How do you get from there to here, and what pulled you so deeply into this intersection of AI and customer insights?

Yeah.

So I've spent a lot of years with a lot of hats on, to be honest.

I think that's pretty important.

I mean, you already mentioned a few of the sort of spaces that I've worked in or the types of teams that I work in, but I actually also come from a marketing background.

I studied communications.

I worked in advertising and marketing, and then I moved to UX design and service design, and then I had my first actual official user research role title, but I did, I did research in all of those roles.

I'm wondering a- as we build out these workflows, as we look at to see if and how AI can make things better, what do we hold back? What should we not automate?

Um, what is the next step? Maybe also the hand off to the, to the other department, right? We are not working in a bubble as a researcher.

I think the next step that most people do is checking everything that came out of AI, [chuckles] to be honest.

And I think this is really important because, you know, one question I get asked often is, how do we stop kind of triple-checking everything? 'Cause a lot of teams I've worked with who are, you know, very used to doing research and know how to do it well, whether they're designers or product managers or researchers, are still unable to immediately look and go, "This is right or wrong." Because especially if you're doing, you know, new work, you're not testing with old insights or old data files, you can't tell.

Claude and all of the other large language models give you something that looks very confident, and they can be filled with so many issues, but who can tell what's right and wrong? So I think everyone is still where, let's say people fall into two camps, either overly trusting [chuckles] or really second-guessing everything and going, we spend, you know, a fraction of time getting the insights out now from the data.

But we kind of eat all of that time that we saved back up because we kind of triple-check and compare exact details and data and the outputs.

So I think a next step is actually implementing a workflow where you have a little bit of an iteration process, and that doesn't have to be that you, you know, the human digs through every detail of what came out of Claude or other large language model.

You need kind of a system.

most designers don't work with GitHub, but I think it's, it's important to know.