Why Seeing the Current State of the Business Is No Longer Enough
For many years, digital transformation has focused on one main task — increasing business transparency. Companies implemented ERP systems. Created corporate data warehouses. Developed analytics platforms. Built dashboards. Deployed monitoring systems.
As a result, organisations learned to understand their own activities much better. But as business complexity grew, it became clear that observability alone was no longer sufficient.
Even if a leader sees what is happening in real time, many questions remain.
- What will happen in a month?
- What consequences will a new decision cause?
- How will the workload on departments change?
- What risks will appear when processes change?
- How will a new project affect existing resources?
Traditional information systems are rarely able to answer such questions. They show reality well. But they hardly help explore possible futures.
That is why the concept of an organisational digital twin is attracting more and more attention.
What Is an Organisational Digital Twin
An organisational digital twin is a dynamic digital model of a company that reflects its processes, resources, constraints, events, and current state as closely as possible to real activity.
It is important to understand what a digital twin is not.
- It is not a process diagram.
- Not an organisational structure chart.
- Not an ERP system.
- Not a set of reports.
- Not a consultant‘s presentation.
A digital twin is a living digital model of the business. It evolves with the organisation. Receives data from the operational environment. Reflects ongoing changes. Allows analysis of development scenarios. In essence, the digital twin becomes a virtual version of the company.
How the Idea of Digital Twins Emerged
Initially, the concept of a digital twin originated in engineering. The first digital twins were used to model physical objects: engines, aircraft, production equipment. Engineers created a digital copy of an object and used it to analyse its state and predict behaviour.
Over time, computing power began to grow. New data sources appeared. Integration technologies developed. The idea of the digital twin began to expand.
- First came digital twins of production lines.
- Then of entire factories.
- After that, logistics networks.
- The next logical step was modelling the organisation as a whole.
Thus, the concept of the Digital Twin Organisation was born.
Why Digital Twins Are Becoming Possible Right Now
For many years, the idea of an organisational digital twin remained largely theoretical. The reason was simple. There were no technologies capable of providing the necessary level of observability and computation.
Today, the situation has changed. Several technological areas have simultaneously reached sufficient maturity.
Cloud Platforms
Modern cloud solutions allow storing and processing huge volumes of information.
Integration Technologies
Organisations have gained the ability to combine data from many systems.
Process Intelligence
It has become possible to understand real processes, not just formal regulations.
Event‑Driven Architecture
Systems have learned to react to events almost in real time.
Artificial Intelligence
AI allows analysing complex interconnections and building forecasts.
Availability of Computing Resources
What ten years ago required multimillion‑dollar investments has now become accessible to a much wider range of companies.
It is the combination of these factors that makes digital twins a practical management tool.
What Does an Organisational Digital Twin Consist Of
Despite differences between companies, most digital twins include several mandatory components.
Process Layer
Processes are the foundation of any organisation. This is where value is created. The process layer reflects the real routes of work execution. Not the regulation. Not the description. But the actual behaviour of the system.
Resource Layer
An organisation consists of resources: people, equipment, finances, technology, information. The digital twin must understand the availability and utilisation of these resources.
Event Layer
Any organisation is a flow of events. An order appears. A contract is signed. A problem arises. Demand changes. A project is launched. Events reflect the dynamics of the business.
Information Layer
Data connects all elements of the model. This is where indicators, documents, reference data, and operational information reside.
Management Layer
This level describes decision‑making rules: priorities, constraints, permissible courses of action.
Simulation Layer
This is what distinguishes a digital twin from most traditional systems. Here, the organisation can explore alternative futures.
How a Digital Twin Differs from ERP
In practice, these concepts are often confused. But their purposes are fundamentally different.
- ERP records operations. The digital twin models the organisation.
- ERP stores transactions. The digital twin explores interconnections.
- ERP answers: what happened? The digital twin answers: what will happen if we change the situation?
ERP is an accounting system. The digital twin becomes a system of understanding and forecasting. Therefore, the digital twin does not replace ERP. It uses information from ERP as one of its data sources.
How a Digital Twin Differs from BI
The similarity between digital twins and analytics platforms can also be misleading.
- BI helps analyse the past and present. The digital twin helps explore the future.
- BI shows metrics. The digital twin shows the consequences of decisions.
- BI answers: why did it happen? The digital twin answers: what will happen next?
That is why digital twins are becoming the next stage in the evolution of corporate analytics.
Why Process Intelligence Is a Mandatory Element
One of the most common reasons for digital twin project failures is the lack of understanding of real processes. Many organisations try to model their business based on regulations.
But real processes almost always differ from formal descriptions. Workarounds arise. Exceptions. Informal practices. Local optimisations.
Therefore, a digital twin must be built on the basis of the organisation‘s actual behaviour. Process Intelligence provides this capability.
Control Tower and the Digital Twin
In the previous article, we discussed the concept of the Control Tower. These approaches complement each other.
The Control Tower is responsible for observation. It shows the current state of the business. The digital twin is responsible for modelling. It helps explore possible development scenarios.
One could say that the Control Tower helps understand the present. The digital twin helps understand the future.
Executive Copilot and the Digital Twin
Particularly interesting opportunities arise when combining the digital twin with an Executive Copilot. Imagine a situation. The CEO asks a question:
- What will happen if we open a new warehouse in the Central Federal District?
- Or: how will the team‘s workload change when launching a new product?
- Or: what consequences will a twenty percent increase in sales volume cause?
The Executive Copilot can consult the digital twin and generate a forecast based on the current model of the organisation. As a result, the leader receives not a guess, but a substantiated scenario analysis.
What Problems Does an Organisational Digital Twin Solve
The practical value of a digital twin is evident in various management scenarios.
Resource Planning
Assessment of needs for personnel, equipment, financing.
Bottleneck Identification
Identifying processes that limit the organisation‘s development.
Process Optimisation
Modelling alternative ways of performing work.
Risk Analysis
Assessing the consequences of various negative scenarios.
Project Management
Forecasting the impact of projects on current activities.
Organisational Changes
Assessing the impact of a new management structure.
Investment Decisions
Testing hypotheses before actually committing resources.
Scenario Modelling as a New Management Standard
Historically, most management decisions were made based on experience, intuition, and a limited set of data. The digital twin makes it possible to move to a different approach.
Before implementing a decision, the organisation can test various development scenarios. For example:
- What will happen if the number of employees is increased?
- How will order fulfilment times change?
- What consequences will process automation cause?
- How will business profitability change?
Such practice significantly reduces the cost of errors. In effect, the company gains the ability to experiment without risking the real operational environment.
Why Many Digital Twin Projects Fail
Despite the high potential of the technology, not all projects are successful. The most common reasons are as follows.
Static Model Instead of a Living System
A beautiful description of the business is created, but it quickly becomes outdated.
Lack of Data
The model does not receive up‑to‑date information.
Ignoring Processes
Focus is placed on the organisational structure instead of real work execution.
Lack of Event‑Driven Architecture
The system cannot reflect changes in real time.
Excessive Complexity
The organisation tries to model everything at once. As a result, the project becomes too expensive and difficult to manage.
How to Start Creating an Organisational Digital Twin
Practice shows that a phased approach is most successful.
- First step — understanding processes.
- Second — creating observability.
- Third — building an event‑driven model.
- Fourth — integrating data.
- Fifth — implementing analytics.
- Sixth — launching scenario modelling.
This path allows you to gradually increase the organisation‘s maturity and obtain business results at each stage.
The Digital Twin and the Future of Management
Looking at the overall development of corporate systems, an important trend becomes noticeable.
- Organisations are gradually moving from recording information to understanding system behaviour.
- From understanding to forecasting.
- From forecasting to modelling decisions.
The digital twin becomes an important element of this evolution. It creates the foundation for:
- decision support systems;
- Executive Copilot;
- corporate artificial intelligence;
- multi‑agent systems;
- intelligent operating platforms.
In effect, the digital twin becomes a digital representation of the organisation as a manageable system.
Conclusion
For many years, corporate information systems have helped companies better understand the past and present. But the modern environment requires new capabilities.
Organisations need not only to see what is happening. They need to understand the consequences of future decisions before they are implemented.
This is precisely the problem that the organisational digital twin solves. It combines data, processes, events, resources, and management rules into a single dynamic model of the business.
It allows you to explore scenarios. Reduce risks. Test hypotheses. Improve management quality.
In the coming years, the ability to model the consequences of decisions will become one of the key competitive advantages of companies.
Just as ERP changed accounting and BI changed analytics, digital twins are gradually changing the very approach to managing an organisation. From managing facts to managing future possibilities.