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Digital twins have evolved far beyond the conceptual promise that captivated technologists a decade ago. Today, ‘twinning’ represents something more profound: a fundamental shift in how organisations convert uncertainty into informed action.
Digital Twins: a nutshell definition
A digital twin is a virtual representation of a physical object or system. It uses systems of record data, together with sophisticated models, to help mirror its real-world counterpart’s behaviour and performance.
Digital twins enable ‘what-if’ scenario testing, predict potential failures before they occur, and optimise operations – all without affecting physical assets. This creates a continuous feedback loop where insights inform actions and drive performance improvement.
Applications span manufacturing, aerospace, healthcare, smart cities, and energy sectors. Organisations achieve predictive maintenance, reduced downtime, design innovation, and strategic decision-making with unprecedented confidence.
Consider two contrasting deployments – one geared towards saving lives; the other representing hundreds of £millions in value creation – approached from opposite ends of the strategic spectrum.
National Threat. Faced with an accelerating pandemic, in 2020, the UK government commissioned a Contact Tracing app of unprecedented scope. The national priority was to create a virtual representation of the population and their potential viral interactions. Built under extraordinary pressure – in just six weeks with a further six weeks of testing – the NHS COVID-19 app became, almost by default, a real-time prediction engine. It was capable of forecasting infection rates two-to-three days ahead of testing results.
Initially, the team expected to model just a few basic rules. But as the complexity of human behaviour and transmission rates became clear, the challenges went viral. The team had to account for over a thousand different scenarios – from tracking exposure and simulating policy outcomes to enabling decision-makers to understand the consequences of interventions before implementing them.
The cost of failure? Deadly in human terms, devastating in economic ones.
High-Flying Priorities. In the aerospace and aviation sectors, a different story has been unfolding over the last fifteen years…
Rolls-Royce Defence has employed digital twin technology to orchestrate decisions spanning decades – from multi-year service bids requiring nine-figure investments to real-time optimisation of maritime fleets. Without any overstatement, digital twinning is now critical to UK defence.
It is equally vital within the wider aviation industry. For example, easyJet uses the same underlying approach to achieve a significant £multi-million saving in its annual maintenance budgets. With a fleet of over 350 different aircraft, the impact on cost and operational efficiency has been profound. Make no mistake, these are not experimental projects; they are embedded business advantages.
Most organisations still rely on Excel and Microsoft Project for forecasting and planning. These tools served admirably for decades when variables were fewer and planning horizons shorter. But, when simultaneously optimising asset utilisation, managing sustainability commitments, planning workforce allocation, and anticipating regulatory changes across a five-year horizon, suddenly the spreadsheet becomes a liability masquerading as diligence.
The gap between what your planning tools can model and what your business actually faces is where strategic advantage either surfaces or sinks without trace.
The choice between custom development and enterprise platforms is less binary than it appears. What matters is matching the approach to your strategic context.
Custom development offers precision tailored solutions to unique challenges. This is essential when existing solutions don’t address your specific domain… when time sensitivity demands rapid deployment… or when competitive advantage lies in proprietary modelling. The NHS couldn’t wait for an off-the-shelf pandemic response system.
But custom development comes at a price. It demands significant technical expertise, carries ongoing maintenance obligations, and requires constant iteration as understanding deepens.
OTS platforms offer years of refinement, proven scalability, and accumulated domain intelligence. For organisations looking for complex asset optimisation across extended time horizons – typically aerospace maintenance, fleet management, and long-cycle manufacturing – these systems offer mature capabilities that would take years to develop internally. The classic trade-off is adaptation rather than pure customisation – mind you, modern platforms increasingly offer configuration flexibility that bridges this gap.
The real question isn’t which path is ‘better’ but which aligns ‘best’ with your organisation’s strategic priorities, capabilities, and competitive context.
Digital Twins challenge conventional project thinking. They demand a new mindset: you won’t get the granularity right from the outset… embrace this reality.
The NHS team initially estimated a few basic rules. They ended up building more than a thousand scenarios. But this isn’t failure; it’s discovery. Digital twins excel precisely because they surface hidden complexity rather than obscuring it. They reveal interdependencies that planning assumptions miss. They expose bottlenecks that only become visible when the entire system plays forward through time.
This demands a different relationship with technology investment. One that embraces iteration, expects refinement, and measures success not by initial accuracy but by accelerating insight.
Generative AI introduces a compelling new dynamic to digital twin capability. Where traditional modelling requires explicit rule definition, AI can navigate ambiguity, adapt to incomplete data, and identify patterns humans might overlook.
However, this power must be deployed with precision.
Certain domains – such as regulatory compliance, safety-critical decisions, and policy adherence – demand absolute determinism. Transparent, model-based approaches are essential. Users must be able to see scenarios unfolding, identify causal relationships, and trust the basis for recommendations. Black-box AI, however sophisticated, cannot replace this transparency in high-consequence decisions.
Consequently, the future lies not in choosing between model-based rigour and AI-enabled adaptability. Instead, it hinges on adopting and applying both choices within a carefully balanced systems architecture.
If you are responsible for delivering complex, time-sensitive, business-critical initiatives, three questions deserve – and demand – your immediate attention:
The technology has matured – but has your decision-making approach kept pace? The most sophisticated organisations have moved beyond asking whether digital twins offer value. They are asking which decisions benefit most from this capability… how do they integrate insights into governance processes and build organisational muscle around scenario-based, strategic thinking?
The gap between those who can rehearse the future and those who cannot is widening. Strategic certainty increasingly belongs to those who refuse to accept uncertainty as unchangeable.
Our special thanks to ‘Mo’Ramezanpoor of Zühlke and Malcolm Beresford of Aerogilityfor their inspirational contribution to this article. If you would like to discuss any of the thoughts and messages in this article, they would be very happy to talk. Simply contact: innovation@clustre.net
