Here’s the thing about digital twins: operations teams don’t actually want automation. They want a tool that helps them make better decisions faster. The moment a twin starts taking actions without their input, trust evaporates.
A twin that can explain itself is worth ten twins that “just work.”
The trust problem
Every digital twin project starts with ambitious automation goals. “The twin will automatically adjust setpoints!” “It will schedule maintenance without human intervention!”
Then reality hits. The first time the twin makes a wrong call — and it will — the operations team stops trusting it. They’ll work around it, ignore its recommendations, or quietly disable it.
The path back from that is long and painful.
Start with decision support, not automation
The safest path: build a twin that recommends, never acts.
- Show operators what you think should happen
- Explain why (this is non-negotiable)
- Let them approve or override
- Track whether they follow your recommendations
Over time, you build data on accuracy. “The twin’s recommendations were followed 94% of the time, and the 6% overrides were justified.” That’s how you earn the right to automate — with evidence, not assumptions.
What to automate (if anything)
Some things are safe to automate because they’re low-risk and repetitive:
- Data collection and state updates
- Basic validation and alerting
- Report generation
- Routine data transformations
Notice what’s not on that list: anything that changes physical systems.
If you do automate actions, start with the lowest-stakes ones. Adjusting a non-critical parameter within a narrow band. Scheduling a maintenance window (not executing it). Small, reversible, monitored.
Hard rules for production twins
Human approval for anything that matters. Safety, compliance, large costs — all require explicit sign-off. Build this into the platform with audit trails, not side-channel approvals in Slack.
Fail safe, not fail silent. If the twin loses data or produces garbage, operators need to know immediately. The worst failure mode is a twin that looks confident while being wrong.
Model drift is real. Calibrate regularly. Compare predictions to outcomes. If accuracy drops, pause automated actions until you understand why.
Stale data is dangerous data. If your twin relies on data that’s 30 minutes old, it shouldn’t be making real-time decisions. Enforce freshness requirements and fail loudly when they’re violated.
The wind farm example
A wind farm twin mirrors turbine status, power output, wind conditions, and maintenance schedules. Operators can simulate scenarios: “What if we shut down turbine 7 for maintenance tomorrow?”
When a turbine faults, the twin recommends a maintenance window based on forecasted wind (lower revenue impact) and crew availability. But it doesn’t schedule anything automatically. An operator reviews, approves, and the maintenance system takes it from there.
This keeps humans in the loop where it matters while still saving time on analysis.
Where twins go wrong
- Modeling everything instead of focusing on actual decision points
- Automating before earning operator trust
- No explanation for recommendations (“the model said so” isn’t good enough)
- Mixing training data with live data without tracking provenance
- Letting stale data drive decisions under load (when it matters most)
The real test
Can an operator explain why the twin made a recommendation? If yes, you’ve built something useful. If no, you’ve built something dangerous.
A digital twin that operators trust and use beats a fully automated system that gets disabled after the first incident.
XIThing builds operational tools for energy and IoT teams. Get in touch if you’re working on digital twins.
