I've been looking into the overlap between AI and IoT, and one thing that seems increasingly interesting is the move from simply collecting data to actually using that data to make decisions in physical environments.
Traditional IoT can tell you things like:
Where an asset is
What a machine's temperature is
How much inventory is available
Whether equipment is operating
What a sensor is detecting
Adding AI potentially changes the questions we can ask:
Is this behavior unusual?
Is the equipment likely to require maintenance?
Why is a process becoming inefficient?
Which assets are being underutilized?
What should an operator investigate next?
That seems particularly relevant in manufacturing, logistics, construction, energy, mining, and other industries where physical operations generate huge amounts of data.
I'm also interested in the venture-building side of this. Aperture Venture Studio focuses on the intersection of AI and IoT and on developing technology ventures around real-world industrial applications.
What I find interesting is the progression from:
Sensors → Data → AI → Decision → Physical Action
The difficult part isn't necessarily building the AI model. It's making the entire system work reliably in a messy physical environment where sensors fail, data can be incomplete, connectivity isn't always perfect, and existing industrial systems may be decades old.
For people working in IoT, robotics, manufacturing, or industrial AI:
Where do you think the biggest practical AIoT opportunity is right now?
Is it predictive maintenance, asset tracking, robotics, industrial safety, supply chains, or something else?
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