Jesse Zhang

So I would say customer service has a bunch of nice properties that I think are very hard to reason through ahead of time, which is why I think I feel so strongly about this process of discovery, just really staying customer centric.

One of the properties is that the ROI is really easy to justify internally.

You have these numbers already tracked.

It's like, hey, we have so much

conversation volume.

Right now we have a simple chat bot or a simple IVR phone tree.

It's resolving, so to speak, like 15 to 20% of that.

If you're able to take that to 50, 60, 70, 80, like that's huge ROI.

And it's very easy to quantify.

It's like, okay, well, I'm going to take the total cost.

I'm going to chop off 60% of it.

And that's what I'm saving.

The other property, which I think is a little underrated, is that it's very easy to go live.

I think a lot of Gen AI use cases are struggling with that right now, especially at the enterprise level, because there's risk involved.

People don't want to feel like something could go wrong for them when they release your product.

Leadership's going to get mad at them.

It's like, you know, why did you do this?

So that is a big deal with Gen AI.

I think that is one of the reasons why there's been difficult for a lot of use cases to really take off.

Because at the end of the day, there is always going to be risk because the models are non-deterministic.