Watch this FSTEC 2024 panel featuring Fourth’s CEO, Clinton Anderson, Jerry Phillips (VP of Technology at Whataburger), and Drew Broadnax (Director of Operational Finance at Brinker) as they explore the game-changing potential of AI in restaurant operations.
Clinton introduces Fourth iQ and how it tackles key challenges like rising costs and regulatory pressures by enhancing forecasting and labor efficiency. Jerry and Drew share real-world insights on how AI is driving efficiency and boosting customer satisfaction at Whataburger and Chili’s, along with practical advice for those looking to get started with AI.
Clinton Anderson, CEO, Fourth
My name is Clinton Anderson, CEO at Forth. I’ve been at the company for five years and it’s been an amazing five years. I think what the restaurant industry has been through, starting with Covid, coming out of Covid, technology continues to change, our managers’ job, jobs getting more and more complicated, right? The world of compliance getting really tricky, especially if you’re operating in places like Philadelphia, New York, California, Oregon, increasingly even places like Denver, Chicago.
And profits are becoming harder than ever to to, to generate. And so in the milieu of all this, this change, we also have, AI. And that’s what we want to talk to you about today is AI. But rather than me pontificate on what I think is happening in the future, we invited two restaurant executives to join us.
And what’s cool about these two guys? First of all, I love them on the marquee. I put them their names on the marquee, and you’re like, we get standing room only, like, you guys are going to get repeat invitations to join us up here. What’s cool about these two guys and look in full cander, they are customers. I know them well, I like them and I think they the way they approach technology in restaurant operations is really insightful.
So what’s curious here is you’ve got two guys completely different restaurant operations, completely different jobs, yet trying to achieve the same goal. So let me introduce my guest today. We’ve got Drew Brodnax, who is the head of operational finance at Chili’s. You say “what’s operational finance?” His job is working with all the restaurant operators across all the geographies where Chili’s located to optimize store performance, and that can be technology oriented.
It can be process oriented. It’s like, how do we drive improved operation, improve performance? And curiously enough, as I talked to drew about how he thinks about the world. Technology plays a critical part. Sitting next to him is Jerry Philips. Jerry is vice president of technology at Whataburger. Okay, so he’s got more of the technology job, yet they think about the world in a very similar fashion.
Right. Jerry is using technology to transform Whataburger or any water burger fans here. Yeah, this Texas God blessed to have a lot of chili fans too, right? But Whataburger is deeply loved here in the state of Texas. But if you’ve ever been through the drive thru on a busy afternoon, you’re like, what are they doing in that damn store, right?
I’m willing to wait for a good burger. And if Jerry’s got doctor Pepper shakes on the menu, the seasonal items, I’ll wait a long time. But it’s super frustrating, right? Because you just know that look, technology’s not quite working the way it needs to be. And Jerry, for the last look, he’s been there for what? How many years.
Jerry Phillips, VP of Technology, Whataburger
Now? Ten years.
Clinton Anderson, CEO, Fourth
A decade. But I would say in the last three years, he and Joe, the chief technology officer, have been very aggressive in pushing a change agenda in an organization that was pretty happy with some really great success over the last 50 years. And so you’ve got two leaders coming together, different worlds, different restaurant concepts and operations, but similar thinking.
And so as I’ve been in meetings with them individually and we thought, who should we bring onto this forum? These guys seem perfect. So we’re really grateful to have you here today. And I’m going to talk for about maybe ten minutes teeing up this concept, and then we’re going to turn the time over. Then we’re going to facilitate conversation with them, asking questions on their experience with AI.
And then look, we’ll open it up to the group as well. If you’ve got questions or ideas, things you’re concerned about, things you’re interested in, we’ll let you participate as well. But I promise I’ll try and go through this next content pretty quickly. All right, look, at the end of the day, we love the restaurant industry. For many of you, the restaurant, just the atmosphere, the vibe, the the excitement of running a restaurant flows through your blood.
But we this isn’t a not for profit, right? We do this to make money. And I’ve worked in a before I became involved in technology. I was consultant for 20 years of my life and I worked in probably 20 different industries. And there’s only one industry where it’s harder to make money in restaurants. You might want to guess what that is — airlines.
But congratulations, you picked the second hardest industry on planet Earth to make money. But people who love airlines that people love restaurants love airlines and restaurants. So I get it, but we’ve got this increasing challenge, right? Because look, profitability is hard for a bunch of reasons, right? Rising food costs. Right. Rising labor costs, shortage of labor. More turnover than we’ve ever seen before.
Not just in our hourly workers, but also in the management ranks. Our managers lives are more difficult. They’re dealing with not only rising food costs, food shortages in some categories. They’re facing, with this world of increased compliance, how many here have have operations where compliance is a real issue, right. If you’re coming in from states, Oregon, California, cities like Chicago, Philadelphia, New York, and even if those of us who operate in places like Florida, Alabama, Texas, where it’s a lot easier from a compliance perspective, that world is probably coming our way.
And so I would argue that it’s never been more difficult to achieve top end operating margins. The good news is, despite these challenges, if I think about what role AI plays and we’re all wondering what’s AI going to mean to us in our our daily lives, but in our professional lives, AI’s going to make our job easier to make money, and that’s what gets us excited.
AI is already at play at restaurants, right? So you’re probably thinking, where do we go with AI? Should I think about it? Should I start with it? Companies are already using AI, right? So we developed an AI forecasting tool that is in place at these customers. And the AI forecasting tool has increased forecast accuracy by over 20%. And that’s just one example of one use case where AI can make a difference.
And it’s end is having a profitable impact. Right. So Noodles to save over $4 million in labor right. Chili’s is saving over 600 labor hours a week nationally. Why does that matter? Well, to a certain degree, that’s money. But maybe more importantly, it’s freeing up managers time to be out front, training the team, ensuring an amazing guest experience.
Pizza Hut right? Cranking out almost $250,000 a quarter. And that’s at just the stores if implemented thus far. By the time they roll out across their entire US landscape, then impact will be better. Thai Leisure Group, of customer of ours who you haven’t heard of because they’re operating out the UK. Interesting story here. Sure, they’ve they’ve seen a 1% decrease in labor costs, but what they’ve done is redeploy that labor to actually drive increased sales.
We always think about labor being reduction of cost, but if you get the labor in the right places and you’ve got kind of a constrained operating model in your restaurant, you can actually end up with increased sales. So we think that it’s happening now and we should all be asking the question, which is why these guys are here, but how do you get started and where do you see the use cases?
All right. Let me just I said I won’t pontificate for long, but I will pontificate just a couple of minutes.
Where we think AI is going is it starts with it starts with exposing your data. Right. And Jerry and I had a conversation about this yesterday. It’s so important to be able to expose your data to the AI engine. And that AI engine should feed a set of dashboard analytics in real time. That then gets to the to to the holy grail.
Okay, so those three things are kind of fundamental. If you don’t have clean access to your data, that’s the fuel that runs the engine. The dashboard and insights are the AI engine kind of working. So you’re not looking at a graph, right. Or a chart and saying, okay, what does this mean? What do I do next? Instead? It’s kind of doing that analysis for you.
It’s like a 24 over 365 analytics engine. And if it’s in real time, then it’s not looking to say what happened last week. What do I do next week? It’s like, what’s happening now? What do I do now? And that’s getting to the so what? The real power of AI in our industry, which is when the AI engine is consuming data, analyzing situations and it’s all through correlation analysis.
It’s all about making correlations between what’s happening in this store, what’s happening in that store, and is that driving a difference in performance and trying to tease out what makes one store more profitable than another, what makes one crew more profitable than another? What decision resulted in better performance and then surfacing that. And when you get above store insights in a real time environment, you can then drive in-store, in-shift actions. Some of you are like, nodding your head. Others just saying “What does he mean by that?” Let me give you an example. I’m going to get back to this thing. Imagine we have Sam. Samantha has been working as a manager in a new store in Oregon for about a year. Right. She’s been in the system for about four years as a manager, but she’s been in the store for a year, and it’s a busy a really busy day.
And it looks she knows her phone and says, look, it’s not gonna be raining in Oregon this weekend. It’s gonna be warm. She’s got a big patio business, right? And she’s thinking, oh, I bet we’re going to be busy this Saturday. But she’s in the middle of the lunch. Lunch rush and she’s thinking, oh, I’ll need to remember that right now.
Imagine instead of her trying to remember, oh, I need to pick up some staff. And oh, by the way, I operate in, in, in Oregon, so I can’t just tell someone to pick up a staff unless they’re on the volunteer standby list. I’ve got some problems. And these are the decisions that face managers day in, day in, day out.
I make the argument all the time. Even though I didn’t grow up in the restaurant industry. I’ve never worked in a restaurant, but I’ve come to understand the restaurant operations. I think being a store restaurant is arguably one of the toughest jobs in all of America. We ask our manager to do so many things and remember so many things, and no one’s great at everything, and we ask them to be good at almost everything.
But in this environment, imagine that your data is exposed, right? Operational data, weather data coming from third party sources, and the AI engine’s cranking and the AI engines are seeing something that popped up new. Hey, warm weather, sunny day in Oregon and they can look back historically and see what’s happened in past sunny days in Oregon and say, guess what?
In those in those situations, we typically see about a 25% increase in store traffic and 35% increase in ticket. And then without having to make Samantha think about this. Instead Sam gets Sam, gets a notification on her phone that says, hey forecast. Looking for warm, sunny weather this weekend? Please schedule two more staff members in front of house for Saturday at 3 p.m..
And by the way, I recommend Mike and Barry. They’re both on our volunteer standby list. You can ask them to pick up the shift without incurring any compliance issues with the Oregon Board of Labor. That’s one simple example. Imagine that applied across thousands of decisions that the manager makes each and every day and every week. And if we do this right, exposing the data to the right algorithms.
And by the way, the fuel’s the most important thing. The data is the fuel. Because remember these are learning systems. And as you feed more data through the systems, they get smarter, they get better. I was talking to Drew just this morning about it. You show me some data, which I’m really excited to talk about that highlighted that.
You can see the machine learning. It’s getting better each week. They’re they’re operating. And so as you expose the data to these these systems and they’re looking for the correlation analysis, the correlations get smarter and the recommendations get better. So. Think of AI as simply a tool that can be embedded in your systems. That simply allows you to make better decisions on a real time basis across the things that matter most.
Now if we were to take AI and bury Jerry, he’s a store manager. Bury him with a thousand things. He’s going to get overwhelmed. He’s not going to look at his phone. But if we can automate those things that are administrative in nature, so they happen automatically. The frees up time to Jerry to spend more time with guests, more time with the team, and simply prompt them to do the 2 or 3 next best things.
Right. We all remember. Well, some of you were quite young in here. Those of us who have gray hair or no hair, remember the days before smartphones, right? And before Outlook. And you have to remember all these things, right? Like, oh, I’ve got an appointment at 2:00, and you might even carry around like a little planner and. Right.
You had to know how to get from your house to the hotel or from the airport to the hotel looking at a map. And now we just completely trust our phones to do those things. I remember the day when I flew to Mexico for a meeting in downtown Monterrey, and I didn’t know what my flight was. I didn’t know where my hotel was, and I didn’t know where the meeting was, but I went got on the airplane because I knew it was all my fault.
Right? That concept would have been terrifying 25 years ago. So just like some of us are a little bit nervous about AI today, it’s going to become, it’s going to become so commonplace and such a useful tool. It’s going to become second nature to that next generation of managers and workers who are already in our stores today.
Let’s talk about people’s real experience with with AI. And the question I posed to both Drew and Jerry and we’ll start in that order is, first of all, like AI seems so daunting, like where do you start? And how do you guys think about the best use cases where this can be relevant to restaurant operations?
Drew Brodnax, Head of Operational Finance, Chili’s
Yeah. So, for us, so at Brinker, we believe storytelling is probably one of the most powerful things you can do with operators. So as a finance guy, you know, one of my challenges is how do I get our operators to trust, right. And so it’s not seen as some golden palace or some finance person, you know, trying to finance the right, trying to tell me what do so.
What you know, a big thing that we use at Brinker is storytelling. Well, how do you how do you use storytelling. So the first thing we did, you know, I, I like Clinton, I trust Clinton, but when he says, hey, we’ve got this, we’ve got this great tool is I’m going to give it to our aces. Right? So, you know, what we did is we we started with probably one of our highest performing areas, which is a cluster of about ten restaurants.
And we gave it to them. We said, kick the tires on it. talking really closely, you know, weekly with the director of operations and the GM and, and kind of first was like, hey, do we do we think we even have anything here before we get anyone else involved? And, kind of kicked it off in July of last year, which is actually really important timing because in August, at least for our business, we typically struggle towards the end of August and September because you have back to school.
So that’s that’s a really great time to see. Hey, do we think do we think we have something here? And you know, what we started hearing is, hey, this thing this thing is we can see that it’s detecting back to school. We can see that it’s it’s, it’s more accurate. 95% of of the GM said, hey, this is a go.
and this was our top performing area. So we did see we did see benefit with them. And then we said, okay, well, let’s go completely opposite direction now, let’s go to one of our lowest performing areas. That’s really when the film that right. You know, we didn’t we didn’t we didn’t tell them that. But that’s really where we saw the fireworks go off is, you know when you give it you know, we have coming out of Covid, our manager tenure is is lower than what it was.
So, you know, the average manager in our business, you know, isn’t as tenured. We have a lot of managers going through holidays for the first time going through back to school. And so when we gave it to some of the some of those lower tenure managers, to play around with it, we really saw the huge, huge benefit there.
And so then we said, hey, now let’s take this to the brand. And so we rolled it. We rolled it brand wide. But what we used is those to basically cluster of 20 restaurants, a couple of directors, we let them get on the phone with their peers and tell the story. The proof is in the pudding. Showed some results.
And then we got to a point where we actually started having, you know, other markets start asking for it. And so it was no longer seen as something that that corporate was giving me or from finance. It was seen as an operational advantage that my peers are using, saving the manager some time. And so we leaned in the storytelling, we kind of proved it out and then went to some areas, had some results.
And then we kind of we’ve been going for four months plus now and over 20% improvement. So it’s it’s really exciting stuff.
Clinton Anderson, CEO, Fourth
You know, I love how you referenced how it it makes the life of a great manager easier. Great managers are amazing, right? If you’ve been running a store for a long time, like they’re darn near AI, right? But it makes their lives easier. But where it gets super powerful, right is it takes that that underperforming manager, right. That new manager, that insecure manager and makes them very good.
Right. So takes a bad store. Bad manager makes them above average, takes an average store, makes them great right. And that’s and that’s where we see a lot of opportunity. Now Jerry, what was your experience. How did you think about it.
Jerry Phillips, VP of Technology, Whataburger
Very very similar. we did take a, we added to that a little bit. We wanted to make sure, our managers, of course, on a 24 by seven operation, they’re managing a team of 70 people. And so we were like, okay, we can’t just throw AI at them. First off, they don’t understand AI. They, they see the marketing.
They think that it’s, you know, what Microsoft says on TV, right? It’s going to make your emails easier. And these guys are like, I need help.
Clinton Anderson, CEO, Fourth
How does that relate to a restaurant?
Jerry Phillips, VP of Technology, Whataburger
Right, right. I was like, yeah, that’s not our job. Right. And so we said, the first place we’re really going to go focus is in that, that standardizing a sales forecast, which is the basis of your labor and your inventory. And we did a very similar approach. We took, the top markets and we took a few of our lower, performing restaurants.
And again, we didn’t tell them that you’re top and you’re not. But we said, here’s a tool. We walked them through the value that it showed. And then we just step back and we we let them actually start to see it. And then we’ve had some guidance that would come along with that, to sit on top of it and really give them the opportunity.
And it was very similar. What we saw was the good managers, the new managers that didn’t know any different. They actually accepted it a lot faster because they were like, oh, I don’t have to understand the 87 other things that I’ve got to look at right now to adjust. I don’t have to understand that weather forecast. I don’t have to understand, worry so much about it’s football season and there’s going to be a school bus of kids drop in here on a Friday night on the way to a football game.
because this tool knew that from looking back. And it’s not always saying, you know, the dates change, right? Friday is not always a Friday year after year, but it does take a good hard look at that data and understand this is football and this is weather. And how does that impact. And so what it did was it gave them a forecast that was anywhere from 10 to 12, 15% better.
And then it gave them the ability to nudge that where they needed it. And it goes back to what Clinton was saying, that it’s a great tool, to to at least get them 80% there and then they can do the last 10 to 20% on the tweaking.
Clinton Anderson, CEO, Fourth
Okay. So Jerry said something that’s a really important insight that I think is worth noting. And that is AI plus the human experience is what gives you the best outcome, right? Because we’ve experimented, you might imagine, across different ways of doing this. Right. Hey, let’s just go with the manager forecast. Let’s go with our customer’s previous forecast plus manager input.
Let’s go with just our Fourth AI forecasting tool by itself. And let’s go with the Fourth AI forecasting tool plus management input. And what we learned is AI plus the human touch, the human experience, is where you get the very best outcomes. Right. And so in a way, that’s one way to help people get less nervous about this.
This tool is not meant to replace you. It’s meant to make you more effective, more productive. Frees up your time from the administrative stuff I’m gonna call stuff that you hate and get on and spend more time on things that matter more to you. Okay. Awesome. okay. I promised, the folks who organized this event, they will not be overtly commercial.
So this is not a commercial point. But sometimes when I’ve been talking about AI in the industry, some of the restaurant companies like, yeah, we’re tackling that. We’re gonna do that ourselves. What are your thoughts on how companies should think about either doing it themselves, building their own AI tools versus partnership with other companies? And if and if you think that partnership is important, how did you guys think about, you know, where to look for that, that expertise?
Jerry Phillips, VP of Technology, Whataburger
Yeah, I think we’ve tried both. I’ll be honest. AI is the buzzword. I think everybody went out there and after it and they were like, let’s go, let’s go build our own, right? We can all tap into the same elements. We can can do this. What we learned really quickly is that’s extremely hard to do. Let’s not let’s not fool ourselves.
The marketing, there’s a lot of hype in that marketing. and it’s, it’s going to take you time, which means you’re always going to be behind. It’s going to be very expensive. and we run restaurants. We’re not technology shops. when you really think about it. Right? We support technology, we roll out technology, we provide technology that drives the business.
But, we quickly shifted and said, okay, what is this? Well, first off, AI is going to become embedded in every piece of software you have in your life starting now. Doesn’t matter if it’s on your iPhone, don’t matter if it’s on your Google phone, on your laptops, on your bank accounts. Everything is going to have AI on it.
And so we took a step back and said, where do we focus? Where’s the most significant part of our restaurant data? And where is that data sitting, and who has that? Who has that data? And that’s when we started talking to, Fourth and companies around what does that data look like and how can you take that? Again, it’s understanding your data flow.
It’s knowing where that data sits. It’s knowing who has access to that data. AI is, think of it as a, right now think of it as an eighth grader. It’s learning, it’s not going to be perfect. But the more data that you can get into it from real time and coming into the closest that you have to a centralized system, which in our case was our back office platform.
we decided that’s where we’re going to go focus and quickly moved out of the mindset of trying to build our own.
Clinton Anderson, CEO, Fourth
Yeah, it’s interesting. Right? Because for those of you who are less familiar with AI and I’m not a technologist, by the way. Right. So hopefully I can translate this because I’m not into the details of how exactly AI works. But the simplest way to describe it for for I think all of us, is the idea that there’s an engine.
That engine is a set of algorithms that’s based on data science, that are looking for correlations and then looking to put forward, you know, insights from seeing look alikes. I’ve seen this before. I think it’s this is what’s going to happen in the future. Now, the algorithms that get developed by technology companies, are very good generally, but they’re actually not the most important thing.
All right. The algorithms engine and there are learning system. Right. Which means they get better over time. Right. Which is mean the standard error on their recommendations based on what they thought was going to happen, looking at correlations versus what actually happened gets better over time. And what actually makes those algorithms really effective is the fuel. Where does that fuel come from?
Do you know?
Data. It’s the data is the data. So there’s two critical points for us to remember as we think about our own AI journey is how do you expose your data cleanly. Right. So that’s one thing you can start talking to your IT teams about is do we have clean data? And I know that’s something that that both these guys have talked about a lot in our meetings with them.
But the second part, too, is one of the reasons why I would make the argument. I recognize I have a vested interest. I’ll take it with a grain of salt. But the reason I think you should partner with a company who’s expert at this is that they are learning from a huge repository of data, right? So Chili’s has 1200 stores.
Drew Brodnax, Head of Operational Finance, Chili’s
Corporate 1600.
Clinton Anderson, CEO, Fourth
1600, total 1200 corporate stores. Whataburger is at 900.
Jerry Phillips, VP of Technology, Whataburger
900 corporate and almost 200 franchises.
Clinton Anderson, CEO, Fourth
Okay, 900 and another 200. So you guys are operating in excess of 1000 stores, right? The data that runs through our systems is about 110,000. Right. And so for example, one of the reasons why the forecasting tools work so well with our customers in the United States is because it’s been already operating for three years in Europe with our customers.
Right. So that algorithms had a chance to learn. And so you want to look at where you can get the best and most fuel, because the more fuel that goes through that engine, the better the system learns, the more accurate becomes, the more relevant the insights will be to your operation. Okay. All right. Drew, how did you think about it in terms of like, AI, I’m sure a topic that you were getting worn out on by folks in the organization, right from your CEO down to store operators.
How did you think about where to start? And you know whether to build it yourself versus partner?
Drew Brodnax, Head of Operational Finance, Chili’s
Yeah. So resources was a big was a big topic conversation. We had a huge you know ERP. We’re moving Oracle here in a week. So that’s kind of all hands on deck. So you know it was really constrained with that project. our biggest focus was being seamless. So we didn’t want we looked at different, you know some some different options that’s out there.
You know, we didn’t want managers to have to go to two different places, and they’re already used to going into the system that they use today. And you know, this this solution, it was it was seamless. So it wasn’t asking them to do anything different, which was huge. So, keep it seamless, keep it one point of one point of contact.
it was not a huge change to their process. So that that really was probably one of the most important things for us is the experience with them was going to be as little disruption to their operations. so that was that was a big piece of us.
Clinton Anderson, CEO, Fourth
Yeah. That makes sense. Right. That just kind of go ahead going.
Jerry Phillips, VP of Technology, Whataburger
Yeah, I was going to say that that is a critical point, I think, is we actually did a test, a blind test, with a couple of our operators and we, we turned it on and didn’t have on. Right. Because they’re used to generating forecasts. They’re used to the system auto generated forecast. And so they’re used to just going in the tool.
And what we noticed really quick was that first off, not how we want to do this, but we did want to truly get the impact. And they didn’t know it because they’re going into the same tool looking at the same data. But what we immediately saw was the 2 or 3 that we did that to three of them, they started spending about 15 minutes less in the tool.
And we we asked why. And they’re like, well, it seems to be better.
Clinton Anderson, CEO, Fourth
I’m making fewer adjustment. Right?
Jerry Phillips, VP of Technology, Whataburger
I’m making fewer adjustments. And so that was a good validation point. Also.
Clinton Anderson, CEO, Fourth
Yeah, I think I heard you guys say three things, right. Which is one is a resource question. Right. Do you have the resources to build a tool? Everyone gets excited like, yeah, you know, I’ve got this outsource team that’s got like ten engineers, right? Like we’ve got over 350 engineers working in our company, right, to do nothing but this.
And so you need to ask yourself, do I have the resources to do it? Two, how quickly can I move along? Right. Not just now, but in the future. Right. Because remember, this isn’t going to stop is the train is going to keep on going. And then third is how do you avoid disruption, disruption of operations? Okay. Let’s change topics.
And I want you guys to be thinking about this question too because I’m going to I’m going to come back to drew and Jerry. But then I’m going to ask you all the same question. And that question is, all right, so AI seems cool. We’ve talked about the impact already on forecasting, but a where are we going to see an impact on other parts of our operation, the metrics we care about and or impacting performance of store operations.
Where do you see AI having impact in the years to come? So think about that question okay I want to I want you to think about your own operation or organizations you work with. And I’ll come to you in a second. But Drew, Jerry, where are you either seeing impact already or where do you think you’ll see impact on store operations and performance?
Yeah.
Drew Brodnax, Head of Operational Finance, Chili’s
So, so being the finance guy and, if I’m going to come out and stand up here and talk to, you know, audience, I’m going to I want to prove it to myself. So I was curious. So we’ve been focused on, you know, accuracy very clear. You know, more forecast accuracy. But, you know, I think everyone in this room knows better forecast accuracy is going to is going to benefit the restaurant.
Right. There’s probably going to benefit labor probably going to benefit guest experience team member experience all these things that we’ll we’ll rattle off. Right. but I want to I was curious. So a couple about 24 hours, 48 hours ago went in, looked at the last couple of months of operations. And what I wanted to look for is, is there a correlation in performance related specifically to forecast accuracy?
So can I tie forecast accuracy back to think comps and things. Waste inventory management even looked at turnover and even Google scores and you know, again if I’m going to get up here and speak to this one, I want it to be accurate and I want to be able to also stay in mind what I’m what I’m going to say is, you know, there is strong correlation.
So with the three some of the drivers. So comps waste is what we call abt what the things that really stood out to me was team member turnover. So we think about you know, the disruption in the experience. And one of the big things that when team members leave us, they talk about, you know, hey, quality of life, are they happy?
Do they enjoy their job. And we know, you know, having if you if you have a really bad shift because you didn’t have a great forecast, you didn’t have the right amount of people on. It’s not a great experience for not only the guests, but especially the team members. And you know, you’re you need your team members. The experience will never exceed the experience of the team member.
So if they’re happy, then they’re going to transfer that to the guest. And so, you know, turnover was a big thing. And social scores all the way down to actually looking at the profit margin of the restaurant. And I can tell you looking across 1100 restaurants, tying it back to when you look at the restaurants that have a better forecast accuracy, they achieve better results.
Looking at their P&L on these items. So it was just really confirmation that something we probably already know, hey, it’s going to translate, but actually being able to speak to it and kind of the proof is in the pudding there. So I think, you know, that was really powerful to kind of see, especially with the turnover piece, because I know that drives a lot of things.
Clinton Anderson, CEO, Fourth
And I look on that topic, it could be a whole bunch of driving factors, right? You know, we’ve seen customers when we sit down to review their performance with our tools, where some customers are struggling with scheduling, you know, some managers who have over 250 changes to the schedule, right? Imagine if you are an employee and you keep getting your chain turned dry, told, no, that’s not your schedule any more.
Here’s your schedule. Oh you can’t I don’t need you for the shift anymore. Pick up this shift. Those changes are super frustrating. Someone is trying to build their life around a restaurant job. Now, the thing is, you think about that forecast, right? I’m not having to spend time worrying about the forecast or making changes to my staffing. How much more time can I spend with my team helping and helping train them?
Or how much more time can I spend ensuring quality guest experience? There’s so many. There’s some. There’s just a multitude of factors that start to impact that. But that’s just one example of where better forecasting through AI is starting to have a trickle down effect across the entire operations. When you told me was impacting social scores on Google and, and, team retention, I’m like, man, this stuff is working.
What’s working already? Jerry? What’s your what are your thoughts on.
Jerry Phillips, VP of Technology, Whataburger
Yeah, I’m going to just piggyback that and take that into the operations side inside the restaurant permanent, where we’re really the next phase of AI for us to go to really start focusing on is those key decisions that need to go to the manager on duty or the operating partner that’s on duty in that kitchen, in that restaurant right now.
We’ve already said we’ve kind of simplified the office side of their job, but now how do we bring that into the next? They going into a system where they already have all the transactions, they have the entrees, they know what they’re selling, they know what labor is clocked in and they know which station that labor is in. And so having the ability, the next piece we’re going to do is start taking the out the the time of transaction at each station in the restaurant.
And so how long are they spending at the make up table? How long is it taking it up the grill and being able to translate that into a metric that the OP can make a quick, immediate decision on to understand not not as a hammer, you know, we don’t want to use it as a hammer, but to go, you know what?
I ended up with two new people on the makeup table, and I just got a notification. It’s 1215, it’s lunch time and our speed is running about 15 to 20s behind, which translates to more traction in the drive through, more front counter. So hey, Mr.. Operator. Miss. Operator, I need you to go over and spend a little more time and use this as a training opportunity.
Do some on the fly adjustments with staffing. to say this is how you can get better. But it’s not only just like I say, it’s not a hammer. It’s used as an opportunity, as a training opportunity, both for the manager to understand, okay, let’s see how that calculated. I need to work that a little different. That’s really just one example of how it frees up and gets the managers focused on what they need to focus to complete that guest experience for them.
That, employee.
Clinton Anderson, CEO, Fourth
Jerry, I love that scenario because I think what it underpins is this idea that AI can help the manager understand what are the 1 or 2 best and immediate things I can be focusing on right. And I think otherwise it’s just really hard. Someone’s been super experienced, right? A 15-20 year manager will know, right? Hey look, it’s lunchtime.
I can see people actually coming in through the front door or parking to the drive thrus too long. I need to go back and see what’s happening on the production line right. But being able to tell a manager, hey, look, we’ve seen this, degradation in performance on this part of your of your, your operation. And by the way, you’re not having to do that math in your head.
Is that more important than dealing with, you know, something that’s happening in front of the store? The machine will tell you, like, go do these two things. Right. And that prioritization of simplifications, I think, where the all the power of AI lies. How about for our audience here? What are your thoughts? Where do you see AI having impact to solving problems or making your your restaurant operations better?
Any thoughts?
Audience Member
There are lots of opportunities, right and correct and to be honest with you on your approach, right. The the goal of AI is to take a particular business process. Right. And there’s a data exhaust typically from that business process.
Clinton Anderson, CEO, Fourth
Yeah, that’s exactly right.
Audience Member
For that data applying an analytic to it. And it can be machine learning but it can be a juristic right. It doesn’t have to be overly complicated when we think about how we’re going to start.
Clinton Anderson, CEO, Fourth
Yeah. Not everything has to be ChatGPT. Right. That’s right.
Audience Member
But but then taking that and then really, if you’re doing a couple things that you’re going to advise that business process, you’re going to automate it. And at the end you can optimize it. And that’s really hard.
Clinton Anderson, CEO, Fourth
That is our we could serve that our third way. Right. Is that optimization.
Audience Member
Yeah. But you know as any operator look at those business processes. And then about I’m so glad you guys hit on that. Like data is key. Yeah. Peter Norway, data scientist at Google, I want you to say, more data trumps better algorithms. It trumps algorithms.
Clinton Anderson, CEO, Fourth
Every time.
Audience Member
But, the follow up on that is.
Jerry Phillips, VP of Technology, Whataburger
Better data.
Audience Member
Needs more data. Yeah. Yeah. Right. And again practitioner in this for the last ten years I will say this is this is conferences where people talk about this. This is the first time we’ve actually heard a real discussion around this and hitting on the real thing. Because the hardest part about all of this, especially in the restaurant industry, is quality data.
Yeah. You know, that’s where that’s where all the heavy lifting is, because we have inflicted so much brain damage in how we manage our systems. You have five different people managing POS systems over the course of two years. Nomenclature all over the place categories are all over the place. We don’t have good governance and controls and how we get data in that system.
Well guess what? You’re no matter how good your algorithms are going to be, your outcomes are going to be garbage. Yeah. You’re getting you’re getting on the here. Yes. Quick question. What where do we see this going?
Jay Altizer, Cheif Operations Officer, Fourth
I just talked about.
Clinton Anderson, CEO, Fourth
This Jay Altizer, our Chief Operating Officer at Fourth.
Jay Altizer, Cheif Operations Officer, Fourth
Circuit in from the back right here. but a couple of things I think you said that are important. One is some of these processes are difficult to tune and manage, and the systems that run these processes can be administratively burdensome, attitude and manage. That’s where we want to go with this is is how have the AI not just given decisions but acted with learned processes and tweaked with the system works so so that the processes and rules themselves are walking in on the best outcomes over time and that administrative effort starts and starts to starts to be built.
So you don’t have to have somebody hunting through your system switches and settings. But the.
Getting out of the forecasting process and really getting into the rules you used to manage. What.
Clinton Anderson, CEO, Fourth
Yeah, that’s spot on. Look, everything you said aligns with aligns with our.
And good data is better than more data. But data is the first important thing. This is area where we can all be working on as we think about how we move forward with AI. Just work just work, just do an assessment.
The second thing that came out of that, right, which is look, you can start quickly, right? Doesn’t have to be the world’s best kind of AI capability. Capability. Right.
New. Right. We’ve been I’ve been doing that for a decade in my jobs. But then the third thing is how do you find that right, partner? Right. Because, look, it’s kind of weird in our space, we’ll be doing research.
Should be copycat language on the website. And so how do you figure out who can really do this? Right. That becomes a really, I think, interesting challenge. And.
What worked what didn’t a lot of conversations with peers in the industry around what their experience had been can save you some, some pain and and hopefully accelerate.
That, by the way, brilliant. The discussion here is very much appreciate the quality of the conversation. Given all your experiences, what experience? I was wondering, could you.
What doesn’t it do well?
Jerry Phillips, VP of Technology, Whataburger
What do you need to be more careful about how you. Yeah.
Clinton Anderson, CEO, Fourth
And in fact, that’s what it’s going to be. My last question, which ist here’s several.
Jerry Phillips, VP of Technology, Whataburger
Our data is still pretty disparate. Right. and two, like, I said earlier, and we’ve all said everybody’s trying to build this right now, and it’s in every tool. And I think as operators and as people in our restaurants, this is where, I worry that, you know, you hate to use the term governance, but I worry a little bit about the governance and as, as, as a business, this is a chance that technology has to be aligned with the business.
And this is where I get a little worried every once in a while. Right? Because the businesses are not technologists. They don’t understand that when we say, yeah, you got to have clean data, right? They kind of go down the old world. I have all this data. Can’t somebody just organize it and make it tell me something? Yeah.
Clinton Anderson, CEO, Fourth
How hard can it be for my company to, for whatever it is. Yeah.
Jerry Phillips, VP of Technology, Whataburger
And so I think one is that alignment and, understanding. I think the other thing kind of along those lines. And I was going to tap this onto the last conversation a little bit, is don’t tackle it all at once. You see a lot of people trying to tackle everything in the restaurant at one time. Pick your top two.
Clinton Anderson, CEO, Fourth
I can’t emphasize that. Strong enough, right? Find 1 or 2 proven use cases. If you’re the only person thinking about doing something right and no one else is talking about it, you should be a little bit nervous, right? But there’s a lot of people who are already months, sometimes even years down this kind of AI, and we use AI to cover a lot of things.
But it’s a point you raise, right? Like there’s some basic machine learning and tools that can be applied that can significantly improve operations. Start there. Right? Clean up your data. Involve the manager. Right. If there’s three takeaways from how to do this, it’s so measurement operators include your operators agree on a use case that you know will add value right.
Start simple with the best data you have and prove it. Because once you have that prove case that use case with with your operators, your CTO is going to see that finance guys are going to see and they’re like, well, we want more of that. And the finance guys are going to show, like when drew pulled up that that chart right showed this strong positive correlation on all these other metrics from the stores that were using the AI forecasting most effectively.
Like you can imagine having that slide in front of your board, right, and trying to get funding for, I think we think we can get more money for more of that. Like, yeah, every day of the week. And so I think that’s a simpler but pragmatic approach around where do I have good enough data, what’s the use case make sure I’m involving the operators so that they can be the person who’s validating this.
Not me saying it worked. That’s the best I think. Great place to to start. And I don’t know if you have any other comments on what do you worry about or what are the challenges ahead.
Drew Brodnax, Head of Operational Finance, Chili’s
Yeah. So you know, for, for for us it’s really about trust. And you know, you know obviously you want turnover to be, you know, as low as possible. But you naturally are going to have turnover. And so you have you know new managers come in and I think, you know, you’ll have some people that may have a may have a bad day with forecasting.
And it didn’t. And so then they may start to challenge, hey, you know, is this is this a good tool. You know. And I think sometimes people lose fact of perspective of where they came. So you may be actually running some of your best accuracy ran in the last two years. But in your mind it’s not accurate. And you lose where you were last year.
And so, you know, helping them and then finding the sweet spot. You know, we’ve talked about it. But for me it’s, you know, keeping the human element in Clinton. We’ve talked about this about, you know, the tool is working for us. We do think there’s some opportunities of, hey, how do we how do we feed it more? How do we help it more when we do things that aren’t that are specific to our business, that aren’t in a history, aren’t in the history that we know we’re going to be doing, that’s going to be impacting how do we help the machine with those type of insights is really kind of where we see it.
So, you know, to me it’s keeping the human element as a tool in there and then the trust constantly just helping people trust the tool. Because when we moved to AI forecasting, we had managers that were so used to they kind of own the entire forecast because they built it kind of from the ground up. And now you have this black box that’s giving them this number.
And don’t, you know, maybe not understanding exactly how it came to be, but over time, you know, you get repetitions and you get trust. And then they start to kind of put their spin on things, and then you kind of reach that sweet spot in that happy medium.
Clinton Anderson, CEO, Fourth
So yeah. One other point I’d add to that. Right. Which is where are we at on that journey. Right. So if you think about those three elements of what I can impact, which is automation of administrative tasks, right. real providing better insights into performance. And then third, optimization through making recommendations in terms of being able to, personnel provide kind of real time data and insights, I would say we’re, you know, on many aspects of the business in the seventh or eighth inning out of a nine inning baseball game.
So we’re kind of 7 or 8 out of nine on that. On the ability to automate on certain things. Again, completely there on other automation early on. So kind of middle of the pack. And I think we’re still at the very early days is in terms of that optimization. Right. Being able to say how do we optimize. Right.
So we’ve optimize. We started first with forecasting and I’d say they’re we’re at eight or, you know, 8 to 9, right. Seven or something out of nine. But on other parts, right. We’re getting to the point on who should be on which shift. Right. And how do you team up with certain team members. And you know, how do I think about inventory optimization and using AI?
We’re still early days, right? We’re second and third inning there, right? So doing a lot of work on it. But it’s not it’s not fully materialized in the marketplace yet. But that is where that, that that’s where I think this this industry goes. That’s where I goes in our industry in the next kind of ten years time. So yeah, I think we’re out of time.
let me thank again, drew and Jerry for their fantastic contribution and input.
And thank you all for being here.
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