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SKI AREA MANAGERS:
other ski areas. We compete against time. All these other things that people spend their time on, those businesses are sharpening their skills, too, with AI. So, we have to be doing the exact same thing. So, I see AI as wildly transformative for all those things. “IF AI CAN HELP US STREAM- LINE THE WAY WE ANALYZE, SIFT, AND SORT THAT DATA TO HELP US MAKE BETTER- EDUCATED DECISIONS, THAT’S HUGE ...” Rowan: What AI applications or uses should the ski industry be aware of? What are you seeing in other industries? Heidi Boisvert: Skiing is becoming very automated now. It is getting more high-tech when running lifts, for exam- ple. But skiing is still a physical activity, so the opportunity may be in the custom- er’s discovery of skiing or booking a vaca- tion. It’s gathering all the data you have and knowing that new tools are there to shape the customer journey before they even get to the actual scene. … Large language models are going to be able to aggregate together for you my likes, my dislikes. It’s going to learn all of my pref- erences. And these things, they’re going to make my pre-experience much more personalized. Then there’s the automation side in terms of the industrial systems. You don’t have robotic systems (intelligent machines that perform tasks inde- pendently or with minimal human assis- tance) or robotic arms running [resort operations], but I can see that there could be a replacement [of human labor]. This ethic—which is, how can we create something that’s more predictable?—is baked into all the technologies we use today. Themed entertainment might be a field to look at in terms of what is being automated, as we have large mechanical robotic systems that are running more things now. I think the middle area is kind of the experience design of skiing. Is there a way to optimize the flow of bodies through a space? Or time the efficiency
of the lifts with automation to create more precision in the system? There may be new ways to track traffic flow using object detection and human detection to create a whole infrastructure based on a logic of traffic flows. You can do block detection. You can track numbers and devices. There’s LiDAR (Light Detection and Ranging) detection. Hit the Brakes? Mike Lannen: I always like to play the devil’s advocate, so what is your biggest fear? What can go wrong with AI in the ski industry? Amirault: We’ve all seen bad AI these days. You get the marketing email that’s like, “Dear sir, madam—” Lannen: “I hope this finds you well.” Amirault: Yeah, and you’re like, “Oh my God, this is just terrible. I can tell that it’s AI. But it wasn’t AI done right.” Or, [another example], we’ve all used other AI products that can hallucinate or give us bad answers. You can’t just run to ChatGPT and ask it for things, folks. It is confidently wrong a lot of times. And from a data perspective, if we’re piping in accounting data and energy usage and all this stuff, we have to audit [AI’s findings] with human beings. We can’t be taking it as Bible at first. Once AI has enough of a track record, we can start to trust it with some decision mak- ing. But don’t just assume that AI is this magical cape you put on and then you’re flying like Superman. You still have to double check it. Lannen: I agree, trust but verify. Boisvert: I guess the question for me is more, what is the social contract you have with your clients? And also, what are your analytics and your business practices around sharing data B2B (busi- ness-to-business) or sharing with some of those larger corporations that are going to be doing this [data] aggregate? So, for me, it’s about creating really strong internal policies and ethics compliance around the policies you have around AI. Right now, it’s like the Wild West. We don’t really have established proto- cols so that we have an understanding as companies how we want our employees to engage with AI tools. Amirault: I like to stress that, as operators, we have to be good stewards
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