Digital twins: Why the public sector cannot afford to stay analogue

This article was first published in the Spring 2026 issue of the Public Sector Journal, thanks to Hāpai Public.
 

Aotearoa New Zealand’s public sector is increasingly being asked to manage whole systems rather than individual parts, where one decision ripples across many. A decision about where homes are built affects transport demand, water infrastructure, flood exposure, community services, and long-term public cost. Yet decision-makers are still expected to rely on fragmented evidence, static reports, and siloed views of performance.

The decisions facing government are becoming larger, more urgent, and harder to reverse. Infrastructure choices last for decades. Fiscal constraints mean every dollar must work harder.

From data to decisions

In many cases, much of the data needed to support better decisions already exists. What is often lacking is the ability to connect, interpret, and apply that data to inform decision-making. Real-time digital twins can help close that gap.

What is a digital twin?

A real-time digital twin is an integrated, data-driven model of a real-world asset, network, place, or system. It might represent a water network, a transport corridor, a region, or a combination of these. These help decision-makers test options, understand trade-offs, and model consequences before decisions are locked into the physical world.

Put simply, a dashboard can show what is happening now. A real-time digital twin can help decision-makers understand what could happen next.

How it works

A real-time digital twin works by bringing together data that is usually held in separate places and turning it into a living model of how a system behaves. In practice, that means connecting information about assets, demand, capacity, geography, cost, risk, and performance, then using that model to simulate how different parts of the system interact over time. For example, digital twin platforms, such as Deloitte’s Optimal Reality, have been used to model transport networks using historic, simulated, and real-time data, allowing decision-makers to test multiple scenarios before acting in the physical world.

The value of a digital twin is making trade-offs visible. In water, it can show how rainfall, growth, asset performance, and maintenance decisions interact. In transport, it can test how mode-shift policies affect congestion, journey times, and network resilience. The digital twin helps decision-makers test practical ‘what if’ scenarios before problems occur.

Beyond the dashboard

This is where digital twins differ from traditional reporting. A report can describe a problem. A dashboard can show whether performance is improving or deteriorating. A digital twin can test the possible responses to a problem to guide solution design. It can compare options, reveal second-order effects, and show where an apparently efficient choice in one part of the system may create avoidable cost, risk, or service pressure somewhere else.

Complex systems need more integrated management, which in turn requires better analytical tools. If government is being asked to make more joined-up decisions, it also needs more joined-up evidence.

This matters most where the public sector faces its hardest pressures. Government is dealing with more frequent and complex shocks, infrastructure strains, and emergency management demands. Digital twins can test for stress before it arrives: how flooding could affect a water network, how growth could strain transport corridors or healthcare facilities, or where the failure of one asset could cascade across services.

Where AI fits

Artificial intelligence (AI) can further strengthen this by helping digital twins spot patterns that people may not notice quickly enough. Instead of only testing a small number of ‘what if’ scenarios, AI can help scan for changing conditions and indicate where pressure may be building. Used well, AI doesn’t make the decision for government, but it can help decision-makers ask better questions earlier.

This does not replace human judgement, and its output is only as reliable as the data and assumptions behind it. Nor does it create certainty. What it offers is better foresight: helping decision-makers move from reacting after failure towards planning, prevention, and preparedness.

The fiscal case

In a constrained fiscal environment, public investment cannot be assessed project by project or asset by asset alone. Delaying maintenance may reduce short-term expenditure but increase long-term risk. Investing in resilience may appear expensive until the avoided future damage is properly understood. Digital twins allow options to be tested before money is committed, showing trade-offs between cost, service levels, risk, resilience, and long-term value.

Digital twins matter because government is being asked to make decisions in environments characterised by complexity, risk, and investment choices at a system level. As New Zealand confronts pressures across all infrastructure, it needs better ways of understanding how those systems behave.