The company’s digital twin
In industry, digital twins are no longer merely a vision of the future. They represent machines, plant, buildings, networks and supply chains. They help to plan maintenance intervals, predict faults and optimise processes.
A Corporate Twin, on the other hand, models the organisation as a whole. It links data from processes, IT systems, value chains, personnel structures, customer interactions, financial data, compliance requirements, co-determination, security architectures and external factors, thereby creating a complex web of interdependencies.
What happens when a company reorganises a department? What are the consequences of a new cloud strategy? How does AI alter staffing requirements in a core process? What risks arise if a critical service provider fails? What knock-on effects does a cost-cutting measure have on resilience, quality and customer satisfaction?
Today, such questions are often answered based on experience, reports, workshops and gut instinct. For a long time, that was enough – because it had to be. But it is no longer sufficient for the next level of complexity.
Simulation does not replace judgement, but it improves it
The Corporate Twin won’t simply spit out the one ‘right’ decision like a ticket machine with an MBA. However, it can reveal interdependencies that get lost in traditional planning models.
It can compare scenarios, reveal interdependencies and show where a decision has an impact. Thanks to computing power, AI, data platforms and simulation, this will become more realistic in future. What was previously possible mainly for physical assets is increasingly becoming feasible for organisational systems. Companies will then be able to ask not just: ‘What has happened?’ or ‘What is happening right now?’, but: ‘What might happen if we decide this way?’
This fundamentally changes leadership. It makes it transparent and traceable, and places decisions on a solid and transparent footing.
If you want to run simulations, you need to know your organisation
The real prerequisite, however, is an uncomfortable one: anyone wishing to build a digital twin must know their organisation. And not just as an organisational chart, but in as many facets as possible.
A corporate twin requires a data set that is as comprehensive as possible. It requires an understanding of processes, shadow processes and interdependencies, as well as all the human elements that turn an organisation into a living organism: sensitivities, habits, likes, dislikes and vanities.
It is like climate models: the more relevant information is fed in and the higher the resolution, the more accurate the forecasts become. Those who know only rough averages will get rough answers. However, those who measure their organisation precisely can simulate more accurately and thus manage it more effectively.
Making data, processes and interdependencies visible
Organisations need support in analysing their data, processes and interdependencies. They need methods to visualise complex IT and process landscapes. They need data strategies, architectural expertise, AI know-how and a realistic understanding of what can be usefully modelled.
The first Corporate Twin does not need to map the entire organisation straight away. It can start with a critical process: a supply chain, an approval procedure, a customer service function, an IT architecture, a KRITIS scenario, or a department or pilot team. The key is for organisations to start making their reality modelable.
Conclusion: Stop flying blind
The Corporate Twin does not replace responsibility. On the contrary: it makes responsibility more tangible.
Companies should start now to map out their processes, data flows, system dependencies and critical decision-making chains, thereby laying the foundations for better decisions.
Those who understand their organisation today can simulate it tomorrow. Those who can simulate it can identify risks earlier. And those who identify risks earlier no longer have to fly blind.
