Host (3:23)

Amen, brother. I mean, like, yeah, that was one of my least favorite jobs, and I was a merchant too, and I love planographs, but that was one of my least favorite jobs because at the end of the day, there was more efficient uses of my time. All right, so another term that's making its rounds across the industry is this idea of single. Single pane of glass. It's not new. It's been around for a while. But I'm curious, like, what's your perspective on that term in general and what does it actually mean in practice to you?

Julian Mills (3:47)

Concept of it is very appealing. It's a single place where. Where every employee can go, and it gives them just what they need to do to do that job. Okay. I don't think anyone has ever done it. No. Yeah. So I don't think it exists. Having said that, I think at Corso, we're about as far down that road for store leaders and for area leaders as anyone's gone. Okay. So we are bringing together all the work that or most of the work that they need to do, whether it's, you know, what historically would have been called a task or a walk or an audit or an alert or an exception or a ticket or a maintenance ticket or a customer callback. All of those can be done in a single workflow that's driven by data in an intelligent way in Corso. Having said that, there are lots of things we're not doing. Like, you can't check your pay slips, and I don't think you ever would be able to do that. Okay, so we think you Know, you might be able to bring 70, 80% of it into a single pane of glass. But the vision of having everything in one place for every role in the company, I think is aspirational.

Chris (4:47)

We're talking about technology that's helping the store teams get smarter. What do you think that means for the role of the field leaders? How is that going to evolve?

Julian Mills (4:56)

This is changing very fast. So what we're seeing is that as you use data and AI to push work and decision making and action taking down into the store, the role of the district leader is evolving and becoming more what I think what most district leaders would like, which is more of a kind of a coach and a person who's there to help when people really get stuck. Yeah. So if you think about it today, how does it work? So, for example, a couple of weeks ago, I was touring stores with a market director and he said, look, here are my 79 KPIs, here's all my dashboards. I'm somehow meant to walk into the store and know that they got a problem with hams. Yeah. But if I can detect it's got a problem with hams, and I want perhaps the store to have fixed it before I get there. And I should only really be there to help them if they're getting stuck on things that they don't know how to fix.

Host (5:48)

Yeah, right.

Julian Mills (5:49)

So I think that kind of diagnostic role is pushing down into the stores and the field leaders, becoming more of a coach and more of having a broader kind of more strategic role.

Host (5:58)

And I felt that pain every single day. Like, you know, you'd be expected to go in and diagnose these problems. And the part you said about too, like the dashboards. The dashboards are just overwhelming the number of data and like, yeah, you just can't possibly check the whole thing. And so it just doesn't make sense and it's a lot of wasted energy. But, you know, in the perfect segment, the segue of all time that I've always wanted to make, I want to go from ham to AI Julian. So I want to close with this. I want to get us out of here on this. So, you know, AI, you, you mentioned it in that last statement, actually. So, in all seriousness. So, so, but the question is, how far should retailers let AI optimize their store operations? And, and, and how should they think about that in terms of the dichotomy of what still requires human oversight? That's the question I want to talk to you about. So what are you hearing from executives at this event, what did they. How do they think about balancing AI versus human interaction or human responsibility in the store?

Julian Mills (6:52)

So two years ago, if you'd asked me that question, I'd said most retailers are firmly towards the we must remain in control of everything. So they probably battle one out of five on a scale of, you know, human control to AI running everything. I think today we're about three and a half. Okay. So there's been a rapid shift. Having said that, everyone at the event, when you present them the logical conclusions of doing absolutely everything through AI, felt that they weren't quite ready for that yet, or it might not be appropriate for that particular bit of the problem. Let me give you a very specific example. Corsa uses lots of different types of AI across the platform. For one of the things we do, we essentially watch lots of operational data and trigger an alert when something goes wrong. Okay. For something like a product recall, they're saying we absolutely need to know that that product is being taken off the shelf. So, you know, we want to use a more kind of deterministic model for that. You know, we want a more rules based machine learning approach to that. But then there are other areas, for example, where we're much happier to have, you know, an LLM or the equivalent actually optimizing stuff for us. So, for example, an LLM might be helpful in sifting through all our kind of SOPs and come up with a personalized plan on how to fix a particular issue. Yeah, so I think what we're seeing is it's a very nuanced solution. In some places you want a much more deterministic kind of rules based solution. In some places ML is great, and in some places Genai can work magic, but you need to be doing all of them and bring them all together in one place and be very transparent around what you're doing. Right.

Host (8:42)

So net net, there's no one right way. You've always got to have a balance. Which is, which is why we love talking to you, Julian. I mean, you and the team at Corso do such a great job of thinking about the next level of where store operations is going. So thank you for joining us today.

Julian Mills (8:56)

It was great pleasure. Thank you for having me.