Digital twins began as virtual counterparts to physical systems. In aerospace, manufacturing, and other high-stakes environments, they gave engineers a safe way to observe behaviour, test scenarios, and reduce risk before making real-world changes. That framing now feels incomplete.
Digital twins began as virtual counterparts to physical systems. In aerospace, manufacturing, and other high-stakes environments, they gave engineers a safe way to observe behaviour, test scenarios, and reduce risk before making real-world changes. That framing now feels incomplete.
To that point, a 2024 survey of C-suite executives by Hexagon found nearly two-thirds (62%) get immense value from digital twins. The bottleneck has shifted from whether to deploy them to how to make them trustworthy enough for autonomy. That shift matters because the moment a twin becomes a place where software makes recommendations, tests strategies, or guides action, the technical center of gravity changes. Visualization still matters. Simulation still matters. The harder problem sits underneath. A serious digital twin has to hold together current state, historical state, relationships, constraints, external inputs, and, increasingly, the context and reasoning that underpins all this. For developers and architects, that makes the data backbone far more consequential than it has ever been.
AI agents quickly sharpen that problem. A human operator can tolerate a degree of architectural mess. While comparing dashboards, we can skim logs, open a maintenance note, all the while remembering that one source tends to lag — and mentally stitch the rest together.
An AI agent, operating within a twin, is far less forgiving. If it is trying to understand why a cooling loop is underperforming, or whether a maintenance intervention should be delayed until after a production peak, the layers of data needed to make that decision are huge. Live sensor data, equipment dependencies, work-order history, and a lot more — all in the same decision path. Some of that information is relational. Some is document-shaped. Some is graph-shaped. Some is temporal. Some may arrive as embeddings or unstructured operator notes. If that context is fragmented across too many stores and too many APIs, the application layer ends up rebuilding meaning every time the agent asks a serious question.
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: InfoWorld
- Published: 27/08/2026
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Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.
What to watch
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
How I read this entry
If I had to bring this into an organisation, I would start with a boring, measurable process — reconciliation, first-line support, document review — and get it running with a metric before touching anything flashy. The credibility for big projects is bought with a small result nobody can argue with.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
