Bite-Size: How to Create Real Value with AI in QSR, with Sophie Buresi

0:06Sophie Buresi: The mandate at first was quite easy is we need to create value with AI. Please, we need someone to do that. But with a bit more details, actually, QSRP has, I would say, two objectives. The first one, everyone has the same, is we want to improve it a bit. EBITDA and AI. You have other tools to do that, but AI is a very powerful lever to improve EBITDA, either top line or bottom line. But obviously, you need to structure your strategy to reach that objective. And the second objective, and so that's part of the mandate, was also to say, okay, most of our strategy at USRP is based on growth. So making sure that we grow our brands, but also that we grow as a company, potentially acquiring new companies and new brands.

0:59And so we want to make sure that we can continue growing without multiplying our teams, especially the HQ teams, you know, the back office team. So that's the second mandate is how we can leverage AI and everything that we put under the AI umbrella to really keep our organization as lean as possible. It's really the mandate.

1:27Sophie Buresi: I found something that's quite rare, especially in companies that are not super mature. And it was the case of QSRP, especially versus the companies I used to work for that were a bit more advanced in data and AI transformation. But they had a lot of good ideas. I would say real will and also real commitment from the teams, but also from the leadership.

1:55Sophie Buresi: But AI is always about data. And so you need to have your data foundations, right? If you want to create value afterwards with AI. And so that's actually most of my AI foundational work is working together with the data BI teams to make sure that we have these foundations, right? So that we have all the data centralized in one place, correctly, you know, with the correct model, with correct quality, accessible. Because once you've built that sort of central data foundations, then the cost of launching any use case is almost nothing. Really, the cost of building use cases is when you have to rebuild each time foundations for each use case.

2:47Sophie Buresi: when when we were thinking about where to bring ai where ai can bring value in the restaurant operations uh of course you think about drive-through uh because there are a lot of operational constraints to drive through to drive through and your objective is to uh reduce the cost per lane um and in your drive and to increase the revenue per lane as well and there are lots of ideas of how to use AI to do that. And the easiest idea is to say, okay, we're going to use AI to take the order, maybe improve the upsell recommendation during the order. So the idea in itself, I mean, I think for anyone working in QSR, it seems like an obvious idea and you want to test it.

3:32Sophie Buresi: And it seems like a no brainer. But actually it is a brainer. You need to think about it twice. The vendor is going to charge you per lane and it's the same price everywhere. But the value of the lane really depends on the brand, on the geography, you know, even on the store itself. And so if you change one of these criterias, then the same contract, the same use case, the same idea can go from a pure yes, no brainer, let's go to an actual no. or to an intermediate situation, which is our case in most geographies, to say, yes, let's do it, but maybe not in all the restaurants. It's not a definitive no in some markets.

4:24Sophie Buresi: It's more a temporary freeze. Let's reevaluate. And potentially, we can even reevaluate the solution. Maybe in Italy, the solution is not about bringing AI to take the order. In drive-thru, maybe we can find another way to bring value with AI in the drive-thru journey, but not exactly that one.

4:49Two most important points is AOV increase, because actually you see a big impact when you have a recurrent product recommendation during order. So AOV was really important. And also staffing optimization. It doesn't mean sort of reducing the number of people, but maybe, you know, you can reallocate the time of the person taking the order to the kitchen. And especially during peak time, it can be really helpful. But obviously, we're looking at other things as well, like the throughput and drive through speed, making sure that in terms of customer experience, people are, you know, taking less time to order.

5:32if you have these two first metrics that are good, but then each car takes 60 more seconds, you know, to have their order taken, then it's going to be, operationally speaking, it's not going to be a good use case. And also another element that we take into consideration, but it's not something that you can really monitor precisely, you know, with, you know, data being collected is also customer sentiment about the case because it's typically is a very good example even in Europe depending on the markets you have very different feedbacks from the customers on AI use cases especially when it's AI taking the order so you would have people in the northern countries are a bit more used to it and they feel a bit more at ease with sort of AI or digital technology to take orders.

6:30People from the, you know, South countries, like even in Italy, they're not so comfortable having, you know, an AI taking the order. They're okay if the AI supports the person, but just having a pure AI taking the order is that in terms of, yeah, customer assignment and customer experience, it's not received in the same way. And that's something very important because even if you don't see it in the pilot metrics on the long run, it may impact your brand equity.

7:00I'm leading the group AI team, but I'm working on a daily basis with the business teams. And for each use case, even in pilot phase, we are working very closely with the local business teams and they know their market, they know their brand, they know their customers by heart. So we had that input from the local teams from day one. when we launch use case, we have that precise framework with the listed KPIs and everything. And on paper, maybe everything will seem green, but the local person, the local operator will see that there is something missing, will spot the operational points that could block the use case.

7:41So that's why we need, it's not even about integrating and collaborating with local business teams, we need really operators. So when we design use case, when we pilot use case, we always have restaurant managers, district managers that are integrated from day one, because they are the one people that will spot the problems ahead of any kind of use case launch.

8:08We start by testing the most of the time we test use cases in our company-owned restaurants, because it's easier.

8:17Sophie Buresi: We can go faster. But even if we test in the company-owned restaurant before opening the case to franchises, we always take into consideration the potential differences in operating model. I mean, if you want to sell something to someone, you want to make sure that you understand your client's, his constraints. I mean, at the end of the day, the franchisee, he's like the CEO of his own company. So what he's going to look at is the payback, you know, he's going to get from the use case. It's their P &L. It's not our P &L. So we want to make sure that the payback is super clear. And it's clear not in years, but in how many months you will get a payback on that investment.

9:01In terms of financials, we need to be super square on the figures that we're pushing. That's the first point. I think that obviously they also, you know, they trust what they see. So it's important, even if it's company-owned restaurants, it's always important when we select pilot restaurants that we select these restaurants correctly and maybe have different types of restaurants, not only flagships in city centers, but have a good, you know, a diversity of restaurants to make sure that the franchisee will identify with maybe one of the company-owned restaurants because obviously, you know, a franchisee that has a very small restaurant in the countryside, if I tell him, yeah, it's amazing, the use cases work so much in the center of Paris, it's going to be okay, great, but I'm not sure it will work exactly the same where I live.

9:55One point that we really take into consideration, and it's not really an AI topic, but that's why we work really closely with the business teams is the operational impact of the use case.

10:11We selected AI champions in every department to be the sort of leader for, you know, AI use cases. And so I would say it's the easiest dream in terms of what we have to do as a group AI team. the effort is really in the long run, making sure that people keep using the technology and also in making sure that it brings value. And the value is not easy to monitor because it's individual value, but we know that it's here and we have designed surveys to understand the impact and we can see it in, even if it's small impact, if, I don't know, you took maybe five hours during your week to build slides, to build presentation documents, and now you only take one hour, then it's already a great win.

11:05Because if you multiply by the number of weeks and the number of people in HQ teams, then you see the amount of hours that were not saved, but really replaced on like higher value topics. And the second stream, which is, I would say, way more complicated, but the impact is huge, is really process transformation. So here it's not so much about individual efficiency. It's about department-centric efficiency. And so what we want to do is, it's a bit of a consulting approach, but we need to go through that, is understand how the team works. What are the processes? So it's like doing process mapping, understanding how they work.

11:49And sometimes we discover that we have no process or sometimes there are as many processes as there are people in a team. The big mistake would be to think that AI is the only response to that. No, actually, it must be a business driven effort. And at the end of the day, most of the time, simplifying the process is not about adding an agent. It's about just breaking the process, like rebuilding something way easier with less steps, improving communication, maybe making a role clearer. And then you can add that extra layer of agentic or generative AI that will be sort of the cherry on the cake.

12:34and that was the challenge when i arrived at qsrp because everyone was super uh you know uh excited about all this agentic and ai transformation and the beginning i kept saying well yeah but we need like to work on processes but yeah we need to work on the data i was like the i was like the the annoying person saying that we we were missing a lot of things before reaching that sort of a, you know, cherry on the cake AI use case.

13:00Sophie Buresi: But now after a few months, we are in a good dynamic on that one. But that it's, I think it's the new complexity for AI people. Before you had like five years ago, my challenge was to convince people that we could do amazing things with AI. Now my challenge is not to say no to everyone and not to be that annoying person always reminding that we need foundations. We need that. We need to work on processes. We before we can create value with AI.