The Leadership Reality

Senior leaders are operating in an environment of increasing complexity across assets, supply chains, and infrastructure, while being expected to deliver measurable progress against commercial, operational, and sustainability objectives simultaneously.

Operationally, we are firmly in the data age. Access to information and analytical capability has never been greater, and insight can be drawn from almost every part of the organisation. However, the reality of complex systems, interconnected infrastructure, and fragmented data sources means there is still significant room for subjective interpretation. As a result, translating data into intelligence, and intelligence into strategic direction, remains a real challenge for even the most tenured leadership teams.

Wouldn’t it be powerful if there were a way to bring fragmented and siloed data across the organisation into a single, intelligent environment, with the ability to analyse performance, simulate scenarios, and test decisions before they are made? An approach that moves beyond reporting what has already happened, and instead supports leaders in making the right decision the first time, often advancing commercial, operational, and sustainability objectives simultaneously... a Digital Twin.

What is a Digital Twin?

A Digital Twin is a digital replica of a physical object, person, system, or process, contextualized in a digital version of its environment.

The concept of Digital Twins has existed for some time, but recent advances in data science, machine learning, artificial intelligence, and physics based simulation engines have fundamentally changed what is now possible. These capabilities have moved Digital Twins from theoretical models into practical, operational tools that can be deployed at scale to support better decision making and sustained advantage.

Digital Twins in practice

Looking specifically at our core market focus across the LHi Group, the use cases for this technology are particularly compelling.

In Data Centres, Digital Twins are used to optimise energy consumption, cooling efficiency, and capacity planning, addressing one of the most significant cost and sustainability pressures in the sector.Across the Built Environment, Digital Twins support lifecycle management, energy efficiency, and resilience, helping owners and operators make better decisions across design, construction, and long term operation.

In Renewable Energy, they support performance forecasting and asset optimisation across variable conditions, helping reduce downtime and improve yield.

In Mining, Digital Twins enable safer, more efficient operations by modelling complex environments, equipment performance, and production scenarios across the full lifecycle, from exploration through to decommissioning, before changes are made.

In Biopharmaceutical Manufacturing, they are applied to process consistency, scale up, and quality assurance, where minor deviations can have major cost and compliance implications.

The real barriers to Digital Twin value

While the potential of Digital Twins is clear, it is also worth recognising why many initiatives struggle to deliver the intended value. They can be seen as complex, costly, or difficult to adopt, and too often remain stuck at pilot stage. In most cases, this is not because the technology falls short, but because it is treated as a visual or analytical exercise rather than something that actively supports executive and strategic decision making. Without clear ownership and a direct link to how executive teams and boards make informed decisions, even well built Digital Twins can fail to move the needle.

The data challenge behind Digital Twins

At the heart of this challenge is the type of data Digital Twins work with. Unlike traditional analytics or static models, which tend to look at a system at a single point in time, Digital Twins capture how systems behave across both space and time. This makes it possible to see how different components interact, how conditions change, and how the effects of decisions unfold over time. The value therefore comes not from having more data, but from turning that data into foresight, allowing leaders to test scenarios, understand trade offs, and anticipate outcomes before resources are committed or decisions become difficult to reverse.

One of the most underestimated challenges in scaling Digital Twins is the data burden. The question is not simply what data you can capture, but how much you ingest, how often you ingest it, and how much of it is actually new. Many organisations default to full refresh cycles, repeatedly ingesting and storing entire datasets to keep the twin current. This can work at pilot stage, but at enterprise scale it quickly becomes an infrastructure and cost issue, driving unnecessary cloud compute and storage growth. It can also increase security pressure over time, as more data sources, integrations, and stored information expand the overall risk surface. The most effective Digital Twins are designed to track and manage change intelligently, ensuring the platform remains operationally sustainable and continues to deliver value rather than becoming an expensive data replication exercise.

Digital Twin excellence

Ultimately, the organisations that will obtain the greatest value from Digital Twins are those that focus on using data intentionally and with clear ownership. When this is done well, Digital Twins become a practical tool for reducing uncertainty, improving execution, and supporting more informed decision making at executive and board level, helping organisations make sustained progress across commercial, operational, and sustainability objectives.

How XPS supports delivery & adoption

At XPS, we work with clients to explore and adopt Digital Twins in a practical, outcome focused way, helping them transform how their operations are run and governed. By grounding the technology in real decision making and execution, we support organisations in turning complexity into clarity and gaining a sustained competitive advantage within their markets.

Reach out to Scott Cook or Bill Bentaieb to explore this technology further.