Better companiesTipping Points

Tuesday, 24 March 2026

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The Power of Two

In a world where technology evolves at breakneck speed, digital twins are emerging as catalysts for performance and as levers for responsible innovation aligned with societal needs.
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A Dynamic Engine of Innovation 

A digital twin is a virtual replica of an object, process, or environment, updated in real time using sensors, data transmission and processing technologies, and models (AI-based or not). 
In the automotive industry, for example, a digital twin can precisely replicate a welding robot, an assembly line, or even an entire factory. 
This dynamic model allows for analysis, simulation, optimization, and even prediction of the real counterpart’s behavior, providing a safe, low-cost, and agile experimentation environment. 
Born in aerospace and developed in industry, digital twins are now spreading to sectors as diverse as healthcare, smart cities, energy, and agriculture. 
Beyond operational performance, this disruptive innovation can serve as a lever for responsible innovation across these industries. 

 

Healthcare: Personalizing Care, Ensuring Safety 

In medicine and healthcare, a digital twin can represent an organ, a patient, or an entire care pathway. It enables predicting disease progression, testing personalized treatments, and optimizing hospital operations. The result: more humane medicine, reduced risks, and controlled costs. 
By adhering to strict ethical and privacy protocols, digital twins positively contribute to enhancing care quality while safeguarding patient rights. 

 

Cities: Managing Sustainable Infrastructure 

In digitally-enabled “smart cities,” digital twins simulate traffic, water networks, the environmental footprint of new infrastructure, and climate risks. They facilitate public service planning, reduce nuisances, and strengthen urban resilience. 
When used responsibly, they promote rational resource management and urban development focused on citizens’ well-being rather than purely technical performance. 

 

Energy: Accelerating the Transition 

In the energy sector, digital twins model networks, manage decentralized production, and anticipate facility maintenance. They enable higher integration of renewable energy, reduce losses, and secure infrastructure. 
This approach supports a more efficient, reliable, and economically responsible energy transition. 

 

Agriculture: Producing Better, Preserving Resources 

In agriculture, digital twins represent a field, a farm, or a crop cycle. They optimize water and input use, forecast disease outbreaks, and test sustainable practices. 
They help reduce environmental impact, improve yields, and stabilize farmers’ income, all while preserving soil health and biodiversity. 

 

A Strategic Tool for a Sustainable World 

By combining smart sensors, big data, and predictive models, digital twins position themselves as strategic tools for a more sustainable world. 
They are more than a technological marvel, they are among the most powerful innovations for reducing environmental impact, preventing risks, accelerating human learning, and improving quality of life. 
For example, by simulating before producing, experimenting without consuming materials, or adjusting without destroying, digital twins help limit resource use and reduce the environmental footprint of economic and human activity. 
More broadly, they make it possible to test crisis scenarios (climate, energy, health), manage complex systems (power grids, cities, global supply chains), and simulate the impact of public policies or regulations. In this way, digital twins emerge as innovations serving greater collective resilience. 

Key numbers

  • Smart city solutions based on digital twins are projected to reach $5.2 billion by 2028. (Source)

  • 57 % of organizations report that sustainability is among the main reasons for investing in digital twins. (Source)

  • By implementing a digital twin across 37 stores in Asia, Ikea reduced energy consumption for heating, ventilation, and air conditioning systems by 30 %. (Source)

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