Integrating artificial intelligence (AI) in the hospitality industry is an increasingly pressing matter, and this trend underscores the conversations and panels at HITEC 2025. AI is especially prevalent in revenue management, as automation continues to be at the forefront of hotel operations. Revenue Analytics recently launched the N2Pricing Commercial Suite, which expanded its revenue management system and updated how hoteliers extract insights from data. LODGING recently spoke with Tess McGoldrick, senior vice president of travel and hospitality, Revenue Analytics, about the suite and AI implementation while at HITEC 2025:
AI is one of the main themes of HITEC 2025. Is Revenue Analytics’ N2Pricing Commercial Suite an AI-powered solution?
It is. We’ve always had AI in our products, but more so from a machine learning perspective for predictive analytics. But with the buzz around Gen AI, a big part of what we wanted to do with the N2Pricing Commercial Suite is to be able to free up revenue managers’ time to do high-value activities. We’re trying to put a better tool in their hands to be able to say, “Hey, interact with this piece of technology to tell the story better, collaborate with your peers better, and give owners what they’re looking for.”
What was the process of developing and launching the N2Pricing Commercial Suite?
When developing the N2Pricing Commercial Suite, we thought about the common questions that a revenue manager gets asked. When we think about that revenue manager, they’re the person running around on property getting asked all different questions from all different people. We have all that data to answer those questions. We put a layer on top of it to make it easier for them to answer the questions that they get asked, so they have an easier starting point.
What are some of the pitfalls of using AI in revenue management?
The biggest pitfall of AI is that people think they can just use it and don’t have to use their own brain, too. The way we’ve designed this particular feature is to essentially give people a draft. It’s an editable canvas, so if the AI provides, for example, the top five important insights, we want people to still use their brains and make sure that the AI is telling the right story and have that partnership between the human and the machine as opposed to just wiping your hands of it and saying, “Oh, it’s great, the AI spit out, let’s use it.”
Is reliance on AI an obstacle you’re facing so far?
Not so far. The revenue management community is so resilient. One of the things that—as a little bit of a data nerd myself—coming into the revenue management space and meeting all these smart people, people ask me why I like to work at Revenue Analytics. I like working here because I’m working with really smart people, and the great thing is, those are my customers, too. They’re smart people who are, if anything, questioning what they’re seeing. So, they already have that critical thinking muscle built, so I’m not too worried about it.
What are the next steps following the launch of the suite? What are your goals moving forward?
Our approach is to follow a customer-driven map. We’ve got folks that are gonna be using this that are going to give us feedback. We have ideas about what’s next, but I do think that hearing the feedback and another iteration or two of the platform is going to be what’s next. What’s on my mind is other data sources we could bring in so that the data that’s underlying can get better answers. We also thought we were going to build more reports for it to sit on top of, and maybe we will. But as long as all of the data’s under there, I think that this AI generator on top of it is going to almost be more than enough. Who wants another static report when you already have an editable canvas? We’re going to see what people say, what kind of feedback they want. Do they want more screens? Do they just want more data? We’ll go from there.
As AI continues to evolve, how do you see it shaping revenue management?
When the whole AI buzz started, I was one of the people pounding the table. I said, “We’ve been doing it for years. Machine learning is AI.” Everybody is a bit more comfortable now talking about Gen AI and what it can do from a content perspective, or making recording easier. Internally at Revenue Analytics, our teams use a lot of that already, and I think that being able to bring that into a product is going to be the future, not just for us, but for the industry.
