How much of it do you think is the fact that there is not like an actual system that everyone follows and even can you even have a system with something as dynamic as AI or is it just a case of not being able of just having to change doing change management and changing the culture to make sure everyone actually embraces it or is it fundamentally ai it's an accelerator but never actually going to be 2x or 5x or 10x

really good question um i think there's a couple of components to this uh so the first and you and i were kind of talking offline on this um but um when we see AI implementations fall down, it's typically because we're very, you know, shocking a RevOps agency has got frameworks.

We believe there's this framework of how you need to step into AI.

And our belief is kind of four steps.

Like first is you have to have your core go-to-market strategy, processes, toolings, and data in a solid foundation, right? Because otherwise, if you layer a bunch of AI on top, it's just, and you have a bunch of junk, it's garbage in, garbage out, right? The number of organizations we worked with who have 32 definitions of an MQL, well, you layer it on top and say how many MQLs you have, it's going to spit back some numbers that a bunch of people are going to disagree with, right? So you got to have that in place.

The second is having a clear understanding of what's your external, but equally as important to your internal customer journey.

So said another way, like AE gets on a first discovery call.

So like, when should we be using Fable versus Sonnet or Opus or and that's when, which ones are even, in some cases when you should be using a local LLM instead, or even workflows to your point before, as opposed to like, you know, saving money, or do you think that's just kind of not really an issue for a lot of teams?