Why Most Digital Twin Programs Start Too Big

Written for public sector practitioners working at the intersection of infrastructure, planning, and digital governance.

Interest in digital twins across government is growing rapidly. Councils, agencies, and delivery authorities are exploring how real-world data, models, and analytics might improve planning, operations, resilience, and sustainability.

Yet many digital twin programs struggle to gain traction. Others stall after impressive pilots. Some deliver sophisticated models but little measurable change in outcomes.

The problem is rarely the technology. More often, digital twin initiatives start too big, too abstract, and too disconnected from how public sector decisions are actually made.

Digital Twins Are Not Models. They Are Decision Systems

A digital twin is not simply a digital representation of an asset or place. It is an operating environment that connects real-world data to support ongoing decisions about how the built environment is planned, delivered, and operated.

At its best, a digital twin enables decision-makers to understand current conditions, test scenarios, anticipate risk, and act with greater confidence. It provides a continuous feedback loop between what is happening in the real world and the decisions that govern it.

For public sector organisations, this distinction matters. The value of a digital twin does not lie in visual fidelity or system integration alone. It lies in whether decisions improve as a result.

Why Public Sector Context Changes the Equation

Public sector organisations operate under conditions that are very different from those assumed in many digital twin case studies.

Decision-making is distributed across departments, contractors, service providers, and elected officials. Accountability is shared. Budgets are cyclical. Political and community scrutiny is constant. Many commitments are difficult or impossible to reverse once made.

In this environment, the most damaging failures are rarely technical. They occur when decisions are made too late, without sufficient evidence, or without clarity about who is responsible for acting when conditions change.

A digital twin that does not explicitly support governance and accountability will struggle to deliver lasting value in this context.

The Journey Question. Where Are You Starting From?

When organisations ask whether they are “ready” for a digital twin, the conversation often defaults to maturity models, platform selection, or data inventories.

A more useful question is simpler and more practical.

Which decisions in your organisation are currently hard to make well?

These may include decisions about asset performance, service levels, climate adaptation, development sequencing, maintenance prioritisation, or infrastructure timing. In many cases, the challenge is not a lack of data, but uncertainty about thresholds, trade-offs, or consequences.

This question defines the starting point. It grounds the digital twin in a real decision context rather than an abstract future state.

Big Bang Programs Rarely Match How Decisions Are Made

Large, all-encompassing digital twin programs often fail for predictable reasons. They attempt to model entire systems before there is agreement on which decisions matter most, who owns them, or how they will change in response to new information.

This approach increases complexity early, raises expectations, and delays the delivery of visible value. It can also create a disconnect between technical teams building the twin and decision-makers expected to use it.

In contrast, an outcome-led approach starts small by design.

It focuses on a specific decision, risk, or operational challenge. It applies to a defined asset, service, or precinct. It uses a limited set of data sources that are sufficient to inform that decision. It establishes clear thresholds for action and escalation.

From there, the digital twin can expand as decision capability matures.

Progress Is a Governance Outcome, Not a Technical One

Organisations often measure digital twin progress by scale or sophistication. The size of the model. The number of integrated systems. The quality of the visual interface.

In public sector contexts, these are weak indicators of success.

More meaningful signals include fewer late surprises, earlier identification of risk, clearer accountability when conditions change, and more defensible decisions under scrutiny.

A modest digital twin that consistently supports better decisions can deliver more public value than a large platform that sits alongside existing processes without changing them.

Sustainability and Resilience Are Design Constraints

Digital twins are often promoted for their ability to optimise resources and reduce waste. While this is valuable, it understates their potential role in public sector governance.

For governments, sustainability and resilience are not optimisation goals. They are constraints that shape what decisions are acceptable in the first place.

Digital twins are most effective when these constraints are embedded in the system's rules, thresholds, and operating logic. This shifts sustainability and resilience from reporting outcomes to governing conditions.

Starting Small Without Thinking Small

Starting with a targeted outcome is not a lack of ambition. It is an acknowledgement of how public sector systems actually change.

Capability builds through use. Trust builds through results. Scale follows clarity.

Digital twins deliver the greatest value when they are introduced early enough to influence irreversible commitments, narrow enough to remain governable, and clear enough to change how decisions are made.

A closing question for practitioners

Which decisions in your organisation would benefit most from being tested before they become irreversible?