Muhammad Umar argues that AI built for specific travel tasks beats generic tools. Computer-vision analytics in hotel rooms boost labor efficiency 15-30%, while guest-allocation automation saves staff hours. Narrow focus on one domain makes AI more effective.
But then that also makes me like think about how what at least in my eyes, vertical use cases tend to create like real value if you compare them to horizontally. Right. For example, like guest allocation automation will save you hours. And then you talk about, you know, computer vision and physically like that brings, you know, between like a 15 to 30% improvement in overall like labor efficiency and things like that. So why do these vertical use cases end up creating more value?
First of all, structured data, I mean the more think of like more focus you give to the AI agent, the better performance. So think about vertical use cases in travel is imagine vision AI deploying in every room where you have the live analytics, where you're assessing how much, I mean the overall condition of the room when the guest leaves, all sorts of things. Right? So the more focus you have when it comes to AI, which is more focused means which is more vertical in you would make it. The better the responses would be, the better the results would be, the better the adoptions would be. So this is, I think that's the reason why vertical AI in travel, as I said earlier, I mean I've seen incredible use cases of AI in procurement, in logistics, in ground control, in food services, within the food services, in flight. So in these kind of use cases, the more focus you give to the, the better it will perform.
FDE Epi : 16 | AI Agents, Billion-Dollar Infrastructure & the Future of Intelligence
