Marybeth D'Souza

With the introduction of AI, there's a ton of opportunity to change customer success. We have talked about, and in the industry, people have talked about digital CS for years. It started as a segment. Like, it's like, okay, we're going to have like digital CS to handle the lower end and Now, digital is not a segment. It should be deployed and leveraged for all segments.

And then it's really, for our previous conversation, how do you deploy humans or interventions where it makes sense? And the introduction of AI is really just helping to make that, I would say, table stakes now, where I said before, companies may have invested a little bit, but not fully. Now it's table stakes to have the right infrastructure and the right data in place

And then it's really, for our previous conversation, how do you deploy humans or interventions where it makes sense? And the introduction of AI is really just helping to make that, I would say, table stakes now, where I said before, companies may have invested a little bit, but not fully. Now it's table stakes to have the right infrastructure and the right data in place

And then it's really, for our previous conversation, how do you deploy humans or interventions where it makes sense? And the introduction of AI is really just helping to make that, I would say, table stakes now, where I said before, companies may have invested a little bit, but not fully. Now it's table stakes to have the right infrastructure and the right data in place

in order to leverage AI, because you're going to need to do it with your teams and your customers also expect it. Like your customers expect you, how about this data on me? I'm sure you're, you know, introducing, whether it's very low hanging fruit, like meeting summaries and notes and follow-ups to something much more transformational.

in order to leverage AI, because you're going to need to do it with your teams and your customers also expect it. Like your customers expect you, how about this data on me? I'm sure you're, you know, introducing, whether it's very low hanging fruit, like meeting summaries and notes and follow-ups to something much more transformational.

in order to leverage AI, because you're going to need to do it with your teams and your customers also expect it. Like your customers expect you, how about this data on me? I'm sure you're, you know, introducing, whether it's very low hanging fruit, like meeting summaries and notes and follow-ups to something much more transformational.

Like they're expecting it of the companies that they're working with as well.

Like they're expecting it of the companies that they're working with as well.

Like they're expecting it of the companies that they're working with as well.

Yeah. I, the way I think right now, the challenge that we have is moving from experiments. Like last year was the year of pilots. People are like, let me try a pilot leveraging AI. And so it's, how do you move from an experiment to actually scaling? And in order to do that, there's a couple of, there's some things that kind of need to be true.

Yeah. I, the way I think right now, the challenge that we have is moving from experiments. Like last year was the year of pilots. People are like, let me try a pilot leveraging AI. And so it's, how do you move from an experiment to actually scaling? And in order to do that, there's a couple of, there's some things that kind of need to be true.

Yeah. I, the way I think right now, the challenge that we have is moving from experiments. Like last year was the year of pilots. People are like, let me try a pilot leveraging AI. And so it's, how do you move from an experiment to actually scaling? And in order to do that, there's a couple of, there's some things that kind of need to be true.

You need to have, like I said, like the right data models. You need to have the right infrastructure there in order to, within your company, in order to do that. We find a lot of AI projects end up being data projects because that's a precursor to that.

You need to have, like I said, like the right data models. You need to have the right infrastructure there in order to, within your company, in order to do that. We find a lot of AI projects end up being data projects because that's a precursor to that.

You need to have, like I said, like the right data models. You need to have the right infrastructure there in order to, within your company, in order to do that. We find a lot of AI projects end up being data projects because that's a precursor to that.

You need to have the right level of governance and infrastructure in place in order to manage at an enterprise level so that little AI experiments don't get out of control. And you need to also have an understanding with the organization around what's the level of trust we have? How do we introduce this while still having a human in the loop? And how are we navigating this change? And so...

You need to have the right level of governance and infrastructure in place in order to manage at an enterprise level so that little AI experiments don't get out of control. And you need to also have an understanding with the organization around what's the level of trust we have? How do we introduce this while still having a human in the loop? And how are we navigating this change? And so...

You need to have the right level of governance and infrastructure in place in order to manage at an enterprise level so that little AI experiments don't get out of control. And you need to also have an understanding with the organization around what's the level of trust we have? How do we introduce this while still having a human in the loop? And how are we navigating this change? And so...

And that's really, I think, the moment we're in right now is, you know, there's been a lot of really interesting things that have happened that, you know, maybe save some time. But as you look to scale those, it's going to require leadership and cross-functional kind of collaboration in order to do it. The other way that I see...