Get Ready With Me: Generative AI Webinar Prep, Part 1 of 5

This week we're going to do a multi-part series on getting ready for a webinar using generative AI tools and a bunch of different data sources and things like that. And I figured we'll do kind of like a get ready with me video series, except that instead of you know putting on like makeup and trying on clothes, we're gonna get data, process it, fry it on, and convert a previous webinar into the webinar that we're gonna be doing this week. So uh, like I said, this is gonna be a multi-part series, and we're gonna talk about using generative AI tools to quickly adapt a presentation and make sure that it's relevant for the audience. So this week, uh Trust Insights is putting on the generative AI for hospitality. Hospitality in this case refers to hotel uh and and hotel like businesses and the kinds of things that they would need to be uh doing to attract and retain customers.

So, broadly speaking, when I adapt a presentation, I take the existing shell of a presentation and I change the parts that are industry specific. So let me go through here and show you very briefly what's in the average presentation. You have a housekeeping, you have the major uh issues facing that industry, some explanation about how generative AI works, some basic examples of prompting. Then we get into the use cases, uh, the major use cases, generation, uh extraction, summarization, rewriting, classification, and question answering. This is the portion of the presentation where I try to have as many industry relevant examples as possible and going through and identifying what those are and demoing them.

After that, you have uh when should you use AI, when should you not, uh some of the warnings about AI, some of the challenges that would be specific to that industry about generative AI, and then of course preparing a career. So that's the general flow of this presentation. It's it's pretty well structured to be able to pick up one portion of it and switch it out with the other. I also do go through and make sure we change out screenshots and things as technologies evolve so that it's current and not showing stuff that's wildly out of date. So this is hospitality.

The current deck we've got here is a real estate one, which obviously is not at all the same thing. So the first thing we need to do is in the presentation, I like to start each one talking about the challenges facing in industry. So we're gonna change this to key challenges facing hospitality, get rid of the previous generated images, all of which are generated with meta AI. And now we need to figure out what are the challenges facing the hospitality industry. There are some that are perpetual, right?

Heads in beds is perpetual. It's what the hospitality industry does. And so companies are always looking for how do we increase the number of heads and beds, how do we increase the profitability of said heads and beds, and how do we reduce our expenses for serving those heads and beds without necessarily compromising quality. However, there are specific issues to this industry that we would want to know. How do we go about doing this?

Well, we're gonna need two things. First, we're going to need sort of a snapshot of the industry itself, and second, we're going to need an ideal customer profile of uh what the major players in the industry, what challenges they are facing. How do we get that? Well, it turns out one of the best sources for understanding an industry is that industry's podcasts. Every industry's got a podcast of some kind now because podcasting is absurdly crazy.

And most podcasts, sensibly and correctly, have stuff on YouTube. So for the hospitality industry, this is a great show called No Vacancy. And uh they are up to like eight episode 892. Uh they're a weekly show, and they publish a lot of content about the hotel and hospitality industry. So what we'd want to do is we'd want to use the closed captions data from maybe this year's episodes and feed that to a language model and ask it to just summarize, aggregate and summarize, synthesize what the major issues are.

There are ways to do this uh individually, but you can also, and you probably should do this programmatically. So, what I've done is I've gone and grabbed the just the closed captions files, not the audio or the video for this year's uh episodes. Let's go ahead and clean this up. Now we need to get this into a format that generative AI is capable of processing. This right here is very messy.

These are VTC files, uh VTG transcript files. VTs are um marked up so you can see inside of one, there's all this extra stuff. Good news is um it's relatively easy to convert that. So let's go ahead and convert that in Python. Okay, so what we've got now is we've taken our files, we've gone through the transcripts and gotten all the that VTT stuff out so that they're normal text files, and then consolidated them down into one single text file that I call main data here.

And we can take a look here and see how see how it looks. It looks pretty okay. You can see there's markup in these files that denotes what episodes are what. Okay, so now after all is said and done, we've taken our huge folder of VTT files. Each one has been cleaned up to remove the VTT formatting, and then each file now has been consolidated into one big text file.

You can see the episode title, the transcript from that episode, and then this file goes on for 434,000 words, uh, which is it's quite a lot. Um, but this is basically the last eight months of this podcast. So our next step is we need to boil down this eight months into something that is a little more digestible. So let's go ahead and bring up Google's uh Gemini language model. Gemini is my preferred model for this use case because we need the very large context window.

Um, Gemini can hold up to two million tokens, which is about 1.5 million words. We saw that uh our transcript file here is 434,000 words. That is about 600,000 tokens. So that means a tool like Chat GPT can't handle this size data, it has to be something like Gemini. Uh, the first thing we want to do is we want to understand uh what's in this file.

So let's give this a prompt that helps us sort of highlight the issues in the hospitality industry. So my prompt here is you're a hotel and hospitality management expert. You know hotels, customer care, and running a hotel business. Your first task is to read through these transcripts of issues facing the hospitality industry. Once you've read through them, outline the issues in outline format in descending order by priority.

An issue has the highest priority if it will substantially increase or decrease revenue for a hotel or hospitality company. So this is 549,000 tokens. We're going to go ahead and let Gemini do its thing. Okay, in less than 90 seconds, less than a minute and a half. We've had Gemini go through eight months of this podcast to get an understanding of the hotel and hospitality industry.

So in descending order of priority, our major issues are financial and operational challenges, a profitability squeeze as labor costs and insurance costs go up, continued resistance in ADR. Uh I don't know what that means. So let's take out what ADR means in the context of hotels. Average daily rate. Oh, so the price of the hotel.

Continued increases in average daily rate may face consumer pushback, leading to revenue stagnation. Yeah, the prices are too high. High interest rates, uh impacting financing, tight lending environment, deferred capital expenditures, uh, pressure to catch up on deferred maintenance and renovations while facing higher costs. Solutions and opportunity, revenue enhancement, cost optimization, strategic partnership, uh, labor challenges, talent acquisition retention, uh, difficulty finding qualified workers, employee turnover, changing employee expectations. Younger generations prioritize work-life balance, flexibility and purpose-driven work, requiring adaptation and cult company culture and benefit offerings.

Solutions, enhanced recruitment strategies, improved engagement and retention, flexible scheduling, benefit packages that cater to employees' needs, uh, give employees agency and autonomy in their roles, shifting consumer trends, uh, experience economy, rise of alternative lodgings, focus on sustainability, technology-driven expectations, guests expect seamless tech-enabled experiences requiring hotels to integrate technology thoughtfully and efficiently, blurring of business and leisure travel, lines between business and leisure becoming increasingly blurred, requiring hotels to cater to blended travel needs. That is true. Um, solutions and opportunities, like legal and regulatory challenges, targeted legislation, over reliance on hospitality for societal issues, brand oversaturation, major hotel companies uh launching new brands, leading to confusion, brands struggle to clearly define their unique value proposition. So, this is a really good helpful snapshot of the entire hospitality industry and the things that people who are in this industry might be facing. Knowing this, we can now start to think about well, what, if anything, would be applications where we could uh add generative AI capabilities for it.

So, for example, um labor costs and property taxes and cost of consumables. Yeah, there's no no hotel is going to be able to use generative AI to address those issues. Uh, average daily resistance, uh daily rate resistance, consumer pushback. You might be able to do on the marketing front, explaining what a consumer gets for their money. So we're gonna uh bookmark that as uh something potential interest rates, can't do anything about that, can't do anything about lending environment or capex, uh, labor challenges, difficulty finding qualified workers, so labor shortage.

Uh, this is a big one where if you were to go into job boards for how companies are trying to hire hotel workers or look at the recruitment strategies, you might find that the way that they're marketing working in a hotel uh could use some improvements. So, we're gonna put a pin in that for uh areas where generative AI could be useful. Uh, let's take a look at shifting consumer trends, experience economy, alternative logic, sustainability. So, a lot of these are travel trend things. Um, and this is a case where generative AI could be enormously helpful by listening to consumers at scale and being able to adapt your marketing and adapt your product uh your product offerings using generative AI.

Uh, legal and regulatory challenges. I mean, there's a ton of things you can do with legal review and stuff. I'm gonna leave that alone because 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.