Katie Bianchi

One of them, I think, to me is like I would have started earlier on the the data side. I think it's the most underestimated part of any AI effort and probably the least glamorous in certain ways. I think people tend to get excited about the shiny layers of co-pilots and automation and agentic AI.

But if the underlying data isn't clean or you don't know what data is required to solve the issue and you don't figure a way to turn that on and get it flowing across your data pipeline, the value just won't be there in your solution. won't scale.

But if the underlying data isn't clean or you don't know what data is required to solve the issue and you don't figure a way to turn that on and get it flowing across your data pipeline, the value just won't be there in your solution. won't scale.

But if the underlying data isn't clean or you don't know what data is required to solve the issue and you don't figure a way to turn that on and get it flowing across your data pipeline, the value just won't be there in your solution. won't scale.

So look, I think looking back, we probably could have moved a little bit faster if we had started earlier in the journey around data unification and sort of capturing some of the telemetry much more quickly. So I think that's one thing. And I think

So look, I think looking back, we probably could have moved a little bit faster if we had started earlier in the journey around data unification and sort of capturing some of the telemetry much more quickly. So I think that's one thing. And I think

So look, I think looking back, we probably could have moved a little bit faster if we had started earlier in the journey around data unification and sort of capturing some of the telemetry much more quickly. So I think that's one thing. And I think

One of the biggest misconceptions, especially for, I would say, like a customer experience organization, as you go on this AI journey, I think the biggest misconception is that you can do it alone. What do you mean by that? Well, I don't think AI transformation is something that a post-sales team can or should own entirely. in isolation.

One of the biggest misconceptions, especially for, I would say, like a customer experience organization, as you go on this AI journey, I think the biggest misconception is that you can do it alone. What do you mean by that? Well, I don't think AI transformation is something that a post-sales team can or should own entirely. in isolation.

One of the biggest misconceptions, especially for, I would say, like a customer experience organization, as you go on this AI journey, I think the biggest misconception is that you can do it alone. What do you mean by that? Well, I don't think AI transformation is something that a post-sales team can or should own entirely. in isolation.

You can't bolt it on to existing workflows and you don't want to. You need to drive net new process re-architecture. And that takes a lot of partnership with your strategy or operations organization, as well as your IT organization. And when you think about fundamentally what you're doing, you're solving problems with the product. And

You can't bolt it on to existing workflows and you don't want to. You need to drive net new process re-architecture. And that takes a lot of partnership with your strategy or operations organization, as well as your IT organization. And when you think about fundamentally what you're doing, you're solving problems with the product. And

You can't bolt it on to existing workflows and you don't want to. You need to drive net new process re-architecture. And that takes a lot of partnership with your strategy or operations organization, as well as your IT organization. And when you think about fundamentally what you're doing, you're solving problems with the product. And

our product organization is actually the one that is building technically doing the engineering behind the co-pilot build. And I think that that has been a huge accelerant for us. Now we're doing that in the highest degree of partnership where we are cleaning our data, validating our data, and then helping the product organization improve both efficacy and accuracy.

our product organization is actually the one that is building technically doing the engineering behind the co-pilot build. And I think that that has been a huge accelerant for us. Now we're doing that in the highest degree of partnership where we are cleaning our data, validating our data, and then helping the product organization improve both efficacy and accuracy.

our product organization is actually the one that is building technically doing the engineering behind the co-pilot build. And I think that that has been a huge accelerant for us. Now we're doing that in the highest degree of partnership where we are cleaning our data, validating our data, and then helping the product organization improve both efficacy and accuracy.

But we would not be where we are right now if they weren't owning the engineering around that, right? Number one, because they've got the engineering expertise, but number two, because they have the product expertise.

But we would not be where we are right now if they weren't owning the engineering around that, right? Number one, because they've got the engineering expertise, but number two, because they have the product expertise.

But we would not be where we are right now if they weren't owning the engineering around that, right? Number one, because they've got the engineering expertise, but number two, because they have the product expertise.

Yes. And I think for us, what's been really interesting is that AI and this sort of revolution has made tight collaboration between go-to-market, CX organizations, product and IT. It's an absolute non-negotiable. One example I would give you is really how we're working to unify data capture and automated workflows across both pre and post sales.