AI tools are already reshaping the way we search, shop, schedule, and synthesize data. Whether you actively seek out AI to take your meeting notes, write your policy documents, or answer your customer service questions, AI integrations in the apps, software, and web browsers you do use are growing exponentially. 

In the ski industry, AI is powering website chatbots, analyzing ticketing data, and even surveilling lift operations. For all that AI can already do, though, experts say we’ve just begun to tap its potential.

In conversations with Eternity’s Mike Lannen and SAM—in the persons of publisher Olivia Rowan and marketing director Karolyn Towle—Dave Amirault and Heidi Boisvert talk about the future of AI and what its growth potential might mean for the winter sports industry. 

Amirault is a veteran ski industry marketer and the chief innovation officer for SNOW Partners, where he is exploring the use of AI for data analysis, website optimization, and more. Boisvert—who grew up skiing at McIntyre, N.H., now owned by her cousin Ross Boisvert—is an assistant professor of AI and the arts at the University of Florida and the CEO of think tank and creative agency futurePerfect Lab, which aims to harness the power of pop culture and emerging technology to support nonprofits.

The Train Has Left the Station

Olivia Rowan: How do you see AI revolutionizing the ski industry? Where do you see the most value that we can get out of it?

Dave Amirault: From an operator perspective, I think there’s a lot of value in AI helping to solve things we’re bad at. Fraud’s a big example. I think AI can have a lot of really good impacts there because fraud prevention relies on large datasets, which we’re bad at analyzing. And if AI can help us trim a little bit of the fraud off the front and back— whether it’s on people scamming us at the gate or people scamming us on the credit card side—I think that’s really big. 

Operationally, we run a lot of heavy machinery. We use a lot of power at ski resorts. We use tons of water. And every day, we are amassing more and more data on labor, diesel fuel use, electricity use, water use, all these things. If AI can help us streamline the way we analyze, sift, and sort that data to help us make better-educated decisions, that’s huge, because our accounting and BI (business intelligence) teams or business analysts, they don’t have time to do this. 

It also helps save us money. We all know our operations are wildly capital intensive, labor intensive, and resource intensive to run. And Mother Nature can come in and knock us out with a rainstorm at any moment. But it’s how we get knocked down and recover, and how wise we are in the resourcing, that can help deliver the bottom line for us. 

Then there’s stuff for the guest. Skiing is tough. It’s expensive. You don’t know where to park. Where the hell is ski school check-in? If we can help reduce the friction for new or existing guests, that’s just going to help people fall more in love with the sport and give them a higher propensity to come back and spend more and then bring their friends. 

We compete against all these other things: cruise ships, Yankee Stadium, Topgolf. You name it, that’s what we’re up against. We don’t compete against 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. 

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 example. But skiing is still a physical activity, so the opportunity may be in the customer’s discovery of skiing or booking a vacation. 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 preferences. 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 independently or with minimal human assistance) 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 making. 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 (business-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 protocols 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 of the data that our customers provide for us. We have a lot of sensitive data. These people stay with us. Their children are taught to ski with us. We know their allergies. We know where they live. So, there’s a lot of personal information that floats around our data centers or up in the cloud or with third-party providers that we need to be hyper cognizant of.

AI thrives on large datasets. The larger inference it can use, the larger the dataset it has access to, the better modeling it can create, the better answers you can get. We have a lot of really valuable data that we can be providing. It’s up to us to do the due diligence on the vendors that we’re selecting to figure out: What are their data policies around privacy? What is it around security? Do they have SOC 2 (System and Organizational Controls 2) compliance? 

It seems like there’s a new AI company every 10 seconds. Just be really wary of that thing you get on LinkedIn Sales Navigator from someone pitching you on something, because at some point you’re probably going to be propositioned for a large dataset.

The Toothpaste is Out of the Tube

Karolyn Towle: There are many traditionalists in the industry, so if you had to pitch AI to a traditionalist in the industry who might be skeptical, how would you convince them of its value?

Amirault: I think it’s important not to put AI on a pedestal as an industry. We’ve been through a lot of technological shifts. The first one that I was a part of was the Internet Age. Everyone scrambled to make websites, and the websites were terrible. And then it became connectivity. And connectivity was kind of lagging because ski areas are typically in the middle of nowhere. 

So, we always go through these big changes. On some of them, we are lagging. Some of them happen quickly. This one’s happening quickly and here’s why: AI is already being integrated to the software that we’re using. Medallia, Domo, Office 365, Google—these products, they are jamming AI features into them every day. So, it’s not something you can stop. 

It’s like fighting the ocean at this point. You just have to realize that it’s how you utilize it. I can go into data privacy, data rights, and your responsibility as an operator. But the toothpaste is out of the tube, kids. There’s no stopping this one. 

These conversations are excerpted from SAM and Eternity’s Ski Resort AI Bootcamp, which launched this fall. They have been edited for space and clarity. For more about the Ski Resort AI Bootcamp, visit:
www.saminfo.com/event/ai-bootcamp.