Scott Guthrie

And so I think people are increasingly focused on not just for the potential of AI, but really like, okay, let's demonstrate how we use AI in business.

Yeah.

to really drive tangible results.

That's one aspect and you need some degree of observability to know if you're doing that.

And then there's the, okay, now that we're seeing the big results and now that we're using it for mainline tasks that are really driving business value, how does it not break?

And I think a lot of people go through an experience where something goes wrong in their AI workflow.

Maybe someone rolled out a code fix without it being fully tested or someone rolled a new model

And it improved on eight dimensions, but regressed two.

And that's when people suddenly realize also, wow, I've really taken a dependency on AI.

And I now need the maturity level that I would have when I'm going to touch one of my core systems.

And that's where you need observability as well.

And that's why I think observability just pops over and over again in our conversations.

And I think people are also realizing as models are smart,

And as employees can ask the model to do things, how do you know that the model doesn't give an answer because it's been given access to maybe more information that the employee shouldn't know?

Things like financials or internal HR or legal things that are going on inside an organization.

If a customer asks in a support bot, like, hey, what's the maximum discount that my salesperson is allowed to give me?

You might not want that support model to give you the answer.

And that's where also I think things like governance and things like the right compliance levels really come into play.

And again, the more you have a really good story for that,

the more organizations can bet on AI and get real value and are willing to invest to do even more.