Successful AI adoption comes from intentionally prioritizing a handful of projects and pursuing them deeply, end-to-end, rather than spreading effort thin across many ideas.

5:12Sure. But really being intentional and saying, "I'm gonna pick four or five projects, and we are going to go deep," and actually go see it end to end, like the workflow, measure the productivity, how is it actually being useful. Because there's a lot of projects start with a lot of enthusiasm, and then y- there has to be an intentionality towards that. And so the companies that I feel

5:36are very successful are, who are doing it in a more intentional way. The second thing that I feel is a consistent theme is, like I said, there's a very subtle difference between optimizing a process and reimagining a process. Once you actually put an effort into reimagining a process and you've drawn the workflows, the ROI, and you have-- you've done-- assuming you've done it well, the ROI is just so obvious, and then

6:05the adoption just goes, you know- Sure … skyrocket-rockets across the organization. And then the third thing, I think a lot of times when customers have to, and or organizations have a choice whether they optimize their internal processes or they try and, bring AI to their customers. Try to do a lot, together. but again, it comes back to intentionality about what processes, what are the times that you're trying to fix.

6:31And, the companies that I've seen execute really well have actually just taken, they're not just dipping their, feet in the water, toe in the water. They've gone all in, bet on something, and then made it successful. for a Neo Cloud, sure, that's their business. They know how to stand up, racks and deploy large scale infrastructure. For enterprises, maybe that's a little bit of a, rusty, skill, right?

6:59'Cause they've, swung a lot to cloud. They've maybe started to repatriate. Even still, GPUs and AI workloads, change the game a little bit. I'm just curious, w- what's the differentiator between someone that says, I wanna bring these workloads in-house because of, token costs or, data privacy issues," and succeeds at it versus, tries and, turns back to the cloud because it didn't work out? Every organization is different, right?