Works okay until policies or other variables change. Then it's just confidently providing bad information which results in additional support time. And when it's wrong/lying 10% of the time what's the liability impact - what if the question is about handling allergens in food? Also updating that kind of knowledge base is going to be a nightmare.

> "Are there any dishes you're out of, if we swing by in an hour or two?"

> Full knowledge is easy enough for 90% success. Just record every human to human conversation for a month and use a RAG or fine tune the model on the transcriptions.

How could it possibly work that training on historical conversations would answer whether we are out of chicken today?

But what if AI is better than humans, it will provide you the best service and out perform even the best human in the universe. then? would you still decline this AI ?

This is a good point, because the "friendliness" of the host being paid to be friendly to you is completely genuine.

This is an interesting use case. I don't know if it's a deal breaker though, because most restaurants today don't accommodate this either, unless you're already there in-person.

Letting customers do this on the phone is a risk to the restaurant, because it relies on two things being true: (1) that you'll show up. Showing up in person is a big one -- many diners in urban areas game reservation slots by overbooking, so no-shows are fact of life; (2) that you will not take more than 45 minutes (or whatever duration that doesn't eat into reservation slots).

If you're already there in-person, it's less risky on the restaurant. Then it falls on staff's knowledge of average dining time (accounting for factors like group size, kitchen turnaround time, etc.). This can be codified in an statistical model, but if you're already there in-person, staff can make a call on whether they can fit you in.

For (1), humans can't make a good decision either, unless you're a regular and staff knows you. In theory, a machine can tap into OpenTable or Tock databases to check your no-show rate, but that data is often noisy and spotty.

That said, a restaurant reservation system only has limited degrees of freedom, so if this is a common exception use case, the algorithm can recognize it and escalate to human to resolve.

If restaurants let you do this today, it's likely that they're doing a coin-flip, knowing that they might lose the bet. If that's the case, algorithms can do slightly better than random -- there are reinforcement learning algorithms that can learn about the flakiness of their clientele over time.

Another way around this is selling tickets or taking reservation deposits, like what Alinea in Chicago does -- a $xx ticket to hold the spot which goes toward the cost of the meal. Nick Kokonas (co-owner of Alinea) did a podcast on this where he said that as soon as they did this, the rate of no-shows fell.