrangements are inadequate.

This imbalance has generated recurrent friction points across the AI lifecycle: tools trained on data that poorly represent frontline populations; algorithms that cannot be audited or adapted by public providers; clinical decision‑support systems misaligned with real‑world workflows; and procurement processes that privilege short‑term pilots over system learning. While these appear as technical problems, they are also symptoms of deeper institutional misalignment. Innovation is advancing faster than the public frameworks required to steward it toward shared outcomes.

The result is a checkmate situation. Many AI solutions remain trapped in cycles of pilots and proofs of concept, failing to reach scale or demonstrate durable impact on health outcomes. The absence of credible, long‑term business and financing models delays the generation of evidence on effectiveness, equity and value for money. Without predictability and scale, private actors cannot invest for the long term or absorb the risks of delivering public value. Conversely, without robust evidence and system‑level benefits, health systems are reluctant to commit to sustained procurement, integration or reimbursement. Each side waits for certainty from the other, and innovation stalls where it is needed most.

What is failing is the infrastructure of the relationship between them. Underdeveloped are the shared values and principles that define success beyond commercial performance; aligned incentives for long‑term public outcomes; shared accountability when technologies shape clinical decisions; governance standards that ensure transparency and trust; data‑stewardship frameworks that enable learning while protecting rights; procurement mechanisms that privilege mission over novelty; and financing models that share risk and reward. Without these foundations, public-private relationship won’t deliver what matters.

Beyond digital technologies, similar dynamics are evident across the broader landscape of health innovations and health products, including pharmaceuticals, diagnostics, medical devices and service delivery models. The development, pricing and distribution of these innovations are often driven by global market incentives that prioritize commercially viable products and populations, leaving critical gaps in access for underserved settings. High-cost medicines, limited investment in prevention and primary care–oriented solutions, and fragmented pathways from innovation to adoption illustrate how existing models fail to align private innovation with public health needs. While mechanisms such as public–private partnerships, managed entry agreements, access programs, social enterprises, and other mission-driven business models have emerged to bridge these gaps, they remain uneven in their ability to ensure affordability, sustainability and equitable access. As with AI, the challenge is not only technological but institutional: without governance frameworks, financing models and incentive structures that deliberately prioritize equity and system-level value, health innovations may create more disparities rather than addressing them.

Emerging models such as social enterprises, benefit corporations, blended-value enterprises, and other mission-driven business approaches are also increasingly shaping how commercial actors engage with health outcomes. These models attempt to balance financial sustainability with explicit social and public health objectives, including equitable access, community benefit, and long-term system resilience. Exploring how governance and financing mechanisms can support such models may offer additional pathways for aligning commercial incentives with public value.

What does it take for AI and innovation to support clinical decisions in understaffed, resource-constrained primary care clinics without on-site specialists, where patients lack alternative care options? The key isn't in technological sophistication but in whether innovation is co-shaped to serve the underserved. Addressing this requires redesigning public–private health relationships and business models to ensure AI and innovation are co-created, governed, and used as a common good, not just market tools.