Skip Kimpel welcomes Brandon McRrill, co-founder and CEO of 5Out, to delve into the transformative role of machine learning (ML) in the hospitality industry. As the conversation unfolds, McRrill shares his extensive background in the restaurant sector, which began at the tender age of 15. His journey through various prestigious establishments laid the groundwork for his current endeavor, where he aims to harness data to enhance operational efficiency in restaurants. A key theme of the discussion is the common misconception that artificial intelligence (AI) is synonymous with machine learning. McRrill argues that true AI, as depicted in films, does not yet exist in practical applications. Instead, what we see today is sophisticated programming that utilizes vast datasets to automate and simplify processes, ultimately benefiting restaurant operations.

The dialogue shifts to practical applications of ML within the restaurant space. McRrill emphasizes the importance of predictive analytics in optimizing staffing, menu design, and inventory management. By leveraging data, restaurants can make informed decisions that not only improve profitability but also enhance the customer experience. Kimpel and McRrill dissect the challenges of integrating ML into existing systems, highlighting the need for user-friendly interfaces that allow operators to easily access and implement insights without getting bogged down in technical complexities. As they explore the future of hospitality technology, they stress the importance of delivering actionable outcomes rather than just insights, setting the stage for a new era of efficiency in the industry.

Takeaways:

• The podcast explores the misconceptions surrounding AI and emphasizes that true AI does not yet exist as commonly portrayed.
• Brandon McRrill highlights the importance of machine learning as advanced programming, not independent thinking.
• Restaurants can leverage machine learning to optimize operations and enhance customer experiences through automation.
• The discussion includes the challenges of integrating machine learning into existing software platforms in the hospitality industry.
• Listeners are encouraged to evaluate AI solutions by focusing on the deliverables rather than the technical jargon.
• Brandon emphasizes the need for simplicity in technology to ensure that restaurant operators can easily implement and benefit from it.

Links Referenced in This Episode:

• gainadvisors.com
• bootcamp.magicgate.com
• magicgate.com
• skipkimpel.com

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You are listening to the Tech Chef Podcast.

This show is powered by Growth Advisors International Network where travel and hospitality companies come to grow.

For more information please visit gainadvisors.com.

This is Brandon McCrell, co founder and CEO of Five out and you're listening to Skip on the Tech Chef Podcast.

Off Premise Strategy, Business Continuity.

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Welcome back to the Tech Chef.

I'm Skip Kimpel, your guide to the latest hospitality technology insights and inspiration straight from sunny Fort Lauderdale, Florida.

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Your continued support and engagement motivate us to push boundaries and deliver top notch tech content that helps professionals like you stay ahead, tackle challenges and drive innovation.

Thank you for being an invaluable part of our growth and success.

Anybody that was at CES this year, all I have to say is wow.

It was an action packed show with major announcements coming from all industries.

Of most interest to me however was the keynotes from Nvidia and Sony.

I'm not going to spoil it for you, but if you missed those, make sure you jump on YouTube and check them out.

This week's show is interesting as we challenge the concept of AI.

Crazy, you say?

Well if you are curious you are in the right place as we explore this topic with our special guest, Brandon McGrill.

With more than 26 years of experience in the hospitality industry, he is the co founder and CEO of five Out, a company that leverages machine learning to provide actionable directions for food, service and resort businesses.

He leads the vision, strategy and execution of five out, which aims to revolutionize the way hospitality operators design, manage and optimize their operations and customer experience.

Brandon has a very strong background in food and beverage conceptual development, having founded and co founded several successful restaurants and cafes.

Buckle up and let's get a little controversial with this week's title episode.

AI does not Exist.

Let's go.

Brandon, thank you so much for joining the show today.

Can you share your professional journey leading up to founding five out and really what inspired you to step into the restaurant and hospitality tech industry?

First of all, thanks.

Great to be here and great talking with you and seeing you again.

And then to answer those questions, I would say five out was begotten from my career in the hospitality industry.

So I got started in the restaurant industry when I was 15 years old.

I was playing soccer at the time, competitively, got injured.

I was injured enough to the point where I couldn't play soccer competitively anymore, but I still could walk and walk well.

And so my mother said, you need to take that 15 year old energy, get out of the house and take it somewhere else.

So I went looking for a job and in all the places that I applied without much experience at the ripe old age of 15, the first place that hired me was actually the Grosse Pointe Hunt Club in Grosse Pointe Woods, Michigan.

And I was parking cars and washing dishes.

So I would park cars in a little tuxedo shirt and suit and then I would take it off and run inside and wash dishes and then come back outside and put that shirt and suit back on and unpark people's cars at the end of their meal.

And I just kind of became enamored with the industry from that point.

I was always interested in food growing up, but I really started to understand where it came from, literally and figuratively, and I stuck with it from there.

So throughout the rest of high school and college, I worked in restaurants.

I transferred from going to school in downtown Detroit to Columbia Chicago and accidentally fell into working at a high end restaurant called Alinea, which is a three Michelin star, four star restaurant in Chicago.

And that's when I really made a material commitment to transition my career to hospitality.

Previously I was studying photography.

So from there I went to work for some pretty relatively well known restaurant tours like one off Hospitality.

Dm Paul Khan, Richard Melman to let us entertain you, Danny Meyer at Union Square, Union Square Hospitality group at the MoMA, John George Lucas, David Burke, Marcus Samuelson.

And then I started opening my own restaurants.

So I was in my late 20s.

I started opening my own restaurants in the Lower east side of Manhattan.

Being only open for three and a half months at my second restaurant put us on a national radar.

We got scooped up by a publicly traded REIT to open up a ground floor restaurant in a $350 million mixed vertical neighborhood in Philly.

Went and opened, that stayed, worked with those developers to open up two more restaurants.

And that has been the last 26 years of restaurant touring.

Wow, fast go backwards.

Yeah, that was, it was a lot.

It was A run, but it was 26 years, so obviously time to get stuff in.

And by the way, you did a lot of name dropping there.

But the only thing that stuck in my head is the picture of you parking cars.

It reminds me of the.

The kid in goodfellas parking the cars for all the mobsters out front.

Barely, barely, barely tall enough to look over the steering wheel.

No driver's license.

But thankfully, the parking lot was very close to the front door.

I'm in New York City.

I've just opened up my second restaurant.

And facing headwinds as I had seen it, many other restaurants that I had worked at before and for many other restaurant tours that I'd worked that before, and that's that.

Generally speaking, the hospitality industry and the restaurant industry in particular, has a finite profit margin.

And with the two largest expenses being labor and cost of goods.

If you can understand what your product demand is going to be, and therefore what you're purchasing and expense needs to be across those two things and understand them in advance, then you can potentially be more profitable.

And the easiest way to, to kind of speak to that is if you're running a banquet hall, let's say, for example, and you book a wedding six months in advance, and the people who are hosting the wedding have sent out requests for RSVP and ask people if they want steak or chicken, and they have filled it out that they're coming and which one they're eating.

Then you know how many people you have, and you know what they're going to, what they're going to eat.

So you know, you only need to purchase and prep exactly for the amount of people that you know are coming and it's already paid for and exactly what food they're going to be, they're going to be having.

So you know how many staff you need to cook that many chicken and steak and how many drinks they're going to have, and all these things become predetermined.

And if a restaurant, any restaurant, not just a banquet hall, but any restaurant, had that kind of visibility, looking forward into what was going to occur in advance, they could have a better handle on how much staff do I need doing what job, where and when, and how much product do I need, where and when.

So myself and my CFO at the time, Mike Marion, who's now my CTO at five out, we decided, is there enough data out there to allow us to create something simple and rudimentary that can help inform us and give us a better understanding of what is likely to occur in the future.

Utilizing Excel and a thumb drive plugged into a Micros terminal in a basement, we pulled raw data and utilized other outside data like foot traffic and weather and local and national events, and made simple algorithms handwritten in Excel and put this data through those algorithms and use it to forecast what was going to happen going forward, just by a single week, broken down by day, and then by meal period, and then by hour, starting with revenue and then items sold.

And if you know what your hourly revenue is and your headcount and what items are going to get sold, then you can simply use simple arithmetic to reverse engineer into a labor budget and a purchasing budget that is more specific and finite than you would on your own by guessing.

And in doing so, we were able to bring $300,000 of additional net profitability to a $3 million restaurant.

So that was a sizable material beneficial impact.

So we applied that to a second restaurant of mine and then three more, and at five restaurants we crashed out.

Because Mike is a human being and not a computer, and he was spending about 10 hours a week per restaurant calculating this data, turning it into a.

First of all pulling it, then calculating it, turning it into a deliverable that's actually utilizable by a restaurateur, which I'm one, so I get to make comments like that.

So it has to be simple.

And then tracking changes and following up to make sure they did it.

Those seven steps for each restaurant took about eight to ten hours per week.

So we hit bandwidth issues.

So that Mike and I said, why don't we turn this into software?

If we can automate this process, automate the data digestion, automate the output creation, automate the updates, automate the changes, automate the distribution, automate the tracking, then we can apply this to a lot more people.

We did that.

Rolled it out of my own restaurants.

That was working, Saving time, saving money.

Then I started handing it out for free to my friends.

They started utilizing it and telling us they liked it a lot.

And then we said, I think we got something here.

We should probably take this to a broader market.

That was how five out got born.

Interesting.

Well, that leads perfectly into the main portion of what the show is about.

It's really not a pitch for five Out.

Even though I'm fascinated by your product, you know, I consider you to be one of the experts in the industry around AI and ML machine learning.

So.

Right.

Start heading down that route because everything you've explained so far is important to the conversation because you Identified very specific data points that help formulate the success of the output.

How should restaurants leverage AI and ML to optimize restaurant operations and customer experiences?

Let's have the complicated part of the conversation first and get it out of the way and then we'll have the simple of the conversation.

So the complicated part is what's AI, what's ML, what's the difference between the two?

So AI doesn't really exist in our world because it is, it is a computer learning on its own, teaching itself and creating on its own, and having its own mind and doing what it wants to do.

And as far as we all know, besides what we've seen in the movies, that hasn't been created and it certainly hasn't been rolled out at scale.

So we're not talking AI.

So then let's talk about ML.

So machine learning is learning, but not coming up with any ideas on its own and not doing what it wants to do.

Therefore it's simply programming.

Higher level, more complex programming, but programming nonetheless.

And so what that means is going into the simple part of this conversation is it's just automation, which is just technology, which is just replacing the horse drawn carriage with an automobile.

So for all those folks that are out there that are a little bit skeptical or nervous or scared or hesitant or anything that doesn't get you leaned in into the conversation around AI and ML, it's just another form of technology that's being introduced to us.

Just like the printing press, or just like the Internet or like the computer.

It's just one more thing that's here to help us in our life.

And I think when people come to that realization, which is going to happen for everybody in their own time, but the faster people come to that realization, and the more people come to that realization, the more quickly that our lives are all going to get a lot simpler as a byproduct of ML.

First and foremost.

That's the lofty, broad, flowery language answer.

Well, very controversial answer too.

So are you saying at this point in time AI is a misused term?

Yes, very much so.

I think that because of the complexity and the nuance of what the actual differentiation is between machine learning and AI, that a lot of people are lumping those two things together and calling one the same and making them synonymous.

And in fact they're very, very different.

And so what I want everybody to start doing is understanding that starting at the top, which I like to call ChatGPT, that's machine learning.

It's not AI, it's not true AI and everything, I'll call it below that, if that's the top and that's the gold standard, is also machine learning.

So we are not in the age of AI yet.

We're actually only in the age of machine learning.

And that's just advanced algorithms utilizing large data sets.

It's not thinking on its own, it's not writing its own course, it doesn't have its own thoughts.

And therefore it's not out to kill us, such as Terminator 2, which is great because we don't need that.

Okay, so I will remove AI from this conversation.

And by the way, I think that last statement is probably going to break the Internet when this podcast launches because we might have some conversations going on in line in online regarding that, which is.

I love those conversations.

I would love for people to point URI to true AI being deployed currently in the restaurant space, the hospitality space, or any other sector, because I have yet to see it.

So what are some of the biggest challenges that you faced when integrating machine learning into five outs platform?

The biggest challenge is that software development and machine learning are two completely different functions.

Almost the same as building an airplane versus building a rocket ship.

An airplane has all of its engineering requirements and its engine requirements, and all of those things are well understood and known by most of the same group of people.

But building a rocket ship is you have the engineering side and then you have the rocket science.

And it's a great example.

So if you want to build a software platform, you need engineers.

If you want to do something with machine learning, you need data scientists and other ancillary folks around the world of data science.

And you can't work one without the other.

So if you want to build a software solution that utilizes machine learning, you actually have to engage two independently skilled teams and bring them together and help their work coalesce.

So I guess what I'm saying is it's a lot easier and more straightforward to build software than it is to build software that utilizes ML.

And that was probably the biggest challenge, is learning that and then figuring out how to exist in that world where we had to bring those two areas of focus together.

So when creating this language model and this database, how do you ensure data security and ethical use of.

I keep wanting to use the term AI.

Now you throw me all off when handling sensitive restaurant and customer data.

I mean, what does that look like within the industry?

So I'll speak to our experience and then I'll speak to other folks experience that I can only, you know, talk about from A from a outsider perspective, as far as we're concerned here in, in our company, in our solution, we don't utilize any personalized data.

So we specifically, you know, block that out.

If anybody tries to give it to us, we tell them we don't want it.

Reasons are somewhat business related, somewhat security like you're talking about, but also we just don't need it.

We're not focused on the individualistic Persona, perspective, experience, we're focused on the backend financial data aspects.

So that's our experience, that's our walk of life when it comes to other people.

Now that we live in a cloud based world where data is mostly stored with Amazon and utilizing aws, which is where a lot of the government stores a lot of its data, I like to look at an equal playing field.

So if you're able to engage with AWS and I'm able to engage with aws, the United States government and IBM and JP Morgan are able to engage with aws.

It's not exactly apples to apples, but it's at the very least apples to oranges.

We all kind of have a fair shot at keeping our data secure.

Now we know starting up from the very top level all the way down to the simplest of solutions, there is data breaches and it happens.

And that's the world that we live in, that we, most of us have chosen to opt into, but not everybody.

So I think that we're all pretty secure or at the very least as secure as most folks in the world who focus on making it so that it is we are secure, stay that way.

So that's the way I think about data security and data privacy.

In terms of ethics, that's a whole other conversation and that we are no way, shape or form in a position to have figured out.

I mean I think at the end of the day, going back to talking about the gold standard of ML, when we're talking about open air, we're talking about chat.

Machine learning is machine learning is machine learning.

Data training is data training is data training.

Data sets are what's going to set people apart, no pun intended.

So whoever has access to the best data sets to put into their models.

And this is going to get interesting for everybody, skip it.

In the next one to five plus years it's going to be who has a data set that no one else can get access to, who has that the report from the United States government?

Who has that analysis from Bain, who has that Harvard study?

Who has that unpublished data set that they can put into their model that all of a sudden is going to lift it up and get it much higher.

So being able to tier up into different levels of data set and who can get the best data sets and who can, you know, unfortunately this is not a word I like to use, but obfuscate that data set from some of their competitors is what's gonna, is what's gonna help separate some cream from the rest of the pack, so to speak.

Yeah, it's interesting you say that because that has come to light recently.

We're Magic Gate is actually training a model for a very specific industry that will be unique and will be competitive and will be advantageous to Magic 8.

It becomes the proprietary information of our organization and having that unique data set really gives us a competitive advantage.

So I can't wait to talk more about that in the future, but totally get where you're coming from, Brandon.

We're going to take a little break and when we come back, we are going to dig into some of the, the insights that you can drive from this data that you're collecting, the future of AI and hospitality and a bunch of other great things.

So everybody stay tuned.

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Okay, let's pick up where we left off.

Let's talk about some of the machine learning driven insights.

How can tech companies use machine learning to help restaurants make better decisions about menu design, staffing or marketing campaigns?

Obviously I'm moving outside the five and out sector here, but I think there is education to be had for other vendors.

There's common misconceptions, especially for operators in regards to what this technology is and what it isn't.

So I'm very interested to hear your insight on that.

Yeah, so there's, there's a couple roads to think about when starting to understand what you might want to utilize from new technology perspective when it comes to machine learning.

And I like to, I like to simplify it as much as possible.

And we can say it's machine learning, which it is, but then we can also say it's automation, which it is, and therefore it's just technology.

So it's really just what new automation technology do I want to bring into my restaurant.

So as an example, do I want automation that answers the phone for me and indicates to someone whether they want to know what my business address is or what my telephone number is, or what directions to my business, or to be able to make a reservation or maybe even to troubleshoot a problematic scenario where it can feed me to a pipeline of this person's voicemail or this person's voicemail.

That's ML, that's automation.

That's technology.

That's one utilization.

Same type of ML can also be used to help people place orders.

So if you've been to McDonald's lately, you might have noticed that there is a voice that answers when you pull up and then there's a different voice that takes your order.

So there's actually an ML that's asking you if you want to utilize your telephone number to be able to be recognized, or if you want to use the custom app, or if you've placed an order with the custom app.

There's all these different AB testing that's happening.

And then after you answer that order, which is being asked by ML and answer is being logged by ML and a decision tree being utilized by ML, then a human being comes in.

And what does that allow for?

That allows for one human being to be taking someone in order and ML to get started on the process for the next person.

So what's that?

It's automation.

What's automation technology?

So it's ML, sure, but it's automation technology.

And that's simple and that's easy.

That's not anything that anybody should be scared of or hesitant of.

So that's another thing that I think that people can look to.

How do you see ML further revolutionizing the restaurant and hospitality industries over the next five to 10 years?

And will we see the true AI in that time period?

So that to me breaks down to the definition of hospitality.

So some people think the definition, definition of hospitality is the function of getting served, and some people think it's the service, and then some people think it's both.

So if I go to a hotel and I can check in and sleep in that room, then I've gotten hospitality.

And some people think it's the level of service that you get, and then some people think it's the marriage of those two.

That's a very good point because a lot of people view it as the touch much points and which with that, which adds to the value add of the proposition.

Right?

So if I can call a restaurant and an ML can Answer the phone and answer my questions and I can make a reservation or change a reservation or get a question answered, then that's hospitality.

But if I really enjoy that experience, if that ML sounds like I want it to sound and it makes me feel good about having done it and I feel good leaving it, then that's an elevated level of hospitality.

So the way I want to answer the question is can we first hit function and then can we get to form?

And if we can get form and function, then that's the perfect marriage of hospitality for most.

I think some people are still going to say I prefer that, that human element and aspect.

And I'm, I'm, I want to have my cake and eat it too.

I want to live in a world where both exist for forever, hopefully, knock on wood.

Some people want just one, some people just want the other.

But I think people figuring out what their druthers are on that, on that answer is going to be an interesting thing that we're going to see develop over the next five and 10 years is what does hospitality mean to you when it comes to something that's been programmed by a human?

Right, because it's not a robot you're talking to, it's a human being that programmed a voice.

It's maybe a voice recording and it's maybe a person that wrote that code.

So underlying there is a human at work, at play there.

So it's still going to be about human connectivity and human emotionality.

Well, with these advancements in these technologies, do you see automation in this area potentially replacing traditional roles in restaurants or hotels?

Or is it more about augmenting the human decision making process?

That's the million dollar question right there.

It might be the trillion dollar question.

Yeah, exactly.

I was listening to an interview by Mark Andreessen at Andreessen Horowitz this morning and he that the, the new ML that's going to be coming out is going to be able to decide what is the next best solution to code on its own and then we'll be able to execute on that.

And in that occurring, that whole process that you just talked about where there is no human intervention at a certain point is coming the next six to 12 months.

And as that's the case, I'm not certain who's going to outperform.

I want to say that the human being is still going to be able to outperform and is still going to be able to have more human empathy and more human sympathy just by simple definition.

But I'm not, I'm not ruling out the option or the possibility that the computer is going to be able to confuse the human and we're going to lose, we're going to lose an ability to be able to differentiate between the two.

And then when we hit there, of course we're right back up to the initial part of this conversation.

True AI when we can no longer tell.

Right.

That's the touring test.

So many restaurant operators may not be tech savvy and it sounds like you've built a lot of this technology that we just talked about into five outs offering which, you know, how do you make, how do you make these tools intuitive and user friendly to the operator?

I think there's nothing more important than that, frankly.

Simplicity is key.

And I also think that insights are a bad word and I think that helping is a bad word.

And I think that those things are currently where a lot of people are focused on and we're past that.

I think everybody should be past that to a point of giving something in its final state or near final state, it has to be delivered as an answer and we need to actually take it beyond that and get to what we internally call a closed loop system.

So as an example, you might send an email to someone and say, hey, I want to do this thing at work and I'm putting you on notice on Monday that if I don't get an answer back from you about Wednesday, I'm going to do that thing.

And so what we would like to do in our areas of focus and what I think other people are going to do is start creating similar closed loop systems where I'm actually going to create a schedule, a labor schedule for a restaurant and I'm going to send it to an operator, let's say on a Monday and say I'm giving you until Wednesday to edit, change, opine, pull back, what have you.

But after that, if you haven't made any of those changes or edits, I'm actually going to distribute that to your team and then those folks are the folks that are going to actually show up.

And we're doing the same thing with purchase orders and prep lists.

So I want to create an automated prep list which is how much food do I need to make for tomorrow?

I want to email that to the team in the morning, first thing and prior to I want to send it to whoever's the decision maker at the night time and say, here's the prep list the teams are going to get tomorrow, make edits or changes.

And if you don't by this time at 6am they're going to have those in their inbox.

Same thing for the vendor.

I'm going to put together your meat order, your dairy order, your fish order, your beverage order and I'm going to send it to you and I'm going to say you have this amount of time to apply and if you don't reply at a change or otherwise, I'm going to send it to your vendor, which means the next day that's the food and that's the beverage that's going to show up at your front door.

So that's where we need to get to, is not only well past insights and well past help and guidance, we got to get to a delivered answer, a finite, explicit delivered answer and we actually need to ship that answer to the next person so that we're not bottlenecking decision making, we're not bottlenecking on execution and that we're moving at speed.

Well, that was really my next question, but I think that's your, you've answered it and it was really around, you know, how do you balance the complexity of these data driven insights with the simplicity that restaurant operators often need in their day to day tools?

And it's really just handing them the results of what they're looking for, what they need, not necessarily what they're looking for.

It's absolutely similar as opening up a new computer.

So if you go get a new Apple laptop, you open the box, you take it out of the plastic, you open it, you turn it on and that's it and it runs from there.

There are so many complex systems running underneath the surface that none of it, well, most I don't and a lot of people don't understand.

There are many out there that do, but most don't.

But it just works just like a car, which I also don't understand how a combustion engine works, but it works and I go in and I press the on button and the car works and it runs.

That's the level that I think that the restaurant industry and probably other people as well, hospitality inclusive, need to be thinking about machine learning.

It has to be.

The end result has to be automated, deliverable action.

And anything else I think is a failure of our own ability to recognize what works as a solution in the hospitality industry.

I don't think insights work, I don't think data works, I don't think help works, I don't think guidance works, I think delivered actions are it.

And that's anything else I think is short sighted and it's it's doesn't have legs.

Brandon, in closing, if you could give one piece of advice to restaurant operators, navigating, and I'm going to say the word the AI landscape, what would it be and what would you caution them against?

Now, obviously there's questions they should be asking based upon our conversation today.

You know, I've said for years, a lot of smoke and mirrors out there when it comes to AI, a lot of wizard of Ozing.

And I think you have provided some very interesting insight today.

Like I said, it's going to cause some controversy and I can't wait to see the post on this when we, when we go live with the show.

But what is your advice for those operators looking to do go down this path?

I would, I would advise people to ask anyone that they're speaking to who is offering up AI and ML solutions to show them first and foremost what the deliverable is, not to start at the beginning of the story and say, why did you create this?

What does it do, how does it work, who does it work for, et cetera.

I would go to the very end of the line and say, what does the deliverable look like?

Show me the deliverable.

And then if the deliverable looks like it's something that your team can use and that you could put it into your work process and you can digest it and then it's going to be helpful for you, then I would start working backwards.

Where did this information come from?

How did you get to it?

What is the, what is the process?

Who else have you used it for?

That's how I would ask people to go about evaluating a solution.

Show me the end deliverable and look at it and say, could my, and would my team use this to execute in their restaurants?

And if the answer is yes, then start working your way backwards to the rest of that journey.

Brandon, I am so glad I brought you on the show.

I know we had a little scheduling issues, but this was a very worthwhile conversation.

I love your insight, I love your perspective and I am so appreciative that you came on the show to talk to our audience here today all about what is not AI, but more or less it's machine learning.

Great to do it.

Happy to be here and thank you for the invitation.

Boy, that was interesting.

I bet you might have a few questions or a few comments and I would love to hear your thoughts on this and continue the conversation.

If you'd like to do so, you can leave a message on my LinkedIn profile.

You can always reach out to me via Everything Social, Skip Kimple or everything magicgatetech.

This includes X, Facebook, Instagram and LinkedIn.

You can always go to the website skipkimple.com for all of the archived shows including the show notes which are posted there as well.

You can also hear these new episodes on the Magic gate website@magicgate.com and of course you can email me at any point in time@Skipagicgate.com next week on the Tech Chef Ken Garvanovits joins us to talk about transformative growth and leadership for high impact hospitality executives.

He is all about driving innovation, creating scalable growth and creating team excellence.

Known for his Amazon best selling book entitled Business Breakthrough 3.0 and his skills at public speaking, Ken shows us some eye opening opportunities to fine tune your leadership style.

Well, here is to wishing you a good productive week ahead as we push forward to conquer January together.

So until next week, stay safe, stay healthy and stay hungry.

My friends.