December 24, 2025 • 43 mins
Join Paul Barron and Cherryh Cansler on Fast Casual Nation as they dive deep into AI's transformation of the restaurant industry with Kerry Leo, VP of Technology at Shipley Donuts, and restaurant tech consultant Paul Molinari. Discover how Shipley achieved 24% higher average order values through AI-powered ordering, learn why traditional Google search is becoming obsolete, and understand how data unification is creating the "single pane of glass" operators need. From voice ordering in mobile apps to agentic AI solving integration challenges, this episode reveals practical strategies for implementing AI in your restaurant operations. Whether you're just starting your AI journey or looking to accelerate adoption, this conversation provides actionable insights on everything from choosing the right tech partners to measuring real ROI.
00:00 - Why AI in restaurants is hitting a turning point
01:44 - Shipley Donuts launches AI powered ordering
02:39 - AI boosts average order value through smart upselling
04:59 - The exact moment Shipley committed to AI
07:01 - How AI mimics top performing cashiers
11:42 - Voice ordering and mobile app AI roadmap
16:42 - OpenAI vs Google Gemini and the AI platform battle
25:45 - Domino’s AI case study and massive efficiency gains
#RestaurantTech #AIinRestaurants #FastCasual
Become a supporter of this podcast: https://www.spreaker.com/podcast/fast-casual-nation--3598490/support.
Get Your Podcast Now! Are you a hospitality or restaurant industry leader looking to amplify your voice and establish yourself as a thought leader? Look no further than SavorFM, the premier podcast platform designed exclusively for hospitality visionaries like you. Take the next step in your industry leadership journey – visit https://www.savor.fm/
Capital & Advisory: Are you a fast-casual restaurant startup or a technology innovator in the food service industry? Don't miss out on the opportunity to tap into decades of expertise. Reach out to Savor Capital & Advisory now to explore how their seasoned professionals can propel your business forward. Discover if you're eligible to leverage our unparalleled knowledge in food service branding and technology and take your venture to new heights.
Don't wait – amplify your voice or supercharge your startup's growth today with Savor's ecosystem of industry-leading platforms and advisory services. Visit https://www.savor.fm/capital-advisory
00:00 - Why AI in restaurants is hitting a turning point
01:44 - Shipley Donuts launches AI powered ordering
02:39 - AI boosts average order value through smart upselling
04:59 - The exact moment Shipley committed to AI
07:01 - How AI mimics top performing cashiers
11:42 - Voice ordering and mobile app AI roadmap
16:42 - OpenAI vs Google Gemini and the AI platform battle
25:45 - Domino’s AI case study and massive efficiency gains
#RestaurantTech #AIinRestaurants #FastCasual
Become a supporter of this podcast: https://www.spreaker.com/podcast/fast-casual-nation--3598490/support.
Get Your Podcast Now! Are you a hospitality or restaurant industry leader looking to amplify your voice and establish yourself as a thought leader? Look no further than SavorFM, the premier podcast platform designed exclusively for hospitality visionaries like you. Take the next step in your industry leadership journey – visit https://www.savor.fm/
Capital & Advisory: Are you a fast-casual restaurant startup or a technology innovator in the food service industry? Don't miss out on the opportunity to tap into decades of expertise. Reach out to Savor Capital & Advisory now to explore how their seasoned professionals can propel your business forward. Discover if you're eligible to leverage our unparalleled knowledge in food service branding and technology and take your venture to new heights.
Don't wait – amplify your voice or supercharge your startup's growth today with Savor's ecosystem of industry-leading platforms and advisory services. Visit https://www.savor.fm/capital-advisory
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome back to another episode of Fast Casual Nation, the
podcast that started at all you guys know the drill.
We are here doing a special podcast today and of
course joining me is MS Karen shrek Canceler.
Speaker 2 (00:12):
How are you, I'm great Ready to nerd out over
all things AI with you guys.
Speaker 1 (00:17):
Yeah, well, we've got the topic of AI and tech.
We will have on two amazing guests that will be
I think revealing some of the most amazing ideas that
you have ever heard around new tech and AI in
the restaurant segment. So we're going to be cutting into
all that stick around right here. My name is Paul Baron.
(00:54):
As the early pioneer in fast casual I've seen the
industry evolve from just a few operators the most sought
after segment by consumers around the world. Now we're planning
to shape its future. Tap into decades of my expertise
identifying the emerging brands and tech winners in the space
(01:15):
saber capital. We'll be fueling the next generation of fast
casual innovation. All right, we are back here on the
Fast Casual Nation podcast. If you guys are not subscribed
to this show right now, all I ask for you
to do is as you're listening, just go over to
Spotify or Apple Podcast, hit that little subscribe button, give
(01:37):
us a star two. We love the five star version
of it. And then of course if you're watching this
on YouTube, make sure I just subscribe here to save FM. Listen, Cherre,
you guys had a tech story that I'll show right here.
That was your most recent news on Shipley who is
going to be joining us today Shipley Donuts, and they've
(01:57):
really started to integrate a little bit more. Explain to
me what's going on with them before we bring them
onto the show.
Speaker 2 (02:05):
So they recently ruled out an online ordering system with
Savory SAI v o Ry not to be confused with
the other Savory, and it looks pretty cool. I was
playing around with it this morning actually, And you can
just go on there and type in I want to
order an eight am catering order tomorrow for thirty people,
and then it brings up it gives.
Speaker 3 (02:25):
You all the suggestions reguidations.
Speaker 1 (02:27):
Yeah, it was great, Listen, this is something I need.
Customer used AI features on twenty two percent of the orders.
That's I wouldn't say that's high, but that's pretty good.
Speaker 3 (02:39):
I mean then we're just now rolling it out. I
think it's pretty Yeah.
Speaker 1 (02:42):
AI assisted results in twenty four percent higher. Now this
is important right there, higher average order values. We got
to talk about this with Carrie. That's pretty significant. And
then of course them doing this I think in general
just shows the direction of a lot of these brands
going kind of this next level. Are you seeing more
brands now really starting to jump in because this time
(03:06):
six months ago I wasn't seeing as much engagement. What
about now?
Speaker 2 (03:10):
Yeah, I think we're we're picking it up. Pepe brands
like Shipley, you know, they're coming on. They're not just
on their own anymore. So it's just it's the beginning
of the explosion.
Speaker 1 (03:23):
Finally it begins. Well, listen, speaking of explosions, we got
to bring in two explosions, and of course one of
them we will bring in and that is mister Paul Molinari.
How are you, Paul?
Speaker 4 (03:36):
Hey, how you doing?
Speaker 5 (03:37):
Baron?
Speaker 1 (03:37):
Nice to see you, man. We're going to bring you
in on the On the Fast Casual podcast. You're you're
an old hand at the Restaurant Masterminds podcast.
Speaker 4 (03:45):
But now I'll tell you this is like this is
the big boy table. You know this is this is
a grown ups table. I love being here with you guys.
Speaker 1 (03:54):
This is good. That's good. I love it. We also
have Carrie Leo coming in from Shipley, who is who
we were just talking about right there making headlines. Carrie,
welcome to the show.
Speaker 5 (04:05):
There's a first for everything, right, you know, carry making headlines.
Glad to be here.
Speaker 1 (04:11):
First, let's get into a little bit about the topic today. Really,
we're going to dive into everything from drive through fails
and wins to pizza prefecttion. We'll talk a little bit
about that. Real story of AI in restaurants is our
focus today, along with you know, kind of this next
generation technology. But I believe we are at a pivotal
point right now where AI is tech and tech is AI.
(04:36):
We've come that far with so many of these large
language models. Carrie, first question to you when you look
at what we're dealing with right now, because I think
the number was seventy nine percent of restaurant operators have
either implemented something around AI or are considering it right now.
What was the this is when we're going to go
do it? Point for you? When did that happen?
Speaker 5 (04:59):
Well, we've been looking at this for a couple of
years now, and you know, it's a new technology. We
weren't quite sure how to leverage it from a consumer
facing technology. We were using it in our back office
system to do some inventory things and you know, calculations
and automation and prep and prediction what to make our shops,
(05:25):
especially our new ones as we expand out into new territories,
need help with what to make because there is a
new market. But for this purpose, we did partner with
Savory company that has been doing this kind of in
the commercial real estate business, and they had a cool
(05:47):
idea that has not been really widely known in the
restaurant business as far as like an online ordering agentic
AI experience where it's actually learning through data based on
what people are ordering. So we spent a good bit
of time gathering data first and then we built this
(06:10):
out with them, and we we helped piece everything together.
Their their agent or the AI tool was really a
form that you kind of went in and did a
catering order at another brand they had started with, but
with us, we had to go full circle and integrate
it with all of our online ordering and are you know,
(06:31):
is coming into the pos and how hard was that
it was It was not too bad. We had some
really good partners with Olo Patronics, and then these guys
just they were really easy to work with. We made
this happen. And then what was really neat is we
patterned it all after the best cashier I've ever known
(06:55):
back in my Chick fil A days. They just the cashiers.
They're phenomenal as all might ye see yourselves. But we
wanted to incorporate all those great attributes of suggestive selling.
Auto boxing for donuts was a big thing because previously
it was one of our problems. When we looked at this,
we wanted to look at all the problems. There was
(07:16):
a box they had to click through twelve times and pick,
jump around and pick donuts and drop it into a box.
Now they can just pick their donuts like they're in
a shop and drop it into the basket and it
auto boxes and recognizes that it has twelve donuts or
six if you fall short. If you hit eleven donuts,
so go hey, for another dollar, you can have a
(07:39):
dozen and get a discount.
Speaker 4 (07:40):
You know.
Speaker 5 (07:40):
So we're trying to pattern it after real people and
make that experience great for the consumer.
Speaker 1 (07:47):
Where would you say, on a scale of one to ten,
you guys are as a brand in terms of implementing
AI right now?
Speaker 5 (07:54):
On scale of one to ten, I would say we're
probably five. I you know, I don't think we're at
the helm of it, you know, but we we're definitely
leaning into it and we've got a lot of things
on our roadmap coming for sure.
Speaker 1 (08:09):
Hey, Paul, are you seeing I know you're working with
a lot of tech companies, Yeah, right now, most of
which some of which we'll talk about in today's episode.
But what are you finding right now from the operator side?
Are through the tech you know, vision of what they're
trying to solve?
Speaker 4 (08:26):
Are they that I love what Carrie how Kerry describes
their approach. They're trying to solve a problem. And I
think that these AI companies that are very specific in
the types of problems that they're solving may not necessarily
be customer facing, but rather operational or something, you know,
(08:50):
something It could be something as simple as getting just
improving procurement for example, getting invoices through, or something to
something a lot more ambitious, like say, I imagine being
able to talk to your restaurant, like Kerry, Like imagine
(09:11):
being able to talk to Shipley Donuts in the brand
and saying, you know, and tell me, how are these
locations in East Houston doing, you know, literally having a
verbal conversation and bringing all of that information back to
you in a format that's easy to understand, easy to share,
and brings it down to a level where everybody's on
(09:33):
the same page. I think the goal for many of
these technology companies is trying to create this single pane
of glass. I think that's something that we're going to
see a lot more of where we're integrit we're ingesting
all of this data, and we're then we're going to
after we ingest it and clean it, we're going to
(09:54):
then make it available to whomever needs it, so everybody's
on the same page.
Speaker 1 (10:00):
Our case study today was especially on the drive through
side is McDonald's. They did an acquisition back in twenty nineteen.
This was in reference they partnered with IBM later, but
a priente I think was the company's name. Basically it
was an AI voice ordering tech and I think it
was very early. Obviously back in twenty nineteen, five years ago,
(10:22):
we have come a long way. SHERA are you when
you look at the trials that we're seeing in restaurants
today versus what we saw back in twenty nineteen, which
were kind of our missteps. Are you seeing a lot
more successes like what Carrie's doing or what's the scale?
Speaker 2 (10:40):
Yeah? Yeah, they're better, I mean, especially especially at the
drive through. I mean I gave a keynote last year
about you know, where we are in fast casual industry,
and I had a whole bunch of examples of fails,
so like Wendy's McDonald do you know, like all the
just and you're not seeing that nearly as much.
Speaker 3 (10:57):
I think we're getting there.
Speaker 1 (10:59):
So yeah, yeah, what about accuracy? Are you are you
guys scoring accuracy Carrie at all? I like to determine
how the customer feedback is coming in.
Speaker 5 (11:09):
Yes, we definitely do that. We've got a couple of
different ways to do that. We're also looking at tracking
that on the as far as the website goes and
the ordering for us, you know, with the type of
AI and the type of model we're moving to, We're
we're doing server side rendering and a better tracking of
we're guests or clicking where they're falling off where they
(11:32):
drop off. We're also on the McDonald's note, we're also
looking in a couple of ways to implement voice in
our mobile app. Next right, so we've done our website,
We're about to release the new version of our mobile app,
which will include voice ordering, so they can be just
driving down the road and go, hey, Siri, I want
(11:53):
to order Shipley, you know, and they can just go
right through it. We're also looking at that with a
drive through as well. We know that there's a lot
of testing. It's got to go into the drive through
piece sell it.
Speaker 1 (12:04):
I've noticed these AI or APIs have really started to accelerate,
so we're seeing a lot of these kind of things.
Here's a question for you, Paul, that kind of is
a bit of a counterbalance. Do you think that we
are going to get so good on either the AI
APIs for some a brand like Kerry to where they
(12:26):
could just go out and implement all of this stuff
on their own kind of with their own lms.
Speaker 4 (12:32):
What do you think I think that there's and maybe
sure it can help with this too, because I feel
like it's always the same old gatekeepers ya as, It's
always as it's always been. It's you know, how listen
this data has to be accessible. You know, agentic AI
is only as strong as its weakest connection. You know,
(12:54):
you these things have to work together in order for
things to happen. So I think that, yes, I believe
that the APIs are going to get stronger and that
access is going to be more open, But I still
think there's still a lot of hesitancy with certain technology
companies making that possible.
Speaker 1 (13:16):
Well, on the POS side, are you finding, Kerrie that
the point of sale guys are okay with integrations coming
in from API because essentially you're trying to go direct
to POS? Is that right correct?
Speaker 5 (13:31):
Now? The key there is we already had a good
integration with OLO, the online ordering provider, so that's where
all of our digital stuff is being aggregated and then
is pushed into the point of sales. So we already
had that integration. But there are a lot of POS companies,
you know, when we're doing RFPs and talking to them,
(13:52):
they're starting to lean heavily into building out AI solutions
as well.
Speaker 4 (13:57):
Yeah, there seems to be you know, there's this new
uh you know, POS companies kind of want to create
headlines for themselves every couple of years, and the new
one is now how they're all pivoting towards becoming these
AI platforms.
Speaker 1 (14:12):
Is that real?
Speaker 5 (14:14):
Well, you know, it's we'll see.
Speaker 1 (14:18):
I'm just gonna ask, you know, I just blurt out
stuff most of the time.
Speaker 4 (14:21):
We'll see if they can pull it off. I mean,
you know, if if the if the pos will is
and always will be sort of the heart or the
hub of the restaurant tech stack. You know, they have
a head start, but at the end of the day,
they have to execute on all of these different actions
that they may not have the expertise frankly to pull off.
Speaker 1 (14:45):
I'm concerned about that because I'm watching the tool sets
that we use in a lot of our analysis business
on the finance side of things, and the amount of
uh I won't I won't say, well, yeah, well they
the tools that we're using right now across I'd say
four different AI platforms is literally supplanting a lot of
(15:09):
the old tech that we were using prior. And it's
like happening so fast. I'm talking to six months, and
it's likely that this is going to come down to
three months, maybe ninety days in just a very few
I think, just a very few months away from getting
to that level we share. Do you think we're at
(15:31):
an inflection point yet for the restaurant industry or do
you think it's still a ways out there.
Speaker 2 (15:37):
I think for everybody it's still a little ways out.
But I mean, obviously we're getting closer. But some of them,
you know, you're seeing it happening. I mean you're seeing
the predictive, the suggestive selling, the up selling. So I
mean it is working. I just it just takes the
restaurant industry a long time, as you guys all know,
to catch up. I think with tech compared to other industries.
Speaker 1 (16:00):
Carry what what ll M did you guys end up
using for for your platform?
Speaker 5 (16:04):
I think our platform. If I'm not this is a
good savory question, but uh, next days react and we're
using there some some of their clutch they got okay,
all right on it.
Speaker 1 (16:20):
So I think that's direct. I think that's direct into anthropic,
which is intriguing because they have one of the bigger
APIs that I've seen that has started to roll out
into some of the food service space. Paul, have you
been watching the most recent uh stuff with what Google
has been able to do with Gemini?
Speaker 4 (16:41):
Because it tell me because there's there's so much. There
is so much.
Speaker 1 (16:45):
So what's happening is there is a war going on
right now between open ai and Google. Yes, and open
ai was a first mover and then all of it.
Remember that Google was on the precipice of getting split up.
They were going to take Alphabet and start divvying it out.
So YouTube was gonna I mean basically when I worked
(17:07):
at Microsoft, this is what we had to live through
of getting basically anti trust nailed on us or we
had to slow down all of our production. But the
judge recently came in and said that their search business
is now at it has an existential threat, which is
open Ai. So open ai basically saved Google from getting
(17:31):
split up.
Speaker 4 (17:33):
Ah, look at that. So you're helping hand from your
from your art from your competitor.
Speaker 1 (17:40):
Yeah, which could have opened them up for where they
are right now with Gemini because Gemini has really accelerated.
I don't know if you guys are testing these models
as much.
Speaker 2 (17:49):
Yeah, I am ad user of Gemini. It's my it's
my favorite one. I use it constantly all day long.
Speaker 4 (17:55):
By five, I think, you know, one of the things
that I think I find fascinating about these different llms
is that there are different use cases for each one,
like you may use one in your personal life and
one in your work life, and then one specifically that
you like as a shopping tool.
Speaker 1 (18:11):
Well, one thing about about open ai that I have found,
and I'm wondering, Kerry, if you guys are finding this,
because when you look at the lms from open Ai,
what I have found is it gets harder and harder
to get data out of it. Because it's so I
think they themselves. When I'm talking about the development team
over at open Ai, they're starting to be so concerned
(18:35):
about delivering pure accuracy where Gemini Anthropic, which is Claude Perplexity,
the big four kind of that are circling right now
seem to be delivering more accurate and more timely information.
So I'm concerned open im may end up falling back
behind in terms of development. So are you guys seeing
(18:59):
that right now in terms of accuracy levels? Are they
continuing to get better and better?
Speaker 5 (19:06):
Yeah? They are for us. I mean we use Jim
and I that's part of this whole solution. But you
know where we're headed. It's very interesting. I don't know
if you heard with chat GPT on this subject, right,
A marketplace is opening up in there. Door Dash is
already going to be in there. The way we built
(19:29):
with our partnership with Savory, our new platform is it's
ready to go into that as well. So you know,
AI is going to be talking to AI and you
know all that kind of stuff for too long. And
what we think is in the next couple of years,
it's going to change the way people search Google. You know, the.
Speaker 1 (19:50):
Regular I think search is done.
Speaker 5 (19:51):
Yeah, yeah, this is going to be the new frontier.
Speaker 1 (19:55):
Right, it's done.
Speaker 4 (19:57):
That's right. So there's you know, there's search engine optimization
and now there's answer engine optimization an oh uh and
you have to be you have to be baked into
those uh, into those lllms.
Speaker 1 (20:09):
Now, yeah, well, and I think this is the thing
for brands because in reality, you've got only a handful
of search pools you go into. You've got Maps, which
is really built around Google that has all had all
sorts of layers that I think is going to get
even better, especially with Gemini starting to integrate to Maps
because that's on their on their roadmap, which is good
(20:32):
because if you've got AI enabled menus that's the plug
in probably the natural fit for Google Maps to kind
of control that. But outside of that, are you guys
doing anything on that UH front carry in terms of
dealing with consumer search in the future or discovery.
Speaker 5 (20:52):
Yeah, not not just yet. I mean other than what
I've what I've shared, We're starting to delve into that
little bit. But I think you know, back to your
data question. Also, we're using AI for this too. We
have a Snowflake environment, So all the data that we're
pulling from our website, from our pos, from UH transaction
(21:15):
level detail, everything's feeding one source. And now we're building
out the AI solution within snowflake to do those things
where I think one of the Paulls said, you'll just
be able to ask a question and like, hey, why
is this shop sales different on Tuesday at seven thirty
(21:38):
one pm compared to last year or two years ago
or whatever, and it's is you know, we're getting close
to that as well.
Speaker 4 (21:46):
That's right, Kerrie. I love that because what you're talking
about is that unification, right right, that data unific You're
ingesting all of this data you're putting inside of your
snowflake environment and you're unifying it unifying, it becomes essentially
one secure platform. And the beautiful thing about that is
it then it cleans it because it's able to kind
(22:08):
of recognize its pattern recognition, right, and it can it
can elevate those anomalies and then clean them to where
they thinks it should be. And now all of a sudden,
your accuracy is going up and you're able to execute
much faster. And that's what I was talking about earlier
with this like single pane of glass idea, where all
(22:31):
of these different insights and these reports and these alerts,
all of these things are able to bubble up now
and not just be like, Okay, this is what's happening,
but it can also now prescribe an action. And that
could be right at the store level. It could be
(22:53):
you know, sending an alert all of a sudden now
to your store manager saying, hey, guess what this is happening?
Speaker 5 (23:00):
Fix it?
Speaker 4 (23:01):
Well?
Speaker 1 (23:01):
And I guess the key there is is how much
data goes in a good example of this one I
got to share this. I think this is a good
comparison of how to analyze problems inside an organization where
AI has capability of looking at it. And of course
I have to share this right here, which is the
(23:23):
struggle of the Kansas City Chiefs.
Speaker 2 (23:27):
And we have Carrie on who is from Houston, who
lives in Houston.
Speaker 1 (23:32):
I know, I know, Carrie is the Houston I guess
this is major injury crisis, offensive aggression and turnovers, questionable
coaching decisions, super Bowl hangover. I'm just thinking if you
could apply this to a football game. Imagine what you
could do if you actually funneled real data in from
(23:52):
a restaurant.
Speaker 4 (23:54):
Yes, it's bananas. Yeah, I'll do to take more than anything.
Speaker 3 (24:00):
Yeah, never, I think he was tired. He dropped two passages.
Speaker 1 (24:04):
The question is, Carrie, are you guys funneling that data in?
Speaker 5 (24:08):
Yeah, it's great, great question, Paul. So again we're we're
at the cusp of that. I just met with our
CEO last Friday about this topic and we wanted to
be able to coach our shops. Our gms are frontline
folks as well as our bove story and corporate people.
(24:30):
But you know, the all the data we have. We
have labor data coming in all day long, we have
sales data, we have transational data, we have product data.
All of this stuff is feeding one source and so
we want to build it out where it's actually instead
of somebody having to run a report or look at
a dashboard, have a screen or an audible signal, or
(24:53):
somebody even you know, hey, you might want to think
about cutting back your schedule today, you know, sales are down,
or you might want to make more strawberry ice donuts
because you're getting low and compared to last week, at
this time, you've sold you know, ten times more than
you have available. Now, you know, it's all those kinds
(25:14):
of things we're looking at. So we wanted to coach
real time almost throughout the day. We think that would
be a big game changer.
Speaker 4 (25:23):
Absolutely, all of these different data overlays right where you're
able to now ingest everything from weather reports to you know,
local events that might be happening, all of these different
ways in which you can then overlay and supplement or
augment this data to be just become that much more
powerful of an insight engine.
Speaker 1 (25:45):
Yeah. Well, and I think you look at the models
that are working because this is this is the thing
that I know operators are trying to say, Okay, well,
who's got it right, who's fixing it? I was looking
at one of the case studies we put on the
sheet Today's Share and this was Domino's obviously pizza marketplace.
You guys run that. So when you look at that
(26:06):
kind of success story that Domino's is, this is in
reference to their AI demand forecasting and inventory. Basically it
is what it boiled down to. But I was looking
at one of the notes on it. Here it said
the system reduced training time from sixteen hours to under
one hour. That's crazy, one.
Speaker 3 (26:26):
Yeah, and the.
Speaker 2 (26:28):
Crazy, and then you know it improved forecasting accuracy from
seventy five to ninety five. I mean, those are those
are crazy, those were crazy marks totally worth it.
Speaker 1 (26:38):
Why so do you think why aren't we seeing more
brands doing this? I don't understand this. It seems like
this shouldn't be mean its price.
Speaker 2 (26:47):
It's price. I mean I don't I can't tell you
what it is, but I'm sure it's not cheap. And
also I think people don't know what they don't know,
and they're kind of scared to dive in.
Speaker 1 (26:57):
So it's just early stage.
Speaker 4 (26:58):
It's definitely price. And when you're talking about Microsoft, you
know Microsoft doing a deal directly with Dominoes. How many
brands can actually pull that off. The other thing is
that they are taking sort of this all in one approach.
It feels like they're putting in a lot of eggs
in the Microsoft basket, where other brands, perhaps like Shipley
(27:19):
or others, you know, can look at different types of
points solutions. You know, it can probably do everything that
Microsoft three sixty five, Dynamics three sixty five can do,
but it does so more piecemeal, like I use this
company to solve this problem. I use this company to
solve that problem.
Speaker 1 (27:35):
But then you run into the problem that we have
had in the industry for a long time, and that is,
you know, stuff doesn't talk to each other.
Speaker 5 (27:43):
That's there, Well, there you go now.
Speaker 1 (27:45):
But at the same time, what you know, Carrie mentioned
earlier was the fact that agentic AI may solve that.
If you've got an AI agent that simply just starts
conveying this, you no longer need the web hooks and
API protocols to basically talk to two different systems. Are
the solution providers you're talking to, Kerrie, are they even
(28:06):
mentioning that kind of concept yet?
Speaker 5 (28:08):
For Yeah, yeah, they're already talking about it. And you're right,
we've done a lot of growth over the last almost
five years since I got to Shipley, A lot of
technology has been implemented, implemented here at this ninety year
old brand that really didn't have a lot of technology
(28:28):
when I got here. But what we did is we
did a lot of research with making sure they had
strong APIs and the companies we selected were already talking
to each other. We did need point solutions of back office.
Synergy Suite is a really fine built from the ground
(28:49):
up product that has all the areas of running the
business at the shop level or restaurant level that integrated
with a in a sell, we're pulling BI data out
of it into snowflake. We're pulling BI data out of
the point of sell. Olo was the above store digital
(29:10):
aggregator that helped with our delivery service providers and online ordering.
They have a great API, but they're all looking at
this stuff and synergy, you know, for a prep and prediction.
They've already got that AI built in, so it's already
looking at all the data coming into that system and
(29:30):
actually suggesting building out a dashboard for the next day
or the week, looks at six weeks worth of data
by hour, and it tells you what to make. So
a lot of that fall was placed, you know, early
on as we selected them, and then now they're all
heavily trying to make you know the API and or
(29:54):
or actually the connections a lot better.
Speaker 1 (29:57):
Yeah, I won't I won't bring it up in today's call,
but we we've covered this quite a bit over on
some of our other channels. Is agentic to a gentic
payment that eventually is going to be some sort of
digital you know, payment architecture that will eventually be moved
into I think the restaurant space with stable coins. They've
already kind of gone in this direction. Once we get
(30:19):
some regulatory alignment out of that, it's probably going to
open this up for digital payments to move much faster
and not have to deal with you know, plus one settlement,
which is what you have to deal with right now
on a lot of the merchants. So that's something we'll
talk about in some upcoming shows. But one question is this,
and I'll ask this to you shareff for right now.
(30:41):
I know you talk to a lot of suppliers in
the industry. Is anybody talking about data like data centers
for just the food service industry?
Speaker 2 (30:53):
No, I mean not that I'm not that I'm aware.
That doesn't mean it's not happening, but I haven't heard
it yet.
Speaker 1 (30:57):
Because you've got a lot of these fails that have
happened here recently. Even we were affected by one just
a couple of weeks ago with Riverside. I mean we
had outages because of data centers going down, you know.
So I'm just curious if that's going to become maybe
a industry specific solution so that you could avoid, you know,
(31:19):
maybe some of the traditional problems that the bigger areas
could face. Have you heard anything, Paul.
Speaker 4 (31:26):
No, I haven't. But one interesting thing is, though, is
I keep hearing Snowflake coming up as kind of like
this data center of the warehouse of choice. I'm not
sure if that is more coincidental.
Speaker 1 (31:38):
What was your research carry on Snowflake? What was it
that drew you to them?
Speaker 5 (31:43):
Just the fact that they were already delving into AI,
and they're a pretty mainstream player out there as far
as BI and cloud based, easily integrated to with like
five Tran and ma till In and of these other
e lts that come in, so uh, I mean, they're
(32:05):
just they've been around a while, and and I think
on that note, you know, as far as risk mitigation,
you want to make sure whoever you're selecting is it is.
They've got a distributed model and lots of backup and
different data centers, so if one Trump goes down or
something like that, you're not down right.
Speaker 3 (32:23):
When you were building this out, did you have another
restaurant brands in mind that you wanted to kind of
you were like, this is a very good one.
Speaker 2 (32:32):
I want to I want to make it like this
or were you kind of starting from your own problem
solving use case.
Speaker 5 (32:38):
Yeah, we just kind of started from scratch and did
our research and talked to people. But they they clearly
and and this is not a paid endorsement, but they
they clearly have a really good product that's easy to use.
Our Nick next door to me, our BI guy, I mean,
(32:59):
he's been in there, uh, doing all sorts of amazing
things with this data, and and it's easy for it
was easy for him to learn.
Speaker 4 (33:10):
Hey, Carrie, do you I'm curious are you guys looking
at AI to solve more operational issues or to be
more client customer facing increased sales? Is it you know
that that balance?
Speaker 5 (33:27):
Yeah, Yeah, that's a good question. Uh. I think originally
it was more operational. Uh, and we we kind of
are a little more seasoned there, you know with some
of the things we're doing operationally. But now you know,
again through this partnership with Savory, we've we kind of
(33:49):
delved into hey, this this could work for a guest
facing thing with some and it's not a chatbot or
anything like that. I mean, this thing is learning based
on the data based want people people that don't want
to do the ordering. So for instance, we have a
lot of big companies that would call us this is
(34:12):
one of the problems and they say, hey, I got
this meeting, I don't know, I don't know what to get.
You know, here's my budget. So we just kind of
built that into a tool for them, you know, so
then they just started ordering, right, So now they can
put I got a meeting for fifty people, I just
want donuts, and it'll in ten seconds, it builds an
(34:35):
order for that many people with just donuts, or if
you don't if you're not specific, it'll say donuts and Colachi's,
or it'll all add coffee. You know, assuming that you're
having a meeting, you want coffee. So but it's also
smart in that it's not a chat bout it. It's
actually look, it's talking to Olo and go, okay, what
(34:56):
items are available. It's particular shop they're looking at. Uh,
they don't have coffee there, or they don't have this
doughnut there, so it won't offer that up. So we're
trying to think about the guests experience. But yeah, that
I think we're to answer your question, Paul, we want
to do it on all fronts, but this was the
(35:16):
first big opportunity we had to take.
Speaker 1 (35:18):
This is the one you could really measure right to sales,
you know, if you if you're increasing sales right throughput
I mean I could see it on the backside. You
know the example we just gave with Dominoes where they're
improving their you know, their training, which is a hard cost.
Don't get anybody wrong, it's there. But man, if you
can start showing uh, lift frequency, do you know those
(35:40):
kind of things, that's a different game altogether.
Speaker 4 (35:45):
That's right, and it's invisible, that's the other part of it. Right,
So this is something that yeah, it's a customer will
experience this. This uh, the benefit of your upgraded application
but not even knowing that. It's necessarily a that's powering
all of it, right.
Speaker 1 (36:01):
And you said, Carrie, that's coming in the app soon.
Speaker 5 (36:04):
Yes. Uh, the same functionality will be in the app,
including voice. We don't have voice on the web yet
or anything like that. But the app will have the
ability to do a big order, or it'll autobots, or
it also suggestive sales, so if if it sees you
ordering a big order, it'll offer like a bag of
(36:26):
donut holes, you know, for you, Hey, you want to
need some donut holes? You know. Yeah.
Speaker 2 (36:31):
The customer experience, the customer experience on it is great.
I love like I put in the thirty and then
it gave me all the options, but then it had
a very easy options like if it said ten clotchies,
or I could change that to nine or eight or seven,
and then it was like, how about these donut holes?
Speaker 3 (36:44):
So it just makes it super easy. And I know
that magical upselling it feels me.
Speaker 2 (36:48):
It is it is, and well that means a lot
to those office people who are in charge of the.
Speaker 5 (36:54):
Right.
Speaker 3 (36:54):
Yeah, they're trying to get it done.
Speaker 1 (36:56):
The other thing you have is the ease and when
you find something that becomes easy to do. You create
a habit for an individual which creates frequency. And this
is just human nature. Once you start to solve that problem.
Because the more friction that you have right now, I
will give you an example, and that is the Chipotle app.
(37:18):
It's very friction, it's friction oriented. You know. I understand
they have the little just reorder button, but you always
want to change something. You know, if you could just
have an AI that just simply says, hey, last order,
just remove the case, so I need to cut down calories. Done.
Speaker 4 (37:34):
So you just reminded me of something that you know,
we didn't talk about at all. But you know, wonder
this brand that is probably the most ambitious restaurant brand
that we've ever come across ever. I mean, completely vertically integrated.
Speaker 1 (37:51):
It's crazy.
Speaker 4 (37:52):
It's bananas, right. And the idea is that Ice just
wut s Vice for what one hundred and eighty million
dollars nothing? Yes, So I don't know if they're going
to run out of cash before they can figure all
this out or whatever. But here's a company where it's
like you just want to tell it about you. Okay,
I need to lose fifteen pounds. I just want I'd
(38:15):
really like chicken, but I also like pizza. And here
it is. It's going to come with this meal plan
and it's just going to work. I don't know. I
think that that might be the pinnacle of where everything
is going, but I'm not sure.
Speaker 2 (38:31):
Well, and I mean good for good for Sweet Green.
I think when we reported on it, they sold it
for like more than seventy million dollars over what they
paid for it.
Speaker 1 (38:40):
So yeah, Wow, that was a nice little turn of profit.
Speaker 4 (38:45):
They never see that. You never see technology being sold
for more more than doubles.
Speaker 2 (38:50):
I think they paid like seventy million dollars and they
sold it for like one hundred and eighty six.
Speaker 1 (38:55):
That's the thing. Well, you're going to start to see
You just wonder if we're going to start to see
AI brands, you know, because remember we had cloud kitchens
for a while that did fairly well. I wonder if
we'll start to see AI brands really starting to propagate
in the restaurant space. That could become, you know, kind
of similar to what what you know Jeff is doing
over it at Wow about you know, he's kind of
(39:18):
created that model to go for distribution. But imagine if
you get AI that could expand a brand's capability into
maybe other markets or visually or virtually. I just wonder
what's going to happen there, Carrie. What are you guys
looking forward to right now in terms of development? What
(39:39):
seems to be like on your roadmap? Now you've got
all these cool tools.
Speaker 5 (39:43):
Well that's that's another good question. So in addition to
you know, more of the coaching is what we're calling it,
that it could do glean from all this data? Uh,
how do we reach our gifts? You know? How do
we use AI to to do our campaigns for us
and actually target market you know, as this same data
(40:07):
comes in our CDP and our data laks join, how
do we get it to react quickly and offer things
to people? Hey you haven't Paul, you haven't been here
in three weeks. We want to see you, you know, or
last time you bought a kalachi, we want you to
try this new donut. You know. It's those kinds of
(40:28):
things where it today. You know, we have point solutions
that kind of automate, but we really want it to
be personal on dynamics. So with all this data, we're
hoping to move down that path.
Speaker 1 (40:42):
I like it. Well, it's definitely going to be something
that we will continue to bring on experts to talk about.
Is the evolution of where this technology is going. We're
going to have a whole new format for this show
coming up in twenty twenty six, so you guys want
to stick around for that. Carrie, thank you so much
for coming in today. We appreciate it. Good luck to
(41:03):
what you guys are doing at Shipley. We're going to
be watching you guys a little closer now, see what
you guys have up your sleeve over there.
Speaker 2 (41:10):
Yeah, and Carrie's going to be joining us in San
Diego in March talking about all of this on the
AI General Session.
Speaker 3 (41:18):
So we're excited to have you back.
Speaker 1 (41:20):
Live I like it. Thank you, And of course you
guys check out Popcorn GTM dot com. That's where Paul
Malinari lives. And uh, if you've never been to his website,
make sure and check that out. It's a great one
as well.
Speaker 4 (41:34):
Check got a couple of our AI companies. We've got
Live Litux and Curbit and Confects and it just great clients.
Speaker 5 (41:43):
Make pop like it.
Speaker 1 (41:48):
That's one of the best branding I'm telling you it's
great branding. Paul, you guys do chat with that, I
have to say, uh. But Paul will be joining us
back here kind of in our little AI corner from
time to time in the podcast. We're going to have
some new segments for you guys. We have a whole
lineup for twenty twenty six that is going to blow
you away. It is going to be awesome. Carry and Paul,
(42:10):
thanks for coming in today. We appreciate it.
Speaker 4 (42:12):
Thanks Arry, you bet yous guys.
Speaker 1 (42:14):
All right, so you know what to do, like and subscribe.
We will of course get you guys in here on
the great podcast, none other than the Fast Casual Nation
podcast with Shara and I. We're going to be discovering
a lot of cool things and I know Shay, you
guys have been starting to go into some new directions.
(42:35):
Anything you can share yet.
Speaker 2 (42:37):
Well, I think our newest venture is our founder Ology
Summit that we're coming up in February. That is designing right, yes,
and it's designed only for restaurant founders. So it's going
to be a really cool event where you know, it's
lonely at the top, and being a CEO is different
than being a founder, so We're going to have a
(42:58):
lot of great infos there, so no doubt you guys.
Speaker 1 (43:02):
Will leave a link down below for founder Ology. You
guys can check that out and of course, uh subscribe now.
If you're not done it already, make sure and do.
We'll catch you next time right here on Fast Casual Nation.
Take care,
Welcome back to another episode of Fast Casual Nation, the
podcast that started at all you guys know the drill.
We are here doing a special podcast today and of
course joining me is MS Karen shrek Canceler.
Speaker 2 (00:12):
How are you, I'm great Ready to nerd out over
all things AI with you guys.
Speaker 1 (00:17):
Yeah, well, we've got the topic of AI and tech.
We will have on two amazing guests that will be
I think revealing some of the most amazing ideas that
you have ever heard around new tech and AI in
the restaurant segment. So we're going to be cutting into
all that stick around right here. My name is Paul Baron.
(00:54):
As the early pioneer in fast casual I've seen the
industry evolve from just a few operators the most sought
after segment by consumers around the world. Now we're planning
to shape its future. Tap into decades of my expertise
identifying the emerging brands and tech winners in the space
(01:15):
saber capital. We'll be fueling the next generation of fast
casual innovation. All right, we are back here on the
Fast Casual Nation podcast. If you guys are not subscribed
to this show right now, all I ask for you
to do is as you're listening, just go over to
Spotify or Apple Podcast, hit that little subscribe button, give
(01:37):
us a star two. We love the five star version
of it. And then of course if you're watching this
on YouTube, make sure I just subscribe here to save FM. Listen, Cherre,
you guys had a tech story that I'll show right here.
That was your most recent news on Shipley who is
going to be joining us today Shipley Donuts, and they've
(01:57):
really started to integrate a little bit more. Explain to
me what's going on with them before we bring them
onto the show.
Speaker 2 (02:05):
So they recently ruled out an online ordering system with
Savory SAI v o Ry not to be confused with
the other Savory, and it looks pretty cool. I was
playing around with it this morning actually, And you can
just go on there and type in I want to
order an eight am catering order tomorrow for thirty people,
and then it brings up it gives.
Speaker 3 (02:25):
You all the suggestions reguidations.
Speaker 1 (02:27):
Yeah, it was great, Listen, this is something I need.
Customer used AI features on twenty two percent of the orders.
That's I wouldn't say that's high, but that's pretty good.
Speaker 3 (02:39):
I mean then we're just now rolling it out. I
think it's pretty Yeah.
Speaker 1 (02:42):
AI assisted results in twenty four percent higher. Now this
is important right there, higher average order values. We got
to talk about this with Carrie. That's pretty significant. And
then of course them doing this I think in general
just shows the direction of a lot of these brands
going kind of this next level. Are you seeing more
brands now really starting to jump in because this time
(03:06):
six months ago I wasn't seeing as much engagement. What
about now?
Speaker 2 (03:10):
Yeah, I think we're we're picking it up. Pepe brands
like Shipley, you know, they're coming on. They're not just
on their own anymore. So it's just it's the beginning
of the explosion.
Speaker 1 (03:23):
Finally it begins. Well, listen, speaking of explosions, we got
to bring in two explosions, and of course one of
them we will bring in and that is mister Paul Molinari.
How are you, Paul?
Speaker 4 (03:36):
Hey, how you doing?
Speaker 5 (03:37):
Baron?
Speaker 1 (03:37):
Nice to see you, man. We're going to bring you
in on the On the Fast Casual podcast. You're you're
an old hand at the Restaurant Masterminds podcast.
Speaker 4 (03:45):
But now I'll tell you this is like this is
the big boy table. You know this is this is
a grown ups table. I love being here with you guys.
Speaker 1 (03:54):
This is good. That's good. I love it. We also
have Carrie Leo coming in from Shipley, who is who
we were just talking about right there making headlines. Carrie,
welcome to the show.
Speaker 5 (04:05):
There's a first for everything, right, you know, carry making headlines.
Glad to be here.
Speaker 1 (04:11):
First, let's get into a little bit about the topic today. Really,
we're going to dive into everything from drive through fails
and wins to pizza prefecttion. We'll talk a little bit
about that. Real story of AI in restaurants is our
focus today, along with you know, kind of this next
generation technology. But I believe we are at a pivotal
point right now where AI is tech and tech is AI.
(04:36):
We've come that far with so many of these large
language models. Carrie, first question to you when you look
at what we're dealing with right now, because I think
the number was seventy nine percent of restaurant operators have
either implemented something around AI or are considering it right now.
What was the this is when we're going to go
do it? Point for you? When did that happen?
Speaker 5 (04:59):
Well, we've been looking at this for a couple of
years now, and you know, it's a new technology. We
weren't quite sure how to leverage it from a consumer
facing technology. We were using it in our back office
system to do some inventory things and you know, calculations
and automation and prep and prediction what to make our shops,
(05:25):
especially our new ones as we expand out into new territories,
need help with what to make because there is a
new market. But for this purpose, we did partner with
Savory company that has been doing this kind of in
the commercial real estate business, and they had a cool
(05:47):
idea that has not been really widely known in the
restaurant business as far as like an online ordering agentic
AI experience where it's actually learning through data based on
what people are ordering. So we spent a good bit
of time gathering data first and then we built this
(06:10):
out with them, and we we helped piece everything together.
Their their agent or the AI tool was really a
form that you kind of went in and did a
catering order at another brand they had started with, but
with us, we had to go full circle and integrate
it with all of our online ordering and are you know,
(06:31):
is coming into the pos and how hard was that
it was It was not too bad. We had some
really good partners with Olo Patronics, and then these guys
just they were really easy to work with. We made
this happen. And then what was really neat is we
patterned it all after the best cashier I've ever known
(06:55):
back in my Chick fil A days. They just the cashiers.
They're phenomenal as all might ye see yourselves. But we
wanted to incorporate all those great attributes of suggestive selling.
Auto boxing for donuts was a big thing because previously
it was one of our problems. When we looked at this,
we wanted to look at all the problems. There was
(07:16):
a box they had to click through twelve times and pick,
jump around and pick donuts and drop it into a box.
Now they can just pick their donuts like they're in
a shop and drop it into the basket and it
auto boxes and recognizes that it has twelve donuts or
six if you fall short. If you hit eleven donuts,
so go hey, for another dollar, you can have a
(07:39):
dozen and get a discount.
Speaker 4 (07:40):
You know.
Speaker 5 (07:40):
So we're trying to pattern it after real people and
make that experience great for the consumer.
Speaker 1 (07:47):
Where would you say, on a scale of one to ten,
you guys are as a brand in terms of implementing
AI right now?
Speaker 5 (07:54):
On scale of one to ten, I would say we're
probably five. I you know, I don't think we're at
the helm of it, you know, but we we're definitely
leaning into it and we've got a lot of things
on our roadmap coming for sure.
Speaker 1 (08:09):
Hey, Paul, are you seeing I know you're working with
a lot of tech companies, Yeah, right now, most of
which some of which we'll talk about in today's episode.
But what are you finding right now from the operator side?
Are through the tech you know, vision of what they're
trying to solve?
Speaker 4 (08:26):
Are they that I love what Carrie how Kerry describes
their approach. They're trying to solve a problem. And I
think that these AI companies that are very specific in
the types of problems that they're solving may not necessarily
be customer facing, but rather operational or something, you know,
(08:50):
something It could be something as simple as getting just
improving procurement for example, getting invoices through, or something to
something a lot more ambitious, like say, I imagine being
able to talk to your restaurant, like Kerry, Like imagine
(09:11):
being able to talk to Shipley Donuts in the brand
and saying, you know, and tell me, how are these
locations in East Houston doing, you know, literally having a
verbal conversation and bringing all of that information back to
you in a format that's easy to understand, easy to share,
and brings it down to a level where everybody's on
(09:33):
the same page. I think the goal for many of
these technology companies is trying to create this single pane
of glass. I think that's something that we're going to
see a lot more of where we're integrit we're ingesting
all of this data, and we're then we're going to
after we ingest it and clean it, we're going to
(09:54):
then make it available to whomever needs it, so everybody's
on the same page.
Speaker 1 (10:00):
Our case study today was especially on the drive through
side is McDonald's. They did an acquisition back in twenty nineteen.
This was in reference they partnered with IBM later, but
a priente I think was the company's name. Basically it
was an AI voice ordering tech and I think it
was very early. Obviously back in twenty nineteen, five years ago,
(10:22):
we have come a long way. SHERA are you when
you look at the trials that we're seeing in restaurants
today versus what we saw back in twenty nineteen, which
were kind of our missteps. Are you seeing a lot
more successes like what Carrie's doing or what's the scale?
Speaker 2 (10:40):
Yeah? Yeah, they're better, I mean, especially especially at the
drive through. I mean I gave a keynote last year
about you know, where we are in fast casual industry,
and I had a whole bunch of examples of fails,
so like Wendy's McDonald do you know, like all the
just and you're not seeing that nearly as much.
Speaker 3 (10:57):
I think we're getting there.
Speaker 1 (10:59):
So yeah, yeah, what about accuracy? Are you are you
guys scoring accuracy Carrie at all? I like to determine
how the customer feedback is coming in.
Speaker 5 (11:09):
Yes, we definitely do that. We've got a couple of
different ways to do that. We're also looking at tracking
that on the as far as the website goes and
the ordering for us, you know, with the type of
AI and the type of model we're moving to, We're
we're doing server side rendering and a better tracking of
we're guests or clicking where they're falling off where they
(11:32):
drop off. We're also on the McDonald's note, we're also
looking in a couple of ways to implement voice in
our mobile app. Next right, so we've done our website,
We're about to release the new version of our mobile app,
which will include voice ordering, so they can be just
driving down the road and go, hey, Siri, I want
(11:53):
to order Shipley, you know, and they can just go
right through it. We're also looking at that with a
drive through as well. We know that there's a lot
of testing. It's got to go into the drive through
piece sell it.
Speaker 1 (12:04):
I've noticed these AI or APIs have really started to accelerate,
so we're seeing a lot of these kind of things.
Here's a question for you, Paul, that kind of is
a bit of a counterbalance. Do you think that we
are going to get so good on either the AI
APIs for some a brand like Kerry to where they
(12:26):
could just go out and implement all of this stuff
on their own kind of with their own lms.
Speaker 4 (12:32):
What do you think I think that there's and maybe
sure it can help with this too, because I feel
like it's always the same old gatekeepers ya as, It's
always as it's always been. It's you know, how listen
this data has to be accessible. You know, agentic AI
is only as strong as its weakest connection. You know,
(12:54):
you these things have to work together in order for
things to happen. So I think that, yes, I believe
that the APIs are going to get stronger and that
access is going to be more open, But I still
think there's still a lot of hesitancy with certain technology
companies making that possible.
Speaker 1 (13:16):
Well, on the POS side, are you finding, Kerrie that
the point of sale guys are okay with integrations coming
in from API because essentially you're trying to go direct
to POS? Is that right correct?
Speaker 5 (13:31):
Now? The key there is we already had a good
integration with OLO, the online ordering provider, so that's where
all of our digital stuff is being aggregated and then
is pushed into the point of sales. So we already
had that integration. But there are a lot of POS companies,
you know, when we're doing RFPs and talking to them,
(13:52):
they're starting to lean heavily into building out AI solutions
as well.
Speaker 4 (13:57):
Yeah, there seems to be you know, there's this new
uh you know, POS companies kind of want to create
headlines for themselves every couple of years, and the new
one is now how they're all pivoting towards becoming these
AI platforms.
Speaker 1 (14:12):
Is that real?
Speaker 5 (14:14):
Well, you know, it's we'll see.
Speaker 1 (14:18):
I'm just gonna ask, you know, I just blurt out
stuff most of the time.
Speaker 4 (14:21):
We'll see if they can pull it off. I mean,
you know, if if the if the pos will is
and always will be sort of the heart or the
hub of the restaurant tech stack. You know, they have
a head start, but at the end of the day,
they have to execute on all of these different actions
that they may not have the expertise frankly to pull off.
Speaker 1 (14:45):
I'm concerned about that because I'm watching the tool sets
that we use in a lot of our analysis business
on the finance side of things, and the amount of
uh I won't I won't say, well, yeah, well they
the tools that we're using right now across I'd say
four different AI platforms is literally supplanting a lot of
(15:09):
the old tech that we were using prior. And it's
like happening so fast. I'm talking to six months, and
it's likely that this is going to come down to
three months, maybe ninety days in just a very few
I think, just a very few months away from getting
to that level we share. Do you think we're at
(15:31):
an inflection point yet for the restaurant industry or do
you think it's still a ways out there.
Speaker 2 (15:37):
I think for everybody it's still a little ways out.
But I mean, obviously we're getting closer. But some of them,
you know, you're seeing it happening. I mean you're seeing
the predictive, the suggestive selling, the up selling. So I
mean it is working. I just it just takes the
restaurant industry a long time, as you guys all know,
to catch up. I think with tech compared to other industries.
Speaker 1 (16:00):
Carry what what ll M did you guys end up
using for for your platform?
Speaker 5 (16:04):
I think our platform. If I'm not this is a
good savory question, but uh, next days react and we're
using there some some of their clutch they got okay,
all right on it.
Speaker 1 (16:20):
So I think that's direct. I think that's direct into anthropic,
which is intriguing because they have one of the bigger
APIs that I've seen that has started to roll out
into some of the food service space. Paul, have you
been watching the most recent uh stuff with what Google
has been able to do with Gemini?
Speaker 4 (16:41):
Because it tell me because there's there's so much. There
is so much.
Speaker 1 (16:45):
So what's happening is there is a war going on
right now between open ai and Google. Yes, and open
ai was a first mover and then all of it.
Remember that Google was on the precipice of getting split up.
They were going to take Alphabet and start divvying it out.
So YouTube was gonna I mean basically when I worked
(17:07):
at Microsoft, this is what we had to live through
of getting basically anti trust nailed on us or we
had to slow down all of our production. But the
judge recently came in and said that their search business
is now at it has an existential threat, which is
open Ai. So open ai basically saved Google from getting
(17:31):
split up.
Speaker 4 (17:33):
Ah, look at that. So you're helping hand from your
from your art from your competitor.
Speaker 1 (17:40):
Yeah, which could have opened them up for where they
are right now with Gemini because Gemini has really accelerated.
I don't know if you guys are testing these models
as much.
Speaker 2 (17:49):
Yeah, I am ad user of Gemini. It's my it's
my favorite one. I use it constantly all day long.
Speaker 4 (17:55):
By five, I think, you know, one of the things
that I think I find fascinating about these different llms
is that there are different use cases for each one,
like you may use one in your personal life and
one in your work life, and then one specifically that
you like as a shopping tool.
Speaker 1 (18:11):
Well, one thing about about open ai that I have found,
and I'm wondering, Kerry, if you guys are finding this,
because when you look at the lms from open Ai,
what I have found is it gets harder and harder
to get data out of it. Because it's so I
think they themselves. When I'm talking about the development team
over at open Ai, they're starting to be so concerned
(18:35):
about delivering pure accuracy where Gemini Anthropic, which is Claude Perplexity,
the big four kind of that are circling right now
seem to be delivering more accurate and more timely information.
So I'm concerned open im may end up falling back
behind in terms of development. So are you guys seeing
(18:59):
that right now in terms of accuracy levels? Are they
continuing to get better and better?
Speaker 5 (19:06):
Yeah? They are for us. I mean we use Jim
and I that's part of this whole solution. But you
know where we're headed. It's very interesting. I don't know
if you heard with chat GPT on this subject, right,
A marketplace is opening up in there. Door Dash is
already going to be in there. The way we built
(19:29):
with our partnership with Savory, our new platform is it's
ready to go into that as well. So you know,
AI is going to be talking to AI and you
know all that kind of stuff for too long. And
what we think is in the next couple of years,
it's going to change the way people search Google. You know, the.
Speaker 1 (19:50):
Regular I think search is done.
Speaker 5 (19:51):
Yeah, yeah, this is going to be the new frontier.
Speaker 1 (19:55):
Right, it's done.
Speaker 4 (19:57):
That's right. So there's you know, there's search engine optimization
and now there's answer engine optimization an oh uh and
you have to be you have to be baked into
those uh, into those lllms.
Speaker 1 (20:09):
Now, yeah, well, and I think this is the thing
for brands because in reality, you've got only a handful
of search pools you go into. You've got Maps, which
is really built around Google that has all had all
sorts of layers that I think is going to get
even better, especially with Gemini starting to integrate to Maps
because that's on their on their roadmap, which is good
(20:32):
because if you've got AI enabled menus that's the plug
in probably the natural fit for Google Maps to kind
of control that. But outside of that, are you guys
doing anything on that UH front carry in terms of
dealing with consumer search in the future or discovery.
Speaker 5 (20:52):
Yeah, not not just yet. I mean other than what
I've what I've shared, We're starting to delve into that
little bit. But I think you know, back to your
data question. Also, we're using AI for this too. We
have a Snowflake environment, So all the data that we're
pulling from our website, from our pos, from UH transaction
(21:15):
level detail, everything's feeding one source. And now we're building
out the AI solution within snowflake to do those things
where I think one of the Paulls said, you'll just
be able to ask a question and like, hey, why
is this shop sales different on Tuesday at seven thirty
(21:38):
one pm compared to last year or two years ago
or whatever, and it's is you know, we're getting close
to that as well.
Speaker 4 (21:46):
That's right, Kerrie. I love that because what you're talking
about is that unification, right right, that data unific You're
ingesting all of this data you're putting inside of your
snowflake environment and you're unifying it unifying, it becomes essentially
one secure platform. And the beautiful thing about that is
it then it cleans it because it's able to kind
(22:08):
of recognize its pattern recognition, right, and it can it
can elevate those anomalies and then clean them to where
they thinks it should be. And now all of a sudden,
your accuracy is going up and you're able to execute
much faster. And that's what I was talking about earlier
with this like single pane of glass idea, where all
(22:31):
of these different insights and these reports and these alerts,
all of these things are able to bubble up now
and not just be like, Okay, this is what's happening,
but it can also now prescribe an action. And that
could be right at the store level. It could be
(22:53):
you know, sending an alert all of a sudden now
to your store manager saying, hey, guess what this is happening?
Speaker 5 (23:00):
Fix it?
Speaker 4 (23:01):
Well?
Speaker 1 (23:01):
And I guess the key there is is how much
data goes in a good example of this one I
got to share this. I think this is a good
comparison of how to analyze problems inside an organization where
AI has capability of looking at it. And of course
I have to share this right here, which is the
(23:23):
struggle of the Kansas City Chiefs.
Speaker 2 (23:27):
And we have Carrie on who is from Houston, who
lives in Houston.
Speaker 1 (23:32):
I know, I know, Carrie is the Houston I guess
this is major injury crisis, offensive aggression and turnovers, questionable
coaching decisions, super Bowl hangover. I'm just thinking if you
could apply this to a football game. Imagine what you
could do if you actually funneled real data in from
(23:52):
a restaurant.
Speaker 4 (23:54):
Yes, it's bananas. Yeah, I'll do to take more than anything.
Speaker 3 (24:00):
Yeah, never, I think he was tired. He dropped two passages.
Speaker 1 (24:04):
The question is, Carrie, are you guys funneling that data in?
Speaker 5 (24:08):
Yeah, it's great, great question, Paul. So again we're we're
at the cusp of that. I just met with our
CEO last Friday about this topic and we wanted to
be able to coach our shops. Our gms are frontline
folks as well as our bove story and corporate people.
(24:30):
But you know, the all the data we have. We
have labor data coming in all day long, we have
sales data, we have transational data, we have product data.
All of this stuff is feeding one source and so
we want to build it out where it's actually instead
of somebody having to run a report or look at
a dashboard, have a screen or an audible signal, or
(24:53):
somebody even you know, hey, you might want to think
about cutting back your schedule today, you know, sales are down,
or you might want to make more strawberry ice donuts
because you're getting low and compared to last week, at
this time, you've sold you know, ten times more than
you have available. Now, you know, it's all those kinds
(25:14):
of things we're looking at. So we wanted to coach
real time almost throughout the day. We think that would
be a big game changer.
Speaker 4 (25:23):
Absolutely, all of these different data overlays right where you're
able to now ingest everything from weather reports to you know,
local events that might be happening, all of these different
ways in which you can then overlay and supplement or
augment this data to be just become that much more
powerful of an insight engine.
Speaker 1 (25:45):
Yeah. Well, and I think you look at the models
that are working because this is this is the thing
that I know operators are trying to say, Okay, well,
who's got it right, who's fixing it? I was looking
at one of the case studies we put on the
sheet Today's Share and this was Domino's obviously pizza marketplace.
You guys run that. So when you look at that
(26:06):
kind of success story that Domino's is, this is in
reference to their AI demand forecasting and inventory. Basically it
is what it boiled down to. But I was looking
at one of the notes on it. Here it said
the system reduced training time from sixteen hours to under
one hour. That's crazy, one.
Speaker 3 (26:26):
Yeah, and the.
Speaker 2 (26:28):
Crazy, and then you know it improved forecasting accuracy from
seventy five to ninety five. I mean, those are those
are crazy, those were crazy marks totally worth it.
Speaker 1 (26:38):
Why so do you think why aren't we seeing more
brands doing this? I don't understand this. It seems like
this shouldn't be mean its price.
Speaker 2 (26:47):
It's price. I mean I don't I can't tell you
what it is, but I'm sure it's not cheap. And
also I think people don't know what they don't know,
and they're kind of scared to dive in.
Speaker 1 (26:57):
So it's just early stage.
Speaker 4 (26:58):
It's definitely price. And when you're talking about Microsoft, you
know Microsoft doing a deal directly with Dominoes. How many
brands can actually pull that off. The other thing is
that they are taking sort of this all in one approach.
It feels like they're putting in a lot of eggs
in the Microsoft basket, where other brands, perhaps like Shipley
(27:19):
or others, you know, can look at different types of
points solutions. You know, it can probably do everything that
Microsoft three sixty five, Dynamics three sixty five can do,
but it does so more piecemeal, like I use this
company to solve this problem. I use this company to
solve that problem.
Speaker 1 (27:35):
But then you run into the problem that we have
had in the industry for a long time, and that is,
you know, stuff doesn't talk to each other.
Speaker 5 (27:43):
That's there, Well, there you go now.
Speaker 1 (27:45):
But at the same time, what you know, Carrie mentioned
earlier was the fact that agentic AI may solve that.
If you've got an AI agent that simply just starts
conveying this, you no longer need the web hooks and
API protocols to basically talk to two different systems. Are
the solution providers you're talking to, Kerrie, are they even
(28:06):
mentioning that kind of concept yet?
Speaker 5 (28:08):
For Yeah, yeah, they're already talking about it. And you're right,
we've done a lot of growth over the last almost
five years since I got to Shipley, A lot of
technology has been implemented, implemented here at this ninety year
old brand that really didn't have a lot of technology
(28:28):
when I got here. But what we did is we
did a lot of research with making sure they had
strong APIs and the companies we selected were already talking
to each other. We did need point solutions of back office.
Synergy Suite is a really fine built from the ground
(28:49):
up product that has all the areas of running the
business at the shop level or restaurant level that integrated
with a in a sell, we're pulling BI data out
of it into snowflake. We're pulling BI data out of
the point of sell. Olo was the above store digital
(29:10):
aggregator that helped with our delivery service providers and online ordering.
They have a great API, but they're all looking at
this stuff and synergy, you know, for a prep and prediction.
They've already got that AI built in, so it's already
looking at all the data coming into that system and
(29:30):
actually suggesting building out a dashboard for the next day
or the week, looks at six weeks worth of data
by hour, and it tells you what to make. So
a lot of that fall was placed, you know, early
on as we selected them, and then now they're all
heavily trying to make you know the API and or
(29:54):
or actually the connections a lot better.
Speaker 1 (29:57):
Yeah, I won't I won't bring it up in today's call,
but we we've covered this quite a bit over on
some of our other channels. Is agentic to a gentic
payment that eventually is going to be some sort of
digital you know, payment architecture that will eventually be moved
into I think the restaurant space with stable coins. They've
already kind of gone in this direction. Once we get
(30:19):
some regulatory alignment out of that, it's probably going to
open this up for digital payments to move much faster
and not have to deal with you know, plus one settlement,
which is what you have to deal with right now
on a lot of the merchants. So that's something we'll
talk about in some upcoming shows. But one question is this,
and I'll ask this to you shareff for right now.
(30:41):
I know you talk to a lot of suppliers in
the industry. Is anybody talking about data like data centers
for just the food service industry?
Speaker 2 (30:53):
No, I mean not that I'm not that I'm aware.
That doesn't mean it's not happening, but I haven't heard
it yet.
Speaker 1 (30:57):
Because you've got a lot of these fails that have
happened here recently. Even we were affected by one just
a couple of weeks ago with Riverside. I mean we
had outages because of data centers going down, you know.
So I'm just curious if that's going to become maybe
a industry specific solution so that you could avoid, you know,
(31:19):
maybe some of the traditional problems that the bigger areas
could face. Have you heard anything, Paul.
Speaker 4 (31:26):
No, I haven't. But one interesting thing is, though, is
I keep hearing Snowflake coming up as kind of like
this data center of the warehouse of choice. I'm not
sure if that is more coincidental.
Speaker 1 (31:38):
What was your research carry on Snowflake? What was it
that drew you to them?
Speaker 5 (31:43):
Just the fact that they were already delving into AI,
and they're a pretty mainstream player out there as far
as BI and cloud based, easily integrated to with like
five Tran and ma till In and of these other
e lts that come in, so uh, I mean, they're
(32:05):
just they've been around a while, and and I think
on that note, you know, as far as risk mitigation,
you want to make sure whoever you're selecting is it is.
They've got a distributed model and lots of backup and
different data centers, so if one Trump goes down or
something like that, you're not down right.
Speaker 3 (32:23):
When you were building this out, did you have another
restaurant brands in mind that you wanted to kind of
you were like, this is a very good one.
Speaker 2 (32:32):
I want to I want to make it like this
or were you kind of starting from your own problem
solving use case.
Speaker 5 (32:38):
Yeah, we just kind of started from scratch and did
our research and talked to people. But they they clearly
and and this is not a paid endorsement, but they
they clearly have a really good product that's easy to use.
Our Nick next door to me, our BI guy, I mean,
(32:59):
he's been in there, uh, doing all sorts of amazing
things with this data, and and it's easy for it
was easy for him to learn.
Speaker 4 (33:10):
Hey, Carrie, do you I'm curious are you guys looking
at AI to solve more operational issues or to be
more client customer facing increased sales? Is it you know
that that balance?
Speaker 5 (33:27):
Yeah, Yeah, that's a good question. Uh. I think originally
it was more operational. Uh, and we we kind of
are a little more seasoned there, you know with some
of the things we're doing operationally. But now you know,
again through this partnership with Savory, we've we kind of
(33:49):
delved into hey, this this could work for a guest
facing thing with some and it's not a chatbot or
anything like that. I mean, this thing is learning based
on the data based want people people that don't want
to do the ordering. So for instance, we have a
lot of big companies that would call us this is
(34:12):
one of the problems and they say, hey, I got
this meeting, I don't know, I don't know what to get.
You know, here's my budget. So we just kind of
built that into a tool for them, you know, so
then they just started ordering, right, So now they can
put I got a meeting for fifty people, I just
want donuts, and it'll in ten seconds, it builds an
(34:35):
order for that many people with just donuts, or if
you don't if you're not specific, it'll say donuts and Colachi's,
or it'll all add coffee. You know, assuming that you're
having a meeting, you want coffee. So but it's also
smart in that it's not a chat bout it. It's
actually look, it's talking to Olo and go, okay, what
(34:56):
items are available. It's particular shop they're looking at. Uh,
they don't have coffee there, or they don't have this
doughnut there, so it won't offer that up. So we're
trying to think about the guests experience. But yeah, that
I think we're to answer your question, Paul, we want
to do it on all fronts, but this was the
(35:16):
first big opportunity we had to take.
Speaker 1 (35:18):
This is the one you could really measure right to sales,
you know, if you if you're increasing sales right throughput
I mean I could see it on the backside. You
know the example we just gave with Dominoes where they're
improving their you know, their training, which is a hard cost.
Don't get anybody wrong, it's there. But man, if you
can start showing uh, lift frequency, do you know those
(35:40):
kind of things, that's a different game altogether.
Speaker 4 (35:45):
That's right, and it's invisible, that's the other part of it. Right,
So this is something that yeah, it's a customer will
experience this. This uh, the benefit of your upgraded application
but not even knowing that. It's necessarily a that's powering
all of it, right.
Speaker 1 (36:01):
And you said, Carrie, that's coming in the app soon.
Speaker 5 (36:04):
Yes. Uh, the same functionality will be in the app,
including voice. We don't have voice on the web yet
or anything like that. But the app will have the
ability to do a big order, or it'll autobots, or
it also suggestive sales, so if if it sees you
ordering a big order, it'll offer like a bag of
(36:26):
donut holes, you know, for you, Hey, you want to
need some donut holes? You know. Yeah.
Speaker 2 (36:31):
The customer experience, the customer experience on it is great.
I love like I put in the thirty and then
it gave me all the options, but then it had
a very easy options like if it said ten clotchies,
or I could change that to nine or eight or seven,
and then it was like, how about these donut holes?
Speaker 3 (36:44):
So it just makes it super easy. And I know
that magical upselling it feels me.
Speaker 2 (36:48):
It is it is, and well that means a lot
to those office people who are in charge of the.
Speaker 5 (36:54):
Right.
Speaker 3 (36:54):
Yeah, they're trying to get it done.
Speaker 1 (36:56):
The other thing you have is the ease and when
you find something that becomes easy to do. You create
a habit for an individual which creates frequency. And this
is just human nature. Once you start to solve that problem.
Because the more friction that you have right now, I
will give you an example, and that is the Chipotle app.
(37:18):
It's very friction, it's friction oriented. You know. I understand
they have the little just reorder button, but you always
want to change something. You know, if you could just
have an AI that just simply says, hey, last order,
just remove the case, so I need to cut down calories. Done.
Speaker 4 (37:34):
So you just reminded me of something that you know,
we didn't talk about at all. But you know, wonder
this brand that is probably the most ambitious restaurant brand
that we've ever come across ever. I mean, completely vertically integrated.
Speaker 1 (37:51):
It's crazy.
Speaker 4 (37:52):
It's bananas, right. And the idea is that Ice just
wut s Vice for what one hundred and eighty million
dollars nothing? Yes, So I don't know if they're going
to run out of cash before they can figure all
this out or whatever. But here's a company where it's
like you just want to tell it about you. Okay,
I need to lose fifteen pounds. I just want I'd
(38:15):
really like chicken, but I also like pizza. And here
it is. It's going to come with this meal plan
and it's just going to work. I don't know. I
think that that might be the pinnacle of where everything
is going, but I'm not sure.
Speaker 2 (38:31):
Well, and I mean good for good for Sweet Green.
I think when we reported on it, they sold it
for like more than seventy million dollars over what they
paid for it.
Speaker 1 (38:40):
So yeah, Wow, that was a nice little turn of profit.
Speaker 4 (38:45):
They never see that. You never see technology being sold
for more more than doubles.
Speaker 2 (38:50):
I think they paid like seventy million dollars and they
sold it for like one hundred and eighty six.
Speaker 1 (38:55):
That's the thing. Well, you're going to start to see
You just wonder if we're going to start to see
AI brands, you know, because remember we had cloud kitchens
for a while that did fairly well. I wonder if
we'll start to see AI brands really starting to propagate
in the restaurant space. That could become, you know, kind
of similar to what what you know Jeff is doing
over it at Wow about you know, he's kind of
(39:18):
created that model to go for distribution. But imagine if
you get AI that could expand a brand's capability into
maybe other markets or visually or virtually. I just wonder
what's going to happen there, Carrie. What are you guys
looking forward to right now in terms of development? What
(39:39):
seems to be like on your roadmap? Now you've got
all these cool tools.
Speaker 5 (39:43):
Well that's that's another good question. So in addition to
you know, more of the coaching is what we're calling it,
that it could do glean from all this data? Uh,
how do we reach our gifts? You know? How do
we use AI to to do our campaigns for us
and actually target market you know, as this same data
(40:07):
comes in our CDP and our data laks join, how
do we get it to react quickly and offer things
to people? Hey you haven't Paul, you haven't been here
in three weeks. We want to see you, you know, or
last time you bought a kalachi, we want you to
try this new donut. You know. It's those kinds of
(40:28):
things where it today. You know, we have point solutions
that kind of automate, but we really want it to
be personal on dynamics. So with all this data, we're
hoping to move down that path.
Speaker 1 (40:42):
I like it. Well, it's definitely going to be something
that we will continue to bring on experts to talk about.
Is the evolution of where this technology is going. We're
going to have a whole new format for this show
coming up in twenty twenty six, so you guys want
to stick around for that. Carrie, thank you so much
for coming in today. We appreciate it. Good luck to
(41:03):
what you guys are doing at Shipley. We're going to
be watching you guys a little closer now, see what
you guys have up your sleeve over there.
Speaker 2 (41:10):
Yeah, and Carrie's going to be joining us in San
Diego in March talking about all of this on the
AI General Session.
Speaker 3 (41:18):
So we're excited to have you back.
Speaker 1 (41:20):
Live I like it. Thank you, And of course you
guys check out Popcorn GTM dot com. That's where Paul
Malinari lives. And uh, if you've never been to his website,
make sure and check that out. It's a great one
as well.
Speaker 4 (41:34):
Check got a couple of our AI companies. We've got
Live Litux and Curbit and Confects and it just great clients.
Speaker 5 (41:43):
Make pop like it.
Speaker 1 (41:48):
That's one of the best branding I'm telling you it's
great branding. Paul, you guys do chat with that, I
have to say, uh. But Paul will be joining us
back here kind of in our little AI corner from
time to time in the podcast. We're going to have
some new segments for you guys. We have a whole
lineup for twenty twenty six that is going to blow
you away. It is going to be awesome. Carry and Paul,
(42:10):
thanks for coming in today. We appreciate it.
Speaker 4 (42:12):
Thanks Arry, you bet yous guys.
Speaker 1 (42:14):
All right, so you know what to do, like and subscribe.
We will of course get you guys in here on
the great podcast, none other than the Fast Casual Nation
podcast with Shara and I. We're going to be discovering
a lot of cool things and I know Shay, you
guys have been starting to go into some new directions.
(42:35):
Anything you can share yet.
Speaker 2 (42:37):
Well, I think our newest venture is our founder Ology
Summit that we're coming up in February. That is designing right, yes,
and it's designed only for restaurant founders. So it's going
to be a really cool event where you know, it's
lonely at the top, and being a CEO is different
than being a founder, so We're going to have a
(42:58):
lot of great infos there, so no doubt you guys.
Speaker 1 (43:02):
Will leave a link down below for founder Ology. You
guys can check that out and of course, uh subscribe now.
If you're not done it already, make sure and do.
We'll catch you next time right here on Fast Casual Nation.
Take care,
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