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A Digital Twin World  – Understood by All

Do companies understand where they want to be?
Do they have a clear understanding of where they have been, and where they are?

Too much birthday cake during the weekend, and well past midnight here in Gothenburg Sweden. I have been thinking lately that if given enough time, people will surely solve all the problems that are thrown at us. But that is the thing. I do not believe that we have enough time, seen from a planetary perspective. We need to make better decisions faster, and try to go beyond buildings, and learn from other industries.

Tomorrow I have a kick-off with Veoneer and Ericsson regarding Digital Twin enablement with one of the companies I work with regarding practical Digital Twin strategies called SEKAI. I helped them get into a mobility cluster here in Sweden called Mobility X-lab where big automotive giants, telecommunication behemoths, and small nimble companies try to get smarter together. 

And all the 40+ companies that were selected for innovation prototyping are mostly trying to solve problems in isolation. But in the real world, these systems need to be adapted to large organizations that have thousands of experts, systems, agendas, where innovation needs to compete with existing offerings, and industry dynamics that eat up innovation for breakfast. The thousand cuts problem is real. 

  • Do companies understand where they want to be?
  • Possibly. 
  • Do they have a clear understanding of where they have been, and where they are?
  • One could only hope. 

And it is not the access to new technology or even the adoption of new technology for these companies that is hard. It is how they can leverage them with the people they have, processes, systems, culture, roles and of course being stuck in existing industry dynamics with suppliers, buyers, and everything in between. Most companies in the building sector might think that the grass is more digital on the other side of the fence. They might be right, but also wrong.

I discussed the topic of how to leverage interoperable Digital Twins in Manufacturing at a webinar the other week for the Digital Twin Consortium where the industry wants to be able to leverage modern tools that are open and interoperable. But they are also for the most part stuck with data and information that 

And the thing is that these POCs are never about technology. They are always about finding the right people and to see how change can be made in organizations that are made for anything but change. But the thing is just that. They need to change. Their customers demand that of them. The world demands it, and the future workforce as well as AI initiatives at scale.


But how can they make complex things understood by all? Can they? Is it possible to be understood by all or is that a dream that innovation strategists dream?

Back to Basics with completely understandable interoperability

What does interoperability really mean? Is it how well we can understand something? Is it for systems only? Or do we have other ways of interoperability as well?


These were the questions I asked myself the other week when preparing the webinar. And based on prior experience I knew that it was everything but simple. But then I found the sentence above which included “interfaces which are completely understood”. It referred mostly to systems, but being a fan of the ANT-theory (Actor-Network Theory) for the last decade I usually try to put people, systems, AI, in the same network, knowing that the reality exists with all of us at the same place. 

And this got me thinking. 

How can we make AI understood by humans? How can we make it understood by systems? And how can we make anything, and everything understood by all? Is this even possible?

I think so. I want to believe it to be true since we are in a people world. But we need to treat even the most mysterious buzzworthy technologies with a healthy dose of curiosity, as well as skepticism. But perhaps even more so, pragmatism. After all, the one thing we are best at, is using tools to reach an intended outcome. Can we try to strive for systems that are interoperable, robust, useful and attractive. Not only to people, or other systems, but also to that of AI/ML approaches. 

What would that look like? 

The road towards Agent Based Models and Multi Agent Systems

Today humans make the most decisions, where we are sometimes aided by advanced tools. Well, not just sometimes, all the time I would say. But when and where do people stop making decisions and where do we go towards decision making where advanced tools to the job?

And if we aim for agent-based models, and multi-agent systems to run simulations based on a reality that is being emulated through Digital Twins, what should be the agents?

Maybe it is that simple. To first state that we need to transfer knowledge seamlessly between systems, people, standards, and AI. And then identify these actors and agents, model them in ways that make sense for mathematical approaches, and also for other systems, and people in a transparent way. And then we can just feed the models with agents from the weather, the lighting, and also adhere to risk, regulation, sustainability, standards in the industry context. And then just run optimal configurations based on the outcome we are looking for. Where the systems will crash into each other and find the optimal course of

action themselves, mimicking any and all outcomes based on a reality that has been emulated.


Is this completely understood by all yet? No. But with Digital Twins it can be. Think about it. Anything that you want to feed the Digital Twin, could become a part of a wider data fabric. Not limited by space and time, and where these fragmented parallel approaches could run their separate digital threads, only to be woven together towards an enterprise digital fabric that could solve in-channel analytics problems as well as across channel analytics based on physics-based modeling.

Too much?

Most likely. It’s 1.30 AM here.  


We need to make smarter and better decisions faster. We need to use the most modern tools that we can find to solve the hardest problems we have. And that is not limited to just collaboration between different end-points but also to that of collaboration between people. And we need to cater to AI initiatives, knowing that AI/ML approaches are seen as the best sticks of our time. 

And we need to make these initiatives understood by one another at great scale so that we do not keep re-inventing the wheel but instead realise that we are all on this planet whether we like it or not. We need to go above and beyond what we have done before to reach the stage of a world that is understood by all. 

Going above and beyond

If extreme weather, climate crisis is here. And if the ingredients exist, how do we create and share the recipes? How do we move towards systemic change? And what factors should modern systems cater to? People? Other systems? AI? And where do companies start? Do they start with adding new sensors? Or making sense of the old? And how can we learn from industrial ways of working and share knowledge freely between people as well as systems?

If you or someone you know need help with questions regarding strategy, innovation, and figuring out how modern technologies can help you where you are today. Look no further. WINNIIO will always be by your side. Just reach out to me, Nicolas Waern, on LinkedIn or check out my Podcast Beyond Buildings if you need any assistance.

Nicolas Waern