this post was submitted on 24 Oct 2025
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A Boring Dystopia
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The amount of traceability in auto plants regarding what was put onto a vehicle would surprise a lot of folks. The capability to do this greatly predates AI and I'm not really sure what AI would add in the example you cited. Nearly any OE part with a barcode can be tied back to the vehicle it was originally installed on, what shift installed it, which other vehicles have a similar lot of parts, etc. Correlating this information with other basic record keeping, like which station installs the part and who was working at that time, makes it very easy to unearth trends. Plants have quality incentives, so there's a strong motivation to identify areas for improvement.
In addition to quality, it turns out the small additional cost of all this data capture and storage quickly pays for itself in the case of a spill. Being able to say "this specific set of vehicles" can save a ton of money. That's part of the reason why very small and targeted recalls have been becoming more common.
You're absolutely right and I'm aware that the traceability was already a given. The collected data also was already there and has been for years.
The AI in this case added basically a simple "human language" interface and some automation. The multi agent approach was basically a "simple" way to integrate multiple systems. You could've done the same before but with more manual steps or fully automated but then more rigid and less flexible. E.g. with the AI system you could ask follow up questions or add other bots to the conversation etc.
Ah, I see. It's very true that a lot of plants have... older software setups that likely require a bit more of a human touch than should be necessary. I don't work in a plant, but that's basically been my career arc - "the poor humans have to hop between how many disconnected systems to accomplish what now? Let's write some better software to address that."
Using AI as a replacement to human glue seems reasonable if you have decent data to traverse. The "data" at my employer is often bespoke to each system, which results in a lot of gray matter mapping names and attributes across systems. Our IT org is working on rolling out glean, but so far it's basically a better internal search than offering real insights.
Quite interesting. I worked as a software engineer in different areas but not in data sciences. The demo I described was part of a bigger effort to bring visibility to AI related use cases. This one was a relatively good fit in my opinion, as long the data is mostly handled outside of the LLMs and they just "translate" to human language and glue things together. Otherwise I'd fear hallucinations and data not fitting into the context window.