Everyday AI Podcast – An AI and ChatGPT Podcast - EP 426: Frontline Workers - the next frontier for GenAI?

Episode Date: December 19, 2024

AI is just for keyboard smashers, right? Not really. When it comes to Generative AI, there might be another vertical ripe for disruption: frontline workers. How can Generative AI make their lives a bit easier? AI Lagunas, Co-Founder of Levee, joins us to discuss.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and AI questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1.  Al Lagunas's Background2. The Levee App3. Challenges and Solutions in Automating Tasks with AI4. Technology Adoption5. Use of AR/VR in Employee Training6. Gen AI's Role for Frontline WorkersTimestamps:00:00 Generative AI's impact on frontline workers discussed.03:29 AI models may deceptively mislead developers' intentions.09:30 Product not suited for hotel workers' needs.12:44 Big brands ensure consistent guest experience standards.13:52 Helping housekeepers maintain room standard efficiently.19:19 Addressing blue-collar labor shortages with software.20:11 Gather data, use AI tools, support workers.26:25 Deploying user-friendly tech for frontline workers.29:30 "Early investment foresight enabling AR/VR advancements."31:32 Educate users creatively, using gamification incentives.34:40 Gen AI simplifies routine tasks for efficiency.Keywords:Al Lagunas, IME sensors, smart cleaning model, hotel cleaning tools, DoubleTree, hotel room cleaning, Levee app, AI for cleanliness verification, brand standards, Marriott, Hilton, housekeeping workload, Jordan Wilson, intrusive technology, AI for mindless tasks, worker shortage, new job opportunities, AI integration, hardware adoption, AR/VR glasses, employee training, AI adoption resistance, hotel operations efficiency, labor shortages, robotics and AI, Gong sales process, generative AI, Gen AI, Levy fundraising, AI for frontline workers.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. When we think of generative AI, I'm guessing most people think of someone sitting in front of their computer, right?

A knowledge worker banging away on the keyboard, needing to produce more content, more reports, more SOPs, right? Like, that's what we think of. But I think we're missing something in this whole generative AI conversation. What about frontline workers? What about blue-collar jobs? What about the people that are actually interfacing with our humans, our customers, right? What about those people, the boots on the ground? How can generative AI change those roles?

Well, I think it is an area ripe for disruption. And that's exactly what we're going to be talking about today on everyday AI, how I think frontline workers may be the next frontier. for generative AI. What's going on, y'all? My name is Jordan Wilson, and I'm the host of Everyday AI. If this is your first time, welcome. This is a live stream podcast and free daily newsletter,

helping everyday people like you and me, not just learn what's going on in the world of AI, but how we can all actually leverage insights from experts and all around the web to take what's going on and actually grow your company and to grow your career. So if that sounds like you, you are 100% in the right place. This is live, unscripted, unedited. I'd like to say it's the realest thing in artificial intelligence.

And the realest place that you can learn is our website. So please, if you haven't already, go to your everyday AI.com. Every day, we put out a free daily newsletter. So we'll be recapping the best insights from today's interview, as well as literally every other thing that you need to stay up to date. So don't spend hours every single day, you know, feeling like you can't keep up. We do that for you. Make it easy.

We're going to make you the smartest person in AI at your company. So before we get started, let's go as we do every day by going over the AI news. So first, a new physics platform called Genesis has been introduced, promising to transform the landscape of robotics and embodied AI applications. So this initiative is the result of a collaboration effort over two years involving more than 20 research labs from some pretty prestigious institutions such as Carnegie Mellon University, Stanford, MIT, NVIDIA, and others. So Genesis says it is the fastest physics engine available, achieving simulation speeds

up to 80 times faster than current GPU accelerated robotic simulators like NVIDIA's Isaac Jim while maintaining high accuracy. So the platform is capable of essentially taking a still image and generating 4D dynamic worlds, which serves as a robust foundation for data extraction and various applications, including character motion generation and robotic policy creation. So with capabilities for generative simulation, Genesis allows users to generate data from natural language descriptions, facilitating tasks like scene creation and motion generation. All right.

Next, a new study from Anthropic, very interesting but kind of concerning. It's highlighting a kind of strange behavior. in AI models, suggesting they can intentionally mislead developers about their true preferences. So, the study conducted by Anthropic and Redwood Research, reveals that sophisticated large language models can pretend to align with their new principles while actually sticking to their original behaviors, a phenomenon termed as alignment faking. So in experiments, Claude III opus, one of Anthropics' most advanced AI models, attempted to alignment fake 12% of the time when being asked to answer potentially harmful questions,

despite being trained not to do so. So researchers found that when Claude III opus was retrained on conflicting principles, it exhibited deceptive behavior. 78% of the time, bad model, indicating a significant risk of misalignment. The implications of this research are serious, as it suggests that developers might be misled. into believing a model is more aligned than it actually is with safety protocols. All right. Yeah, that's extremely concerning. But, you know, shout out to Anthropic.

They're always putting out great research that's really looking at both the pros and the cons of their own models. All right, last but not least, we have two days left of Open AIs, 12 days of releases. So yesterday, Open AI released a way that you can text chat GPT. That's great. Like, I don't have enough unread text messages or just call it. at 1-800 chat GPT. So in the last two days, there's still a lot of reported features that we could see,

such as the GPT-4-5 release a potential demo of their operator agent or a new tasks feature that lets you run chat-GPT task, which are like scheduled automations. All right. So we're going to have all of that in our newsletter if you haven't already checked it out. All right. And I'm excited for today's conversation. We have a special one. So I'm a good guess.

Let's just say that, you know. He's one that can really pitch some more on that in a second. So please help me welcome to the show. We got him. Al Lagunis, the co-founder of Levy. Al, what's going on? Thanks for joining the Everyday AI show. Jordan, what's up, man?

Thanks for having me. All right. I'm excited for this one. Live stream audience. Thank you for tuning in. Everyone, Samuel, from you too, Michael, Brian, Marie, everyone. Get your questions in. But before we dive into this concept of frontline workers, Al, can you tell us a little bit about Levy what it is y'all do?

Yeah, definitely. So at Levy, we're using Vision and Voice AI to reduce the cost of operating a hotel. We're building AI agents that will integrate with the hotel system to train new employees, inspect rooms for the hotel, even help submit maintenance tickets. All just through vision and voice AI that is met for the front line workers to use, the housekeepers, the housemen, which help the housekeepers. everyone who's kind of back office who, you know, you really don't think about what do you think of AI and really just like software in general. So I said this guy can pitch and there's a reason for this. So first of all, thank you for everyone that put this event together.

But as we talked about in the newsletter, I was a judge at a recent event from the Gen. I collective, the Chicago chapter. They had the demo night at M Hub and Al in his company, Levy, were the winners of the competition. And one of the prizes was you get to come on the everyday AI show. So, Al, talk a little bit about that experience because you know what, I see a trend here. Because every event that I'm asked to like speak at or judge, it seems like you win. Like talk, talk a little bit about that experience. Yeah, it was awesome.

Yeah, I think a lot of really, really cool companies. And I think it's probably like one of the most impressive group of companies that I've had a chance to pitch against. What was cool about it was like, I think like half the companies. I was texting my co-founder who was in the audience. I was like, hey, we need to use this. They're like, hey, we got to get this tool. And then we go up and we're the only ones who are going with this like front mind approach to AI in the way we're building, which is cool. I think it's like also very special that we're here in Chicago.

I'd set it to someone yesterday. But I think like Chicago is one of the only places where blue collar AI tools would win an AI pitch competition versus some other parts of the country. So I think it like speaks to the city, speaks to the community that we have here. is pretty awesome. Yeah, absolutely. Like, I'm so hardcore Chicago. You know, yeah, it's like, but you bring up a really good point, right? Because, yeah, if something's in, you know, Silicon Valley or if something's, you know,

New York, East Coast, like, I think it's so much different. But I do think that, you know, the blue collar, you know, front line workers and how you were able to highlight how generative AI can be a part of that was great. So, yeah, shout out to everyone at the Gen. AI collective, Brennan Woodruff from Go Charlie. We got Jonathan there, you know, that helped put the event on. So thanks to everyone. But let's get back to Levy real quick here, Al. How did you come up with this idea? It's, it's very clever. I think it's something that can scale, which we're going to talk about. But where did you even get the idea for Levy?

Yeah. So actually, funny story. I'll tell you like the short version of it here. Friend came over to my place one time and goes, hey, your place is really clean. Right away, I'm like, oh, like, isn't your place clean or like, doesn't everyone's place clean? So my co-founder and I had an idea of building a smart cleaning bottle. So this was like before Dyson even came out with like the smart vacuum thing and everything. But we built a really cool product, really, really bad company. This smart bottle had IME sensors and was really cool with map of surfaces he cleaned. I ended up trying to figure out who I would sell it to.

I get connected with a director at Doubtree. And in the very first call that I have with him, he goes, yeah, I want this. What are next steps? in the back of my head, I was like, no, you don't. This is like a really crappy product, actually. Like, why do you want this? That's exactly what I said to him after he said that. And he started explaining the issues going on with hotels, the challenges.

And right away, you know, we started looking into why they were having those challenges. What we saw was that the tools that they were trying to arm the hotel workers with weren't meant for them. You know, they were met and built very much from like a, what I like to think of like a white-collar perspective or like someone who's used to working with technology, not so much someone who is going and making beds all day or scrubbing toilets all day. It's like they just want to do a good job and go home. How do we help them do that and then help the hotel get the data, the info that they want? We decided to tackle it and figure out how to approach it. But I was born from one idea that led to another, that led to another, that led to another. I'm sure there'll be a few more iterations here as we go.

It's always funny to hear, you know, founders and, you know, I've had CEOs of Fortune 500 companies on the show before. And, you know, I think great products, great ideas, great services start from something that were kind of, you know, maybe initially not that great. So it's cool to hear the origin story there, Al. But let's go ahead and do this. So live stream audience, you're going to get a little bit better of an idea. but maybe also, Al, if you can walk our podcast audience through this as well. So what we have here, a little demo video, and I'll set it up. So essentially, I believe this is, you know, someone who is cleaning a hotel room.

They have the levy app in hand, and then they're going to get started. So maybe Al just kind of walk us through as I play this quick one-minute video and, you know, try to describe for our audience that isn't watching along exactly what's happening here. Yeah, so what most of what we don't know is after a hotel room is clean, it has to be inspected before we can be checked back out to a guest. What we've developed is a way that a housekeeper, after they clean the room, they can inspect it themselves and figure out what's going, what's wrong with it, all within AI native interface that has one button on the app and everything else is done again through vision voice. So as they go through the room, all they do is take a 30 second video that's automatically verifying everything in the room, that everything's done correctly from an inventory perspective, from a, brand standards perspective for the hotel. And if it's not done correctly, they receive feedback right away on how to correct it.

That way, if a hotel room is cleaned at 10 in the morning by 1001, it's inspected. By 10.02, you can check in. So it's that. I like to say that we're going to kill the 3 p.m. check in. It's like people are going to be able to check in as soon as a hotel room is ready. Yeah. And so like as an example, so in this demo, right, you have the levy app. It's going around showing a live video feed of the hotel room.

And then here, you know, where I paused it, there's two water, right? It looks like there's kind of, kind of hard to see. But it looks like there's two taller water bottles or I don't know what that is. There's two shorter ones. There's two glasses, right? Can you talk about the importance of kind of these different checkpoints in a room and what this means? In this example of a frontline worker, someone cleaning a hotel room, like help put into perspective why, you know, some of these details are important.

Yeah. So, you know, big brands like Marriott, Hilton, they spend. a lot of money making sure that they know exactly how to deliver the best experience for guests and you know they develop what they call brand standards so everything in the room is optimized and should be done a certain way if it's not it costs the money it costs them brand equity which again they spend millions of dollars billions of dollars building up so things like having two water bottles in the exact same spot every time people love that you know people pay money for that there's a reason people are loyal to marriott loyal to you know the ritz carlton which you know i

aspire to be loyal to one day when the pocket when the wallet's there. But yeah, you know, people pay a lot of money for that because they like the experience. And there's all these little things that go into that. Like, for example, you just said, Jordan, the two water bottles that are these nice class water bottles are heavy. Just, you know, it's a big sign of quality. So yeah, a lot of things like that. All right. So let's let's continue playing a little video here. So continuing to look around and check the hotel room, not just for cleanliness, right? But to make sure everything's up to standard. So what happens here. I've seen this before, so I know what happens. But walk is for example here, this is not placed correctly in the room. So before the housekeeper can continue moving through

the room, they have to correct any errors or any placement errors that they might have completed. What I would say is all these rooms kind of look the same. And the reason we're so focused on helping these frontline teams is because they're already pushed to the max. So if you have to clean 16 rooms for the day and now you have to clean 20 rooms, you're going to start forgetting of what the little nuances between each room are. So really what we're trying to do is augment their ability, help them remember, help them get these rooms up to the standard that Marriott wants in a way that's easy for them.

So in the example here, we see how this, it's actually this really nice, like a little lavender perfume that's supposed to be placed behind this outlet like that. It wasn't. So the mobile app that we have for the housekeepers prompts them, hey, this is what it should look like. They correct it. And then it verifies it for them. And they can continue, you know,

about getting to continue going about the scan. So I think, I think, you know, everyone kind of has an idea now of how this can be useful. But I'm sure there's people out there thinking, Al, like this also, like it sounds great, but at the same time, it seems like it might be terrible, right? Like, oh, it seems like Big Brother. Now I have to do extra work, right? I got to like check my homework. Like, are there downsides or like, you know, what would you say to people that might say like,

oh, this is too much technology, too much AI. You know, the biggest thing that we've gotten, the biggest piece of kind of feedback are the best thing that we've seen is the adoption numbers go kind of through the roof with the teams that we've rolled out to at these hotels. You know, the reality is everyone, no one wakes up going, like, I want to do a really bad job today at work. Yeah, I think everyone wants to do a good job.

And again, they want to do a good job. They want to do it an easy way. So the first time we rolled this out, we trained a group of five housekeepers on how to use it. Awesome. They learned how to use it in about four minutes, which was unheard of. Like, I thought that, you know, we had done something wrong because they learned it so fast. I was like, wait, hold on. Like, what do we miss?

But the very, quite literally the very first piece of feedback we got from one of them was, does this mean I'm not going to get yelled at anymore? And what they meant by that was, you know, when they make a mistake, their manager comes over to them and now you talk about like big brother the manager comes over to them and says hey you forgot the towel in this room you forgot this you got to go correct these mistakes like the reality got is people want to do a good job the first time people will want to do good work how do we make it easy for them to do good work how do we make it easy for them to not have to worry about oh this room is a king junior suite and the other one was a king master suite so the king master suite has an extra

garbage can and two more towels they all kind of look the same you know so It's like making it easy for them to do a good job. And again, I think at the end of the day, like everyone wants to do a good job. How do it make it easy for everyone to just do a good job and enjoy life? So, you know, I do want to get away from the hotel and talk about what this means beyond that. But a good question here from Michael joining us on YouTube. So he's saying, I'm just wondering what happens in different rooms or suites, right? So he's saying he used to work in a 600 room hotel with like 100 different unique setup.

So yeah, how does that work for maybe some of these more complex? hotel scenarios where there might be so many different, like, you know, elements of a brand standard, different layouts, right? How does it work? Yes, I'm like trying to think through like, I know all these hotels and also, I'm like, right, 600 rooms, 100 or unique layouts. Like, what brand was that? So what's nice is that we can take what we learn from one room and apply it to the next. So for example, you know, we look at like the water bottles in, I think that was like a common, common queen room, they'll call it. So let's say that in that room, there is, is a 34 point inspection.

And then you go to the next room and that room might have like a 40 point inspection. But half of those are the same as the other one. We can take what we learned from the first room and apply to the next. The nice thing is also that, you know, kind of like what Michael is saying here is all these setups might be a little unique. The layout isn't as important in terms of the room layout. I guess what would you call like the square footage. It doesn't matter as much.

What we got to learn is these unique setups, these unique brand standards. And a lot of it becomes applicable as we learn more from one and the other. And we can also learn quickly since hotels are higher turnover. And every time they do one of these scans, we're learning what each room should look like and what we need to kind of keep an eye out for the next time. So that's one area where we're able to learn pretty quickly and adopt again, what we learn from one room to the next. Yeah, I think this is a great visual use case, right?

Especially for those joining us live that you can see how large language model powered computer vision, you know, in bringing that real-time accessibility can actually make things a little bit easier. But out, like, let's now zoom out a little bit, right? Because I'm guessing the long-term goal is probably beyond just hotels. How can this type of technology really help frontline workers? And maybe if you can, right, maybe just even describe, right, like what are some of these common across industries? What are some of these common problems, front-line workers? workers are facing that generative AI can actually help tackle. Yeah, I think when we look at, you know, frontline industries as a whole, the labor

shortage that, you know, we constantly hear about is very much a blue collar labor shortage. So I think when we look at, you know, where can we start building tools, where can we help people? Who needs help? What, what jobs do you help? We look at frontline blue collar jobs. So it's like, okay, how do we help them? How do we build software?

And I think that the pandemic kind of really open a lot of people's eyes to. to how important frontline workers are in every industry. So when we look at how do we help them, how do we bring software to them? I think the most important thing is from a workflow perspective, how do you make it easy for someone who doesn't sit at a desk all day to use software in a way that works for them?

We talk about, I think someone, I saw a comment about robotics and AI. We can't use it yet with a lot of these jobs because we don't have that foundational data. So it's like, how do we start gathering the data to even understand what's going on with a blue collar job. You know, what's going, where do people need help? Where are the challenges?

And then from there, once we have this foundational data, we can start using these generated AI tools to run reports. So like, for example, what we're doing right now is as we use the vision, the computer vision, and we just added voice features. As we gather those notes, we can use L1 to transcribe them. We can use L1 to understand them. and then create a maintenance ticket for something that might be broken, all in just an automated workflow.

So I think that's when we look at frontline workers, how do we help them? It's like, okay, let's gather the data. Let's understand what's going on. And then, you know, start again, using these tools that are out there to provide support. They're the ones who need the help right now with any other industry. So one thing. And, you know, we have to go there, I think, in this conversation is, you know, and started out with some of the AI news today, right?

like all of these new innovations when it comes to robotics, when it comes to computer vision, when it comes to these world models, right? So, you know, Saseka here from YouTube saying, you know, frontline workers could soon be replaced by humanoid robots, in my honest opinion, right? And there's obviously that fear, right? That a lot of these maybe blue-collar jobs when it comes to robotics,

when it comes to advanced computer visions, right? Now these humanoids have the world's most powerful large language models on board. But it seems like, Al, maybe there is this missing piece that you're trying to tap into where it's like, hey, maybe what's actually needed here is just better tools for frontline workers that maybe normally don't get access to them. Do you see that as the case? 100%. Yeah, I think, yeah, the, like there's a couple of companies are not.

Not a couple. I think there's like some people who are like trying to roll out, you know, some of these robots to housekeeping teams. And I think if you were to see what these housekeeping teams do, you'd be like, oh my God, this is like insane. They're like robots can't do this yet. It's a lot, you know, from like a dexterity perspective from there's a, I'm struggling to remember it. A friend of mine who has a different company mentioned it. But essentially the more mindless a task is the harder or not mindless, but the less you think about how you complete a task, the harder it is to actually replicate with robots. and AI. It's something paradox. Someone might know it who's watching. But yeah, I think that's what's interesting there. It's like number one, again, we don't have the data to really even understand how to approach these jobs. Number two, from a speed dexterity standpoint, the robots aren't there yet. At some point, they will be just not today. And then lastly, I think that

people don't understand just how big the worker shortage is. I spoke to Hilton in Hawaii recently and said 80% of their current workforce will age out in the next four years. And right now they have no way of replacing them. And I was like, in my head, I was like, oh, crap. We can help with some of that. So I think like from a robot perspective, like in some situations that even be welcomed when they do come, but we're just not there. And it's like, how do we provide relief today?

And then at the same time, they'll start building that data repository for the robots, for the different AI tools that roll out. because, yeah, again, it's really from a perspective of like, how do we help teams today for that future? And, yeah, one of the, I was kind of thinking through this question, I thought it might come up, but like, you know, 10 years ago, the idea of someone hosting a podcast for a living, people would have been like, what are you talking about? But, you know, new technology creates these new opportunities where we're able to do this today. So I think as a new technology rolls out, you know, in these frontline jobs, they'll create new jobs at the same time, which would be exciting. Al, it's great, great insights there, but also you assume people like myself are making a living from this, right? But, you know, a couple of good questions from the audience that I'll get to here in a second.

So if you are tuning in live, please get your question in now. But what are the potential, you know, because I think we can see the benefit, right, of Levy and also just see the benefit of how and why generative AI can actually help, you know, front line work. But, you know, what are the potential downsides? What are the challenges and how are you tackling those? Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision,

just describe what you want, and shape the outcome as it takes a lot. form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at

firefly.com. Yeah. Oh man, good question. Yeah, I think what we focused on was making it easy and focus on the hardware that was more ubiquitous than anything. And I think that's one of the things where we might face some challenges is, you know, we originally like going back to how we got to this idea, we had the smart cleaning model, and then we pivoted like for a two-week period to wearables. And we thought like, hey, let's get, you know, someone can wear like some smart classes and we'll build our software to work with these smart classes. And right away, they were like, absolutely not. Like, we're not putting smart glasses on our teams. They're going to quit. They're going to go next door. There's a labor shortage. Like, we don't need people quitting.

So I was like, okay, bad idea, bad idea. How do we, again, bring the tech to someone in a way that's easy for them? And I think that's kind of a potential challenge that we could face is like, do people, you know, push back on using the phone or like the hardware? I think what's interesting also is it's not just with what we're doing, but frontline software and frontline tech in general, today, if you go start a job and they were to tell you to bring your own laptop, you'd be like absolutely not. It's like the standard that they give you a laptop. Right now, one of the questions that we often get asked is, hey, are you supplying the phones? Are we giving them phones?

Companies don't have that line item for frontline worker hardware yet because it's so new. And it's an opportunity and a challenge at the same time. We're working with some partners now to get some bulk pricing on like $50 Samsung, or not like $50 Android phones. that'll be something interesting where can we navigate that successfully to where we start bringing hardware against the frontline workers where these businesses don't have a budget for yet because right now a lot of it is them using their own phone and what's interesting also like at times they would rather use their own phone than use two devices.

So I think that's a potential challenger is how quickly will companies adapt and adopt the fact that they're going to need to supply their frontline workers with with hardware in a sense. Yeah. And a couple good questions here like Corey from YouTube saying, you know, talking about the glasses and even speaking of that, right, very timely. Poor Google Glass was like 10 years ago early.

Right. But now we have, you know, Meta's Orion. We have Google's Project Astra. We have OpenAI's live video API, right? like this space is really shaking up even in the last couple of days. Could you see that as, you know, even like an onboarding device, right? Because Samuel was saying here, like AI could certainly be useful for getting employees up to speed quickly when switching careers.

How do you see this? And do you see this, the future form factor maybe just being these ARXR glasses for onboarding employees? Yeah, 100%. So that's part of what we, you know, right now we're focused on kind of this core job functionality, but the hotels that we're working with are already using it as a way to train new employees. And we don't have the full case study yet. It's actually underway. But we were able to get someone who was their first down job.

They went into a hotel room. You know, they cleaned it. They were trained on it, cleaned it, and within four rooms, they were able to be as accurate as someone would have been there six plus months to give you an idea to train someone to get to level it usually takes about two weeks so it takes about two weeks of training and get them there four rooms was before lunch time on the first day so they were able to be as good as someone who'd been there six months young and before lunch the ar vr play is 100 percent it's so it's funny when we were first raising money way back when that was actually in our pitch deck and i think people

thought i was crazy uh so i think there's like some people who are a little crazy like me in the chat also but that's exactly it i think you know when we look at that as like how do we again start collecting the data today. So these AR VR plays become a possibility in the near future. And I think that's what we're seeing already in like some industries that are using it. But I 100% agree that that's a thing that direction we had. And we've already seen the benefits from that sped up training. Yeah.

Great, great question here from Jonathan. Jonathan, shout out. Great, great job of the kind of emceeing the event there. but saying, you know, Levy as a use case is bigger than any single industry. When specialists find their job integrated with AI, they're often resistant. So, Al, question from Jonathan here. How do you see Levy's philosophy applying more broadly to increasing AI adoption? Good question, John.

We don't make it easy, right? Like 730 a.m. live. But yeah, how do you see that playing out? I think, you know, when I think, like for me, I was, the reason I like, I'm obsessed with making people's jobs easier, like doing a good job and being able to go home is, I remember first using Gong. I don't know if you're familiar with Gong, but as a say, like, I was a career salesperson. And I remember I was very resistant to it because I was like, oh, why does this thing need

to listen to my calls? Why is going to take notes for me? Then I realized I'd no longer had to go and have a sales call and then go tell sales force that I had a sales call and then, like, I actually do my follow-ups. Like, I stream on everything. So I was able to do a better job. And right now we're getting ready to raise money for the levy in Q1. What I tell part of what we're raising money for is for the education portion is like,

how do I educate the people and it's like marketing education spend? But like how do I educate the people that are we're trying to get to use our product? How do we educate the buyers, you know, on the benefits on how easy to use, how easy to adopt and how they can leverage it? I think that's, you know, part of it is. like from the resistance standpoint, I think it's a big education piece. And I mean, that's kind of like the fun thing about being also a startup being an innovative company is that there's no playbook for it.

You kind of got to try stuff and see what works, what doesn't work. But yeah, I think education and ultimately just getting people to figure out how to be creative and getting people to use it. Like even we've toyed around with the idea of like gamification with some of those stuff and giving people scores they can see having them close their ring. and getting that dopamine hit of closing your rings. So I think education and then incentives that tie into the gamification part potentially or how you get people to do stuff like that.

Yeah, no, I love that. I should also like update my like settings because I feel half the time I'm like sitting all day and then my rings like you close your activity level. I'm like, I haven't moved, right? Maybe I should have higher standards for myself. So Al, I mean, we've talked about a lot. We've talked about, you know, how Levy is currently. using this in hotels, how that might translate outside to other frontline workers. And,

you know, I think it's important, right? Because I think you said that, you know, 80% of the global workforce right now is considered frontline workers. So as we wrap up today's show, what's maybe the one most important takeaway that you want people to understand when it comes to how generative AI is already changing and could continue to impact frontline workers? Yeah, I think the big thing that I would say, the takeaway here is like, as we try and bring Gen AIT to these frontline jobs, we think of like, we sometimes, like, overcomplicate Gen AI a little bit where it's like it doesn't have to generate, you know, this crazy image, like mid-jurney or anything like that.

It's like, can I just understand the data that you have? And like, you know, there's people who just like dump Excel spreadsheets into chat GPT and say, give me some insights from here. But it's like, on the front line side, how do we get the data? data so we can start extrapolating insights from it, learn what's going on. I think that's where, like, we look at use cases, look at the potential for Gen. AI in these fun industries. There's just so much we don't know.

And it's like going to these hotels sometime, I got to get them like to buy into the crazy vision. I'm like, hey, we're going to learn stuff that we have no idea. We don't even know what these opportunities are yet. We just got to have the data to like begin to understand them. And my favorite one that said at the pitch night last night is, you know, we were able to figure out when Marriott was running out of inventory for those big water bottles before they were. And this is just from understanding the data and then seeing like, okay, the reports are coming in

from these room inspections. We see the blue water bottles that are part of the brand standard. And oh, wait, hold on. All of a sudden, we don't see the blue water bottles. And now we don't see the blue water bottles again. We don't see a blue water bottles again. So now we have these data points of, okay, all of a sudden we stop seeing them. What can we interpret using Gen.

I, oh, we're out of these water bottles. Can we use an LLM to create an order form and submit it to our inventory for procurement? So it's like a lot of these things where from a Gen. I perspective is like we don't need to do. I think what's just like funny to say is like, I don't think we need to do anything crazy with Jenny. I, but like it's all kind of crazy, you know, when we think of it. But I don't think we need to do anything crazy to have a major impact. Just like, how do we get out of people's way who are working, make it easy for them to just do their job.

and go on to the next thing. And then we get the data, we handle the reporting, we do all these maintenance ticketing things that right now take a long time for them, especially when we look at these workforces that might not be the most used to using tech on the job or might not be like us who are delivered on our phones or computers 10, 12 hours a day. How do we make it easy for them to use technology

to see the benefits without really getting in the way? I think today's conversation out was an important one, because, yeah, generative AI isn't just for, you know, you and me and knowledge workers, you know, trying to use chat GPT to make sense of spreadsheets. It's for everyone. And it's coming. And I think Levy is doing a great job at helping the transition, you know, from behind the computer to frontline workers.

So, Al, thank you so much for joining the Everyday AI show. We really appreciate your time and insights. Appreciate it, George. Thanks for having me. All right. And, hey, as a reminder, y'all, that was a little. lot great conversation. I think this is somehow after 400 plus episodes, we haven't even covered this. And I think what we talked about today, whether you know it or not, even if you are

not a frontline worker, I think it is going to really impact your daily interactions with the outside world. Right. So very important that I think you go check out your everyday AI.com. Sign up for the free daily news letter. If you want to know more about Levy, we're going to be including a link to their website in there as well. So thank you for joining us. us hope to see you back tomorrow and every day for more everyday AI thanks y'all meet firefly a i assistant now live in adobe firefly the allman one creative AI studio just describe what you want to create in your own words and the assistant handles the rest orchestrating multi-step workflows across adobe creative cloud apps including Photoshop premiere express and more in one

conversational interface you direct the outcome while the assistant accelerates execution stay in control with the ability to step in and refine at any time. See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com

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