What the Deloitte Tech Trends 2026 mean for the future of New Zealand healthcare
3. The AI infrastructure reckoning: Optimising compute strategy in the age of inference economics
As AI adoption moves from experimentation to scaled deployment, Tech Trends 2026 points to a shift in cost dynamics. The largest expense is no longer AI model training, but ongoing inference – the day‑to‑day operation of AI systems at scale. This need challenges cloud‑only approaches and forces organisations to reconsider long‑term cost, resilience, and governance. Healthcare must also balance FinOps maturity with heightened requirements for data sovereignty, security, and reliability.
Example: ‘SuperCloud’ approaches
While hybrid cloud is becoming the norm, many organisations are going a step further by adopting ‘SuperCloud’ approaches. These add a layer over different cloud and on‑premise systems, making it easier to manage costs, governance and operations in one place, even when the underlying technology is spread across multiple environments.
For healthcare, this means placing different types of work where they make the most sense. High-volume, predictable processes can run on more controlled, cost-effective systems, while the cloud can be used when flexibility is needed, such as for innovation or handling spikes in demand.
In New Zealand, infrastructure decisions must also consider the risks associated with ageing on‑premise environments that were not designed for continuous, compute‑intensive workloads. In a small health system with constrained budgets and spread-out services, early and deliberate architecture choices are critical not just for AI performance, but for long‑term resilience and sustainability.
4. The great rebuild: Architecting an AI‑native tech organisation
Rather than layering AI onto legacy systems, Tech Trends 2026 paints a picture of organisations undergoing a fundamental rebuild to become AI‑native. This year marks the point where AI shifts from experimentation to essential infrastructure. In AI‑native organisations, intelligence is designed in, not bolted on.
In healthcare, this shift to being AI-first extends beyond technology teams. Many core workflows remain fragmented, manual, and clinician‑unfriendly. Becoming AI‑native requires rethinking how care is designed, coordinated, measured, and governed. The concept of Zero Operations (Zero Ops) captures this shift. The goal is to reduce non-essential manual processes and operational friction as close to zero as practical, with intelligent systems managing routine work and escalating only when human judgement is required.
Example: Emergency triage
AI‑enabled emergency care models are a great way to picture this approach. Virtual EDs use AI to predict demand, update resource availability in real time, and recommend patient arrival windows based on urgency and capacity. AI agents can also triage patients by analysing symptoms and health information via text or phone to determine urgency and use specific platforms to direct patients to the appropriate clinician. For New Zealand, this represents a credible pathway to improving workforce sustainability by returning time and decision‑making space to clinicians.
5. The AI dilemma: Securing and leveraging AI for cyber defence
This year’s Tech Trends frames security as both a risk and an enabler of AI adoption. AI increases the number of ways cyber threats can get into digital systems, but it also gives organisations better tools to detect and respond to them quickly. This is especially important in healthcare, where patient data is highly sensitive. As AI becomes part of everyday clinical and operational decisions, strong security needs to be built in from the start, not added later.
AI‑enabled cyber defence offers continuous monitoring, anomaly detection, and rapid response capabilities. In healthcare, organisations need to secure AI within operating models that place trustworthy AI at the core to ensure overall security.
Example: GenAI cybersecurity in action
Generative AI is shifting cybersecurity from manual, alert‑driven processes to continuous, context‑aware oversight. Rather than overwhelming teams with disconnected alerts, AI connects signals across systems, explains what is happening and why it matters, and recommends policy‑aligned actions.
In healthcare settings, this enables early detection of suspicious access to patient data and more consistent, auditable responses.
The future of technology use in healthcare
Taken together, these trends show that technology is becoming part of the fabric of healthcare, not just an added layer. The examples outlined all point to the same goal: reducing friction in the system so the healthcare workforce can focus on care.
For New Zealand, the opportunity lies in using these approaches to address workforce pressure and rising demand in a deliberate way. This is not about automating current processes to allow the system to run faster, but about redesigning how work is done and embracing a hybrid AI-human workforce by design. If applied thoughtfully, AI can help create a system that is more sustainable for clinicians and more responsive to the people it serves.
