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I think we need to first start off with what the exact definition of AI

"the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages."

This definition of A.I. gets used so loosely now that it's become a joke.
You can't just do 1 part of it and say that you're doing A.I.

For example, I have a light sensor in my house that detects if I walk past it, then turns the light on.
I no longer have to use intelligence to turn the light on, so it must be A.I., right?
No. Replacing decision making and "not requiring human intelligence" does not mean it is A.I.

A.I. requires that the system is actually replacing a function that humans were doing, that requires real intelligence.
This comes down to data analysis and decision making, not just data analysis and not just decision making.
If it only does one or the other, it's not A.I. it's skip logic or if/then statements.

Watson is actually doing some real A.I., but even it has failed miserably at many of its tasks, proving that it lacks the ability to demonstrate real intelligence.
https://spectrum.ieee.org/biomedica...promised-and-underdelivered-on-ai-health-care

We have similar AI built into some of our products and platforms that remove the human intelligence and decision-making process.
 
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✨ AI Highlights

Automotive veteran Alex Snyder vents frustration over vendors misusing the 'AI' label, prompting a live critique of Zenzio, an LLM-powered chat tool used by dealer Fitzgerald. While Joe Pistell sees it as a strong MVP compared to legacy chatbots, others like Carsten and Ryan Everson flag real problems: slow response times, lack of VDP context awareness, and generic CTAs that confuse shoppers already looking at a specific used vehicle. The thread lands on a consensus that current automotive 'AI' is promising but far from customer-ready.

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