To realise AI’s promise in healthcare, fix the foundations first

Healthcare systems across Asia Pacific and Japan (APJ) are under mounting pressure. Ageing populations, workforce shortages, rising patient demand, and uneven access to care are pushing providers to rethink how care is delivered at scale.

Artificial intelligence is increasingly positioned as a way to relieve that strain, from supporting clinical decision-making to automating administrative workloads and improving patient engagement and ultimately create more capacity

That momentum is already visible. According to the Nutanix Enterprise Cloud Index Report, 99 per cent of healthcare organisations are currently leveraging GenAI applications or workloads today, more than any other industry surveyed.

This includes a mix of applications from AI-powered chatbots to code co-pilots and clinical development automation. This is echoed in IDC’s prediction that healthcare GenAI investments are expected to double by 2026 in the Asia Pacific region, excluding Japan.

The sobering fact is that most medical devices are connected now and have become smart devices; however, in most cases, there is no cybersecurity built into these devices. Imagine what can happen when a morphine pump is compromised! Bringing AI to the mix will make this even more complicated.

Yet, enthusiasm for AI is advancing faster than the data security, governance frameworks and digital infrastructure required to support it safely and effectively.

This gap is particularly acute for healthcare organisations across APJ, which operate within diverse and complex regulatory environments while being entrusted with some of the most sensitive personal data. This raises a more fundamental question: can AI meaningfully transform care if the systems underpinning it were never designed to support it at scale?

Legacy systems are the silent bottleneck showing AI’s rise

The healthcare industry across APJ could save up to USD110 billion by 2027 by optimising workflows with intelligent automation and artificial intelligence, according to IDC. However, the Enterprise Cloud Index data is stark: 79 per cent of global healthcare IT leaders say integrating AI into existing infrastructure is their biggest barrier to adoption.

The problem? Many legacy environments were never designed to handle the scale, speed, or complexity of modern AI workloads.

More advanced use cases, such as digital twins, expose these limitations most clearly. In healthcare, digital twins can model anything from a single patient’s physiology to an entire hospital system, allowing clinicians to simulate treatment options, predict risks, reduce costs, and optimise workflows before taking action in the real world.

But they place extreme demands on infrastructure — from high-performance computing and low-latency networks to IoT integration and multicloud platforms for data storage and interoperability.

In organisations still reliant on fragmented or outdated systems, these demands quickly become failure points. If healthcare systems were railway networks, the most advanced organisations and countries could be likened to running bullet trains on modern, well-laid tracks.

Where infrastructure has not kept pace, attempting to deploy advanced AI risks derailment. And in healthcare, a crash isn’t just an inconvenience or costly, it’s life-threatening.

This risk is compounded by security and governance gaps. 96 per cent of global healthcare organisations acknowledge that their current data security and governance frameworks cannot fully support GenAI. For a sector that handles some of the most sensitive personal data, this isn’t just a technical flaw, but a direct threat to patient trust and system integrity.

Modernisation is a strategic inperative, not a choice

Many organisations across APJ are uniquely positioned to leapfrog traditional limitations. New market entrants and those in emerging economies have a rare opportunity to bypass legacy hurdles and design AI-ready systems from the ground up, creating environments where AI can scale, adapt, and innovate safely.

More mature organisations and markets should still look to move at pace, but modernise carefully, blending AI with existing clinical workflows without disrupting patient care or regulatory requirements. It’s a delicate balancing act, but essential to preserve public trust.

We’re already seeing encouraging signs across the region, from Singapore using AI to bolster national healthcare initiatives to public-private innovation hubs in Australia. These are the models we need more of: purposeful, strategic modernisation with clear goals to improve patient outcomes, strengthen data protection, and deliver sustainable value at scale.

Healthcare leaders must see modern infrastructure as a strategic enabler, not a cost centre. Investing in flexible, secure platforms is now non-negotiable; they’re the backbone for safe and effective AI deployment.

Collaboration will define success

For the healthcare industry to realise these goals, it needs to work hand in hand with governments, regulatory bodies, and technology partners to define standards for interoperability, security, and ethics. Doing so can help entire countries across the region move into leadership positions in AI-driven healthcare.

At the same time, the mindset around AI deployment must evolve. Fast implementation is meaningless if it comes at the cost of safety, privacy, or patient trust. The true measure of success lies in how AI improves care and protects data, not just how quickly it’s deployed.

The cost of doing nothing is hefty. If providers ignore AI, clinicians will spend more time on documentation instead of patient care, and billing, coding, scheduling, and claims remain manual and error‑prone.

Operational costs stay high while AI-enabled peers reduce them, leading to competitive disadvantages, particularly in private healthcare. Lack of AI does not end here, AI void means slower diagnosis in areas like radiology, pathology, and oncology

Missed patterns in population health and chronic disease management, less personalised treatment plans.

The healthcare industry stands at a crossroads. Embrace strategic modernisation and collaboration, and APJ organisations could set a global benchmark for secure, AI-powered care. Ignore the infrastructure gap, and it’s patients who will ultimately bear the cost.

In healthcare, infrastructure isn’t just a platform; it’s a promise; it is a lifeline. One that we cannot afford to break.