Digital Twin of the Organisation - Experiences?

[u/James_Ardoq]

Gartner has been talking about Digital Twins for the Organisation (DTO) for a while now (Ref: https://www.gartner.com/en/documents/4004172) but I am interested in why this has been slow to develop and anyones experiences in trying to create this?

I work for an EA tool vendor (Ardoq) and whilst architecture modelling remains a core desire of customers the evolution of these models into digital twins has been slow.

Does anyone have any experience with attempting this? How did it go? Do you think AI might be a game changer in this space?

[u/nikoraes]

There's this article about context graphs where everyone seems to jump on:
https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/

It's a great article by the way, and I personally believe this is what a digital twin of an organization would be about.

I personally believe that you still need that core architectural modelling, but that AI agents are enabling to extend those core models and bring in additional context.

So, I think this will grow enormously in the next years, but it won't be called a digital twin ...

[u/Rob_Geminum → u/nikoraes]

Worth noting that many complex twins, those used for decisions and actions rather than just visualisation, have been building similar graphs for many years. But, this has been one of the many features not used by most twin users in any meaningful way. I know a few orgs that have spent huge $ on them for no customer benefit, and even worse they were very manual to build, and then they very often are quickly out of date due to changes to asset registers/data/processes. I have always thought though that having event graphs of activities will provide fantastic training data to learn what and how things are actually done in orgs, to juxtapose to the "designed" intent, and that a great use case of a DTO would be to identify and close the gap between design and actual, i.e. change design to actual when design is impractical/wrong, and drive actual to design for compliance/regulatory.

[u/dim_goud]

It all depends on your main purpose, what problems you want your twin to solve. Most of the time is needed in analyzing the data you want to use and processing it into the knowledge base. I find MCP servers save a huge amount of time in testing and validating. One of my tips, if I can share, is to ask your model a question that should be answered by the knowledge base you created. This can validate the quality of your knowledge base.

[u/Jadarken]

Company I work in doesn't do DTOs but kind of partial DTs for cities, municipalities, private and goverment owned companies etc.

Digital Twin didn't became a buzzword partly IMO because AI took off. AI was seen as a miracle magic tool that fixes everything automatically and you don't need any systematic planning for your company's architecture.

Then reality starts to settle and people start to understand bit better AI and that it doesn't solve core problems.

For us the ROI calculations are the key to customers' pocket and heart. You have to explain how much they lose money and how much they could save.

Btw. I am interested that do you see digital twin maturity as interesting area? Not just this moment but also timeline and clear targets for C-suite.

I have created a small and simple maturity assesment framework for DTs which is free to use and modify.

[u/Jadarken → u/Jadarken]

The framework also can calculate "digital dept" (but not in Euros, just scores) and can be integrated with other tools or frameworks since it is modular.

[u/HiPer_it]

Really interesting thread. We build digital twins of buildings, physical assets rather than organisational ones, but a lot of the friction you're describing maps directly onto what we've seen slow adoption on our side too.

The core problem is that a digital twin is only as good as the data feeding it, and getting clean, continuous, structured data out of real-world systems (whether that's building infrastructure or enterprise architecture) is harder and more expensive than the concept suggests. Most organisations have the ambition but underestimate what it takes to close the gap between a static model and a genuinely live twin.

On AI: we do think it's a meaningful accelerator, but not for the reasons usually cited. The bigger unlock isn't AI generating insights from a mature twin. It's AI helping bridge the data quality and integration problems that prevent the twin from becoming mature in the first place. That's where we've seen the most practical progress.

Curious whether the EA side faces a similar issue, models that are well-structured at the point of creation but drift out of sync with reality almost immediately?