Get Ready With Me: Generative AI Webinar Prep, Part 3 of 5

In today's episode, this is part three of my get ready with me series on how I prepare for a webinar using generative AI. So let's get right back into it. Create a checklist of employee engagement strategies, tactics, and methods that an individual property manager could use to audit the property's employee engagement practices. So we're not trying to have solve this for like all of say Hilton, but if you have a general manager for a specific location, go through this checklist and say, okay, are we doing the basics? One of the things about best practices, you know, a lot of people crap on best practices and say, oh, you'll never be a leader.

Uh you'll be an innovator with best practices. Well, no. This is like get your house in order with best practices first. Get a solid basic foundation. Because you can't innovate if your basic structure is broken, right?

If your if your employees hate you and your customers hate you, then you have no amount of AI innovation is going to fix the fact that your employees hate you and your customers hate you. You need to adhere to best practices to achieve a minimum level of competence. That's what best practices are, a minimum level of competence. So with this checklist, right, we can say, and uh, this is what goes in the presentation. You should do this.

So we're gonna swap out our real estate example here for a generation. One of the things that we forget when we're doing webinar setup is that this is this is actually valuable in and of itself. So you can take the data from something like uh this Gemini example, and it comes out in markdown format. Let's turn it into a PDF. And from that PDF, we should now have a hospitality engagement best practices checklist, right?

So it's a two and a half page document, which is pretty decent. Do these are you doing these following things? Now, this is this is a tangible benefit. This is something that you can give away in a presentation. You could say, hey, if you want to download our hospitality uh engagement best practices checklist, here it is.

Anytime you're working with generative AI, I mean anytime you're working with data, period, but especially with generative AI, there are all these byproducts of the work. So I'm getting ready for a webinar, I'm building the pieces for this webinar, and this is a byproduct of it, right? Um I could take this piece of content, which is very good, right? It's not groundbreaking, but it's a good set of best practices, and it can become part of the the deliverables from the for the webinar if I wanted to be. So as you're using generative AI, don't just use the tool and say, okay, I did the thing, whatever.

Capture some of its outputs and its byproducts and make use of them. Find a way to take that thing and make use of it. Now let's see, go on through our examples here. We have some more basic examples that we are commonplace. This one is an interactive example.

So this is uh this is from the mortgage one of how we used generative AI to create a best practice thing uh for technological and for um yeah, for for building your own software. An example here would be how could we use it for if we go back to our major use cases, right? Um if I'm a property manager at a hotel, how would I how would I demonstrate technological innovation? Maybe in generation, I could create a custom GPT or another uh type of service where people could interact with it and get ideas for visiting the area. Hotels already have this information, right?

Go to any hotel website and says, hey, here are the things that are of interest in our area. But if you knew your customer really well, if you had uh some decent data, you could build a an interactive experience. So let's pretend uh what's a good hotel? This oh, there's the Boston Marriott Newton. That's a decent hotel.

Oh, the aloft. There's no loft near my house. Um, so let's do this. Let's build a let's use Gemini and start a new chat here. Say, let's plan some day trips in the Metro West Boston area.

I'm staying at the loft hotel in Framingham, Massachusetts. I'm a Gen Z traveler who is interested in experiences as opposed to more tangible purchases, and I'm really interested in off the beaten path, unique experiences that avoid tourist traps. I enjoy nature and short hikes and walks. I don't enjoy tourist traps. I don't enjoy large crowds.

And I'm not especially interested in shopping. When I dine out, I try to avoid chains and support local independent businesses. Based on these criteria, help me plan some interesting things to see while I'm at the aloft. So this is not a super complex prompt, but it does express the preferences of a very specific kind of traveler. So if the hotel manager has that kind of knowledge about the guests, because we saw in the analysis, um, you know, the merging of business and leisure travel was was kind of flagged in there.

Um this is going to come up with some recommendations. Let's go back to our our document set and ask more details about this because I think this is there's something interesting here. Uh, let's see. Outline the key points about the blurring of business and leisure travel. Remember that again, if we've got the data, we should not just do an initial survey of it, but actually continue to ask it questions so that we can dig drill down and know better what our webinar audience is going to be interested in, know better how to present the information to them.

So in this case, we have the blurring of business and leisure travel, traditional lines of blurring, uh, rise of remote work, work life integration, shifting work culture, demand fluctuations, evolving guest expectations, guests leisure. That's a terrible uh logism, leisure seek amenities and experiences, both to professional and personal needs, flexible offerings, personalized services, technology, integration. So let's think about this rationally. A business traveler is not going to have three days off, right? But they might have an afternoon, they might have some time to kill before flight, they might get done with a work meeting early.

Um, they might want to hit the town, um, but they won't have an evening. So let's take a look at our recommendations now. Say this is great. However, we are catering to the leisure traveler, the business and leisure traveler who has only a few hours to spare. Refine your recommendations for this traveler.

Garden stroll and tea time at Garden in the Woods. So this is really terrific here. For the generation part, what we can do is actually have this thing create create travel recommendations. So we're going to screenshot this. This goes in the presentation based on our knowledge.

Let's copy our recommendations. And now what do we have? Of course, we have a guide, right? So you can make PDFs of these things. Um, and this could go on your website.

So let's go ahead and actually. Should we do an animation of this for the website? Nah, you know what? I'm just gonna leave for the for the hotel and and travel uh hospitality webinar. I'm gonna leave this as is and just discuss it in there.

But that's an example of a generative AI task where we're generating something that's for a very specific market segment to cater. And again, we're going back to our key challenges, right? Always staying focused on these key challenges. What the and the experience economy in this case is is what we're catering to. Our next use case is extraction.

Um, this is a case where we're gonna want to find some data about hotel travel. Maybe we can find a data set about hotel reviews. So we go to Perplexity and we say find a data set of hotel reviews. And we have the Carnegie Mellon hotel data set review. We've got a GitHub.

This is a 2020 uh vintage here. The Carnegie Mellon data set says 43,300. Uh, let's take a look at the zip file here. Okay, so this is really good. This is an export of data crawled from TripAdvisor.

So let's go ahead and take a screenshot of this because this is the kind of ugly data that people would be interested in being able to parse. Right. So we're gonna put that into our presentation here. And now let's go to our generative AI model, start a new chat, and let's go ahead and get let's get a couple of a couple of hundred rows. Good.

Something went wrong. No kidding. I bet you can't handle something that large. Let's go to that's it for part three of my get ready with me series. Stay tuned for part four.

See you on the next one. If you enjoyed this video, please hit the like button. Subscribe to my channel if you haven't already. And if you want to know when new videos are available, hit the bell button to be notified as soon as new content is live.