Dannii Mathers

This could be your last, Alan.

Thank you.

So I would say, and we were talking about this just early on, I think right now there are so many shiny new tools being thrown around everywhere.

And it's creating almost a little bit of anxiety of, okay, what do we need to use first?

We need to be showing that our go-to-market teams are utilizing AI.

And I don't think there's much thought behind the strategy of how this should be executed in a meaningful way.

And what I'm seeing is we talk about data is it's really shining a torch on how critical now those data pieces are, because quite often people just be looking at the lagging indicators.

OK, these are the data numbers we want to get to and almost ignoring all these important data sets that make that up, the how, the what's.

And now I think AI is really shining a light on what's actually broken.

Because if you don't know all the hows and the whats and all those important data pieces before you get to the lagging indicators.

AI is just not going to work.

So I think now people are going in this kind of full circle as let's flood.

Let's flood with these new AI tools without really thinking, well, how are we going to extract meaningful data in the first place?

Do we have data that we can use to tell a bigger story?

So I feel like there's a lot of going around in circles of people thinking this is the right thing to do and then immediately pulling back with this isn't working for us and it's not working because data is broken.

I think you've greedily took them all there.

Yeah, it just goes back to even if you look at the core principles of enablement and looking at what's behind all the data.

So I think typically it's having the right focus in the right areas.

So even if we look at, say, top of funnel,

And a lot of customers that we speak to, there's just a lack of measurement across all different levels.