Clove Technologies at Munich CXO Summit | Digital Twins & AI

Every AEC and infrastructure organisation investing in Digital Twins, GIS, and connected assets eventually faces the same question: not “did we build it,” but “can we still trust it?”

At the Munich CXO Summit, Clove Technologies Sales Director T.S. Varma addressed this directly on a panel about Space, Geospatial, and Digital Twins for AEC and Smart City Management. His core point: the real risk isn’t a missing Digital Twin; it’s a Digital Twin that quietly stops matching reality.

 

A Digital Twin is a snapshot. Physical assets are not.

Buildings get modified. Infrastructure gets upgraded. Equipment gets replaced. Cities evolve daily. If the digital model doesn’t keep pace, you’re not managing risk with better data; you’re managing risk with outdated data that looks sophisticated.

That gap between what the model says and what’s actually on the ground is a decision-quality problem, not a technology problem. It shows up as bad planning assumptions, missed maintenance windows, and slow response when conditions change.

Bottom line: “Do we have a Digital Twin?” is the wrong question. “Does it reflect today?” is the right one.

 

Every organisation is racing to layer AI and predictive analytics on top of their data. Here’s the catch: AI can only be as accurate as the ground truth feeding it.

AI doesn’t know if your model is stale. It will generate confident, polished predictions from outdated information without flagging the problem. So before investing further in AI and analytics, the real question is: how current and trustworthy is our underlying data foundation?

This reframes AI readiness as a data governance issue first, a technology issue second.

 

Varma used urban flooding as the clearest illustration. A city can have dashboards, sensors, and analytics with real sophistication. But if the underlying physical model hasn’t been updated recently, none of that intelligence reflects current drainage, construction, or terrain conditions. The tools look advanced; the decisions they support are guesswork.

The Value Begins Where the Model Stops Being Static 

Phase one of Digital Twin adoption was about creation: capture, model, visualise.

Phase two, where the real ROI lives, is about operating it: monitor, validate, update, and only then analyse and predict.

That shift changes what leadership needs to own. It’s no longer just a technology budget line. It requires:

• Clear ownership: who is accountable for keeping the data current
• Defined update cycles: how often is it re-validated against reality
• Connected data sources: IoT, field capture, and operational systems feeding in continuously
• Governance before AI: confidence in the data before you scale predictive tools on top of it
  

The strategic question isn’t “can we build a Digital Twin?” Most organisations can, and have.

It’s “can we keep it relevant?”

The organisations that get lasting value out of Digital Twins won’t be the ones with the most impressive initial model; they’ll be the ones who treat it as a living system, continuously reconciled against physical reality, so every downstream decision, from maintenance to AI-driven prediction, is built on something true.

At Clove Technologies, we help organisations build that continuity, connecting Reality Capture, Geospatial, BIM and Digital Twin capabilities into a data foundation that stays trustworthy over time, not just accurate on day one.