In this episode of Smart Talks with IBM, the focus is on how artificial intelligence, particularly generative ai, is transforming the digital experiences surrounding the US Open tennis tournament. The guest, Brian Ryerson, Senior Director of Digital Strategy at the US Tennis Association (USTA), discusses the role of AI in generating insightful match analytics and commentary which enhances fan engagement and accessibility to content. The collaboration with IBM has made it possible to scale the digital storytelling of tennis matches, providing fans with enriched viewing experiences.

Brian Ryerson sheds light on how digital communication has evolved over his 14-year career at the USTA, highlighting IBM’s integral role in crafting digital properties like the US Open app. The use of AI to handle large volumes of data from matches enables real-time storytelling, allowing fans worldwide to get closer to the game. Features like "slam tracker" and AI-driven match reports have been instrumental in tailoring experiences for diverse fan demographics—from enthusiasts seeking in-depth statistics to casual viewers wanting narrative insights.

Main takeaways from the video:

Please remember to turn on the CC button to view the subtitles.

Key Vocabularies and Common Phrases:

1. generative ai [ˈdʒɛnərətɪv aɪ] - (noun) - A type of artificial intelligence that generates text, images, or other media in response to user prompts using machine learning models. - Synonyms: (creative AI, AI-generated content, neural network models)

Of course, I want to talk about generative ai. How could we not talk about generative ai?

2. storytelling [ˈstɔːrɪˌtɛlɪŋ] - (noun) - The activity of sharing stories, narratives, or accounts to engage, inform, or inspire an audience. - Synonyms: (narration, narrative craft, recounting)

You'll hear me talk a lot about storytelling. I feel like there's a lot of storytelling that happens around the US Open that we really want to bring to fans.

3. punditry [ˈpʌndɪtri] - (noun) - The expression of opinions or commentary by authoritative figures in a particular field. - Synonyms: (critique, commentary, analysis)

So past matches, how many times the players have played each other against each other, even some punditry and other written articles that maybe our editorial team put out a, and really kind of puts a prediction out there

4. structured data [ˈstrʌkʧərd ˈdeɪtə] - (noun) - Data that resides in fixed fields within a record or file, typically organized in databases and easily searchable. - Synonyms: (formatted data, organized data, database data)

Factoring in generative ai really helps us take some of that structured and unstructured data, really one, organize it in a way, but then help us quickly tell that story at scale to all of our fans

5. unstructured data [ˌʌnˈstrʌkʧərd ˈdeɪtə] - (noun) - Information that does not have a pre-defined data model, often text-heavy and includes insights like videos, images, and unstructured text. - Synonyms: (raw data, non-formatted data, free-form data)

And so I'm curious how AI kind of helps you manage both the structured and the unstructured data.

6. data points [ˈdeɪtə ˈpɔɪnts] - (noun) - Individual pieces of factual information used as a basis for reasoning, discussion, or calculation. - Synonyms: (metrics, statistics, data sets)

So likelihood to win essentially pulls in a bunch of data points. So past matches, how many times the players have played each other against each other.

7. editorial team [ˌɛdɪˈtɔːriəl tiːm] - (noun) - A group of individuals responsible for creating and curating content, ensuring its quality and consistency. - Synonyms: (content team, writers' board, editorial board)

Well explore how these AI solutions enable the editorial team to cover more of the tournament than ever before, bringing fans even closer to the game they love

8. scalability [ˌskeɪ.ləˈbɪl.ɪ.ti] - (noun) - The capability of a system or model to handle growth, especially related to the increase in capacity or capability. - Synonyms: (expandability, adaptability, growth potential)

And how do you do that at scale? So we do have a lot of human intervention.

9. ball trajectory [bɔːl trəˈʤɛktəri] - (noun) - The path that a moving object follows through space as a function of time. - Synonyms: (flight path, motion path, movement track)

Now, with AI commentary, not only are we creating and cutting the highlights using our AI technology, but it's now using all the data points that we have around the match, whether it's our live scoring data, our ball trajectory data, etcetera.

10. slam tracker [slæm ˈtrækər] - (noun) - A digital tool providing live scores and comprehensive match insights during a tennis tournament. - Synonyms: (match tracker, live scorer, digital match center)

So slam tracker is our longstanding live scores. I want to say match center. It is where every single data point for every single match lives.

An AI advantage for the US Open

Pushkin hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabwell. This season, we're diving back into the world of artificial intelligence, but with a focus on the powerful concept of open, its possibilities, implications, and misconceptions. We'll look at openness from a variety of angles and explore how the concept is already reshaping industries ways of doing business and our very notion of what's possible.

I'm particularly excited for today's guest, Brian Ryerson. He's senior director of digital strategy at the US Tennis association, helping to oversee one of the most iconic events in the world of sports, the US Open. Brian sat down with Pushkin's own Jacob Goldstein, host of the podcast what's your problem? A veteran business journalist, Jacob has reported for the Wall Street Journal, the Miami Herald, and was a longtime host of the NPR program Planet Money.

IBM has been the official technology partner of the US Tennis association for more than 30 years, and the more recent evolution into generative ai has enhanced the world class digital experiences that help more than 15 million fans from all over the world enjoy the US Open tennis Championships. In this episode, we will explore how generative ai is being used to generate match insights, spoken commentary for match highlights, and post match summaries at scale for fans to enjoy through the US Open app and website.

Well explore how these AI solutions enable the editorial team to cover more of the tournament than ever before, bringing fans even closer to the game they love. And we'll learn more about one of the engines behind this AI powered content creation, a large language model from the IBM granite family, which is trained and maintained using the Watson x Aihdeenen and data platform.

Okay, let's dive in. Brian, welcome to the show. Thanks for having me. I'm excited to be here. Can you say your name and your job? Yeah, I'm Brian Ryerson. I'm senior director of digital strategy at the USTA. Dumb question. What's the USTA? The US Tennis association. And tell me about the USTA. Like, what is it? Yeah, so the USTA is the governing body of tennis in the US.

Our mission is to grow the sport of tennis across the US at all levels. Really? I would say we're more like a health and wellness company where tennis is the means to health and wellness. And then the US Open is kind of our tent poll event that happens every year in flushing meadows and is really our chance to showcase the sport of tennis at its highest level to fans all around the world. Yeah, I mean, the US Open. I assume most people know this, but it's grand slam. It's one of the, what, four biggest tennis tournaments in the world?

Yes. Yeah. Every year we, especially the past couple years, we've seen immense growth and, you know, we are very hopeful this year. And our big goal is to have over a million fans on site during the three week window this year. So it's an amazing event. I always say it's a food and wine festival where tennis is the main attraction and it's a really fun, unique atmosphere. How did you get into the tennis business?

It's a great question. It's not where I thought I'd end up, especially being there for 14 years. So I was a marketing and technology major in school, and I also played college lacrosse. And sports was always a big part of my life and always wanted to be in the sports and entertainment world. I'm here from the New York area. This is where I grew up. So I moved back home and had a few friends who worked there. And I started out more on the numbers side of things and really digital analytics.

It was really the start of where Facebook and Twitter was just starting and digital marketing and all of that. And I went to my first year soap in not really knowing what to expect. And again, I think the atmosphere kind of captivated me and hooked me in. And I've been there now 14 years. And so your title is digital director. What does that mean? What's your job? Yeah, so it's an interesting one because it's tough to explain to folks who are not in the weeds on all things us open or even in the sports world.

But really I oversee all of our consumer facing digital properties. So that's the US Open.org, our website built by IBM, as well as our mobile app. I oversee our content strategy, our sponsorship integrations. So really, anything consumer facing that happens on the web is under my purview. Even some of our new platform extensions and gaming and things like that, anything that you can physically interact with is kind of under my purview.

And so you've been there now for 14 ish years, which in the digital world is a long time. How has that sort of digital experience of sports changed over that time? Yeah, it's obviously grown digital now is what we say and what my team says. It's the number one way to engage with fans that can't make it to the event, as well as those fans who are at the event and how do you enrich their stay? So it's really kind of.

You're tackling multiple fan Personas. It's the international fan who's staying up late to watch in other countries. To the fan here who's maybe watching on broadcast, and we want an accompanying and enrich that broadcast with new stats and insights. To the on site fan who bought a ticket and maybe doesn't know what match is happening on what court. We do have 20 plus courts happening at a time with all different matches. So really try to help all fans navigate the US open the best way possible. And so, like, what are some of the sort of problems you're trying to solve? What are some of the hard things about your job?

Yeah, obviously technology changes at a rapid pace. Right. So I think part of it is how do we stay on the forefront of that and how do we do that in the best way and make the best fan experiences possible and the best user experience as possible. That's always kind of driving factor number one. Then number two, it's understanding and listening to our fans and what kind of content they want.

You'll hear me talk a lot about storytelling. I feel like there's a lot of storytelling that happens around the US Open that we really want to bring to fans and that can be as simple as storytelling of what's happening today and what you should be watching to. Maybe it's your favorite players and what, what's going on behind the scenes with them, to even introducing, I want to say, the casual fans to who they should be watching, why they should follow certain players and more. Bringing that player's story to life. Yeah, I mean, I feel like almost the whole point of sports is to, like, create stories for us to follow.

Right. Like they're engineered to be stories. It's like this thing is happening in front of you and there are two antagonists and the stakes are high and you don't know how it's gonna end. Like, it's built to be a story. Yeah. And that's the main challenge of the job, is you can plan plantain, but once you get on two players on court and you don't know what that outcome is going to be, it's now sitting and waiting and watching, and you become a fan yourself.

And then it's how do you really captivate that story and how do you narrate it and how do you, like, translate that to fans? And it's like you kind of have to do it in real time. Right. Like the whole point of sports is you don't know what's going to happen exactly and that's the excitement. And it's also, there's so many different types of fans. You know, there's the fans who want a lot of enriched data and they're tennis nerds for lack of better of saying it, and that they really want to dive deep into the intricacies of the game versus the casual fan who maybe just wants more of this high level storyline of what does this mean?

Why is it important? So it's really trying to figure out how to deliver that at scale and really help fans get what they're looking for and the type of content they're looking for. So are there specific examples of how fan feedback has led to specific features, digital features you build? Are there particularly popular features you've come up with? What are some specifics?

yeah, some low hanging fruit type things that came from fan feedback is simple things. Sometimes managing time zones of when matches start. A persistent problem for those of us who work across time zones. Exactly. We do have, like I mentioned, 20 plus courts happening at a time, so it's a lot to follow. And how do you translate that to a fan, whether it's to their native language or to their time zone or things like that? So that's one thing that came through fan feedback and another one, a three to five hour match, especially when you're having 20 plus of them happening at a time, is there's too much for one person to follow.

So how do you start from an editorial perspective? Really helping with that storytelling and guiding a fan to like. All right, whether there's an upset about to happen or here's a, here's your matches to watch, or even some of the predictions we're starting to put in is we really want to guide the fan before match. Here's where you should tune in to even after a match of here's what's happened, here's what's important. And we're really excited with some of the features we built in the last few years that I would say really helps us do that at more scale than what we were able to do with just writers following a match and covering every single match.

So I want to talk a little bit about the partnership between IBM and the USTA. Just tell me about the work you do together. So IBM is our official digital and technology partner and innovation partner of the US Open. They predate me. It's a 30 year partnership and it truly is a partnership. So I view the IBM consulting team as an extension of my USta team. So we work with them year round. They design develop and deliver the digital properties.

They help us provide the tools to create content, to do things at scale. They help us from stats and information and really help us push from an innovation standpoint to make sure that we are staying on that cutting edge of technology. So I would truly say it's much more than a sponsorship, where it's truly a partnership to deliver that fan experience. And so what are some of the specific things that you have done with IBM?

Yeah, so, I mean, there's countless ones to talk through. Obviously, they, 30 years ago, they helped us build our first website, and it's kind of grown from there over the past few years, I would say. I think it was 2018 as we started AI highlights. So that was really when we were able to have all 20 matches going at a single time. We were able to quickly deliver succinct highlights to fans, to our digital platform so they could see highlights for every single court.

Is that video highlights? Is that text summaries? What does that mean? At the time, it was video highlights. Okay. So it was really taking that three to five hour match, let's say, and cut it down to a three minute highlight that could show up within moments after a match ending to our website and our mobile app, so fans could see that all around the world and really kind of get that three minute overview. What happened in a match? And was that AI enabled? Was AI a piece of how to do that?

It was. It was probably our first foray into AI back then. 2018 is relatively early for tennis. Exactly. Yeah. It really, I want to say, opened up our ability to one again, storytell, but attract new fans, too, is video has actually been our number one growth area since 2018. And I think a lot of that has to do with the scale of how we deliver that content using AI and being able to deliver this sort of video highlight reels at scale.

Yeah. And do it quickly. Right. We've always had highlights, but it was a manual process where you had a video editor cutting through, you know, a three hour match, selecting the right scene, stitching together would have to get voiced over, et cetera. We really have used AI to make it, I want to say, much more efficient and speed up that process and deliver it more quickly to our fans.

I mean, it would be a bummer to get scooped by whatever, NBC News or ESPN or whatever. I'm sure they're all your partners and you love them, but obviously you want to have the video first. Right? It's your match. Yeah. And I think it's also important to us as being the USTA is ensuring that it's not just, you know, the, the main marquee players, that every player and all those storylines and that whether it's, you know, the main singles draw to our mixed doubles, et cetera, they all need highlights and they all have their own stories to tell. And how do we do that at scale? It was something that, before we had that product, was not something we were able to do.

Great. So let's, let's talk in some more detail about what you're working on. Let's start with the app. Tell me about the US Open app and the companion website. Yeah, so I'll start with the app. And I feel like they serve similar needs, but they're a little different in their own respective manners. Is the app. Everybody has a phone in their hands at this point. The app is kind of their guide to, when I say a million fans on site, we view the app as we want that to be their on site guide and companion.

A million, let's just pause on a million fans on site. Right. Because, like a big professional, whatever, an NFL game or something, that's like 100,000. This is ten x that. Yeah. In a three week window, in a very succinct, tight, action packed window. There's a lot of action coming, a lot of logistics. Okay. Yeah. So keep going. So the app, whether it's finding the schedules, the live scores, what's happening on court, that's really the focus point of the app.

And what we're really focused on this year is how do we build in some of those match summaries into the app, into our slam tracker experience. So again, before match, that kind of match preview of here's, maybe if you have a ticket, here's what to expect, you know, are likely to win who we are predicting. So you can kind of get some information heading in. And then after the match, it's more of what just happened, what it means for the rest of the draw, who they're playing next. Is this the first time this has happened, et cetera, and really enriching that experience as well.

So the app is one your guide to what you should be watching, but also then giving you that insights and context of what's happening on that court as you're watching. The commentator in your pocket. Exactly. So you used a phrase in there as if I already knew it, and I love the phrase, but I want you to talk more about it. That phrase is slam tracker. Yes. So slam tracker is our longstanding live scores. I want to say match center. It is where every single data point for every single match lives.

And it really helps showcase what's happening to match. I say it's our broadcast companion, so if you're watching live, it's our in stadium companion. It's also the best thing to have if, if you aren't able to watch. And so, like, I'm on the app and there's a thing called slam tracker, and I like tap slam tracker. What do I see on my phone when I tap slam tracker? You know, midday when that tournament's happening.

So before match, that's where you get a lot of pre match content. That's where those live. Kind of our predictions or likelihood to win lives within that. So likelihood to win essentially pulls in a bunch of data points. So past matches, how many times the players have played each other against each other, even some punditry and other written articles that maybe our editorial team put out a, and really kind of puts a prediction out there. And so it's just a percentage chance. Yes, exactly. But it uses millions of data points that come up with that. Yes. So it really helps you kind of understand what you're getting into for that match.

During a live match, it is every single point. So point by point scoring, as well as in depth analysis and point commentary, or also this year, have a live visualization that accompanies that that will really help bring the match together. And what I mean by that is it uses our ball tracking technology to really showcase the match in near real time. So within seconds delay of where the ball is being hit, where the players are, and really bring a visualization to life and layered stats and data on top of it?

Is that sort of like when I'm watching a match on tv and there's like a close call, is the ball in or out? And they do that thing where they kind of show a sort of video game version of where the ball landed. Does it look like that? It's like that before every single shot. So it's not just those close ones. It's our first foray to bring that match to life. Huh. And so what do I see on that kind of view that I don't see from whatever watching the video?

Yeah. So one, you'll be able just to see more of the ball trajectory and where the ball is being hit, but then you can also start layering things and stats and insights on top of that. So how many times has player a hit the ball on a certain baseline? How fast are they hitting it? Maybe their serve percentage in a certain side of the court, etcetera. So you can really start layering in for the ones that really want to dive deep into the it's for the nerds, it's for the. It's the information rich. Exactly. It's the strategy of tennis. It really should be an interesting way to slice and dice and match.

Huh? It's remarkable how the USDA is leveraging AI to enhance fan engagement and deliver immersive experiences both on site and online. Brian's emphasis on storytelling really underscores the evolution of sports marketing. The slam chakra feature particularly caught my attention. It's essentially bringing the excitement of a tennis match to life in your palm, moment by moment.

As someone who appreciates the narrative intricacies of sports, I find it compelling how AI helps predict and analyze matches in real time. Tell me about the AI commentary feature. Yeah, I know I mentioned AI highlights back in 2018. It's now progressed for us if we go back to before we had AI highlights to have a highlight ready for the site. It was a video editor cutting the highlight, getting voiced over, and then being published the site, and it took probably an hour plus for that highlight to really be created.

Now, with AI commentary, not only are we creating and cutting the highlights using our AI technology, but it's now using all the data points that we have around the match, whether it's our live scoring data, our ball trajectory data, etcetera. And it's really creating a script that helped storytell around that match. That's all. Using Watson x technology and then using text to speech, we're able to actually then create the commentary on top of that, which all happens now within minutes.

So our team's able to now create fully voiced highlights for every men's and women's singles match to our site within minutes. So I know there's a new feature you're working on for this year called match reports. What are match reports? It's our ability to succinctly tell the story of a match. So everything that happens in 5 hours within that match, down to a couple paragraphs, that really helps a user understand or a fan understand what just happened. Again, some key stats, what's upcoming really help us with that storytelling. In the past, when we have 22 courts happening at a certain time, we would have to pick and choose which stories we think or which matches we think are going to have the best stories.

And that's a really hard thing to predict from an editorial perspective. With our match reports now, we'll be able to have full coverage of every single match during the main draw. So, of course, I want to talk about generative ai. How could we not talk about generative ai? Of course. What are you working on with generative ai? So match reports is the prime example of it. So match reports will be completely using Watson X generative ai technology. And really, again, to us, it's, how can we do that? storytelling at scale. Tennis is such a data rich sport.

All sports have data, but tennis has a lot of shots and different shot types and ball trajectory and live scoring data and umpire chair data and crowd and all that. Factoring in generative ai really helps us take some of that structured and unstructured data, really one, organize it in a way, but then help us quickly tell that story at scale to all of our fans. And I think we're really just starting to scratch at some of the capabilities. We're really excited about where we're being, but we also see the opportunity of even how we can grow to new fans and new fans around the world using generative ai in the future.

So I'm curious, and you alluded to this a moment ago, but I'd like to talk a little bit more about it because it seems interesting as a technical problem, is the nature of turning tennis matches into stories, which is fundamentally what we're talking about here. Ways in different media is about taking both structured data, right, like the stats, you know, points, stats, matches, and also unstructured data, right, like commentary and articles and the kind of fuzzier parts of storytelling. And so I'm curious how AI kind of helps you manage both the structured and the unstructured data.

Yeah. So that, I think the structured data is pretty self explanatory. But when you get into the unstructured data and some of the punditry, that's where you get more of the opinion piece into it. Like a specific player matchup. This player always plays well against so and so, or as they always play well at night, or they're a fan favorite. And the crowd, you know, adrenaline and the crowd being behind you can really motivate you to play a lot better. So it pulls in all those unstructured pieces and helps us really put some more rigor around it and help add and enrich our storytelling with it.

And so I'm curious, when you're starting to use generative ai over the past few years, like, what were your concerns going into that? I think our biggest concern is ensuring that one, factually it is correct, because it's only as good as the data you feed in. And how do you really ensure that your model is working right and that the output and the data you're feeding it matches the output. And how do you do that at scale? So we do have a lot of human intervention. That's where the IBM consulting team, they're on site with us for those full three weeks, really helping us review everything.

And we're constantly learning, especially early in the tournament. And I would say the other big concern, again, it goes around to the data is what data do we have available that is trustworthy? So, you know, we are feel very confident with the data that comes off of court. But when we get into that unstructured piece, what are the right data sources? How do we validate those data sources and how do we ensure that they're accurate? Because the data that has to go in has to be accurate for the output.

So how do you do that? That's the concern. How do you address it? Yeah, so I think there's a number of tools that we use, all within the Watson X umbrella. We do a lot of training with the IBM team, so we have to constantly train and retrain that model. I think the other piece that we're doing is, again, as we're creating that content and we have the IBM consulting team on site helping us with that, is as we see things and we see outputs, it's refeeding that back into the model to make it better for the next time. So it's a constantly learning process that we're undergoing.

So I want to talk about scale. Yes. You have, like, what, 22 different courts with matches going all at the same time. You're trying to approximately, instantly generate summaries of all these matches in something like real time. And I'm curious in particular, how the IBM models. You're using, the IBM granite models are helping you scale.

Yeah. So I think one of the big learnings we had with the. With IBM granite models, too, is that we're able to run it, you know, against last year's tournaments and see what the. What the expected outputs could be and really help train that model heading into the tournament. Because as we talked about in the beginning, is we can plan, plan, but once two players get on court, the outcome is unknown.

So how do we really run it through its paces and really make sure that whatever that outcome could be and whatever that scenario is, whether it's a yde, a fifth set tie break that happens, or maybe there's a, you know, a faulty end of the match or something that we're not anticipating that we have that accounted for and that the a won't throw off that output. So we really try to think through every scenario, which is sometimes difficult. Right. Because again, live sports is the unknown, is the unknown. That's what makes it fun.

We do spend a lot of time thinking through potential scenarios and ensuring that we have the right data sets and the model to, to predict that. Tell me about match reports and the generative ai model you're using for that. Yeah. So match reports will be new for us this year, so we're in testing right now. So we're really excited around it.

But the model that we'll be able to use using Watson X will use a bunch of different parts of the suite of tools, meaning that, again, taking some of that punditry and the unstructured data and the editorial spin, it will take our structured data as well. And really what we're working on right now is figuring out the right prompts for the AI to really ensure that it tells the right structured story, meaning what just happened. Right. So a recap is pretty standard.

Here's what the data is telling us. Who won, who lost, how many sets. Here's the story. Here's the structured data part. That's the easy part. Yeah. And then really where it gets exciting is then what does this mean? Meaning what's upcoming? So there's all these different scenarios when you get into 254 players and a large draw, this allows us to distill that down and really tell kind of what could happen upcoming. The AI helps us do that at scale.

So I want to sort of generalize for a moment to talk about kind of broader challenges with AI and how you've solved them. A lot of generative ai pilots fail because the data quality isn't high enough, because the risk controls aren't there. And so I'm curious how you dealt with those problems and are dealing with them. Data quality, again, we feel calm with the data that is supplied from the US Open and from the USDA. Right. So we have, again, that's our structured scoring data and all that.

I think what we're constantly looking at is when we get outside of our known sources and out to third parties, is that's where a lot of the testing and model work happens. So we pull in different data sources and really try to work through how it changes that output. Again, some of that comes down to where it's an open model and the transparency that we have and the learning that comes behind it. That's where a lot of that confidence can come from, and it comes from a lot of testing and feeding it more data.

Your second question was a little bit more around the output, I believe, right? Yeah. And risks. Right. So risks, I think of risks more in terms of output. Right. The obvious fear is like what if it says something wrong or inflammatory or whatever? Like that seems scary. Yeah, it definitely is. And it was definitely one of our largest concerns when we first took this foray.

I would say a lot of that comes through our work with IBM and IBM consulting team and really ensuring that, again, they're an extension and the partnership there of our team. So whenever we are creating, let's say it's the match report and we're going to be creating these succinct articles for every single men's and women's single match. That happens is all of those will have manual review and people looking through them for accuracy to ensure that the model didn't hallucinate or make up a fact or fill in the gaps and things like that. That's the first step. And then also when our editorial team goes to publish those to the website, they're going to be checking it as well.

So there are manual interventions throughout that to really check that model. But we feel that the ability to do it at scale and with us more to check that is the efficiency problem that we've been looking to solve. So the USTA and IBM have been working together on digital innovation for like 30 years, from the first website for the USTA until now. So that's the past 30 years. If you look ahead, what's the next 30? 30 years is a really long time. About three, I think, where I get excited, and I alluded to it in the beginning about how I feel like we're just scratching at the surface, especially with generative ai.

And where I see it going is there's a lot of different fans out there and we're also very cognizant, the US Open, that we're a worldwide event and that there's a lot of different fans that we're not necessarily creating content for bespoke meaning in their native language, or maybe it's in that native players language and things like that is where I get excited is we've seen immense growth with AI highlights and ability to now do highlights at scale is the ability for us to start creating content in different languages, maybe covering different parts of the match.

So maybe you do have that stats junkie who really wants just it's the fastest serve and here's the deep insights versus the casual fan who's looking for more of the storytelling around how a player trains and what leading up to it was like and what it means for them afterwards and things like that. A lot of that takes a lot of time. Now we're able to solve that efficiency problem and do it in multiple languages. We can really create, I want to say, personalized content to a lot more fans all around the world, which, again, helps us grow the sport of tennis.

Great. So I want to finish with a speed round. Okay, are you ready? I am ready. Okay. First thing that comes to mind, complete this sentence. In five years, AI will transform many parts of the business. What is the number one thing that people misunderstand about? Aih, that it's supplemental, not replacing, meaning that it helps with efficiencies, but it doesn't necessarily replace the creativity. Right now, what advice would you give yourself ten years ago to better prepare you for today?

I think it would have been, especially now that we're able to take so much of that unstructured data and pass content that we were created to help tell stories was to honestly archive more of that in a way that we could be using that to help pull from that now. So, you know, we've seen kind of a change in the guard from some of our star players to now new and up and comers. And it would be really fascinating to me if there was a way to cross section some of that and saying, like, what trajectories are certain up and coming players may be following from others. So it's more. I wish we kept more of the content we created back. Save the data.

That's what you're telling yourself. Exactly. Saving it all now. Oh, yeah, 100%. We learned our lesson. Yes. Yes. So, on the business side of AI, what do you think is the next big thing? I alluded to it earlier. I think it's personalization and getting content that's catered to you at scale, whether that's across the sports sphere or any type of written content or news content.

I feel like the ability to really get complicated to the type of fan you are and the insights you have is where we're all headed. And in terms of your non work life, how do you use AI day to day? It's funny, I was just having this conversation with a friend the other day, and we were talking about that sometimes when you're starting something new, the hardest thing to do is you have a blank piece of paper or a thought. And how do you get started?

Sometimes with these generative models, the easiest thing and the best thing you can do is it helps you get started, meaning it may not be 100% with that first prompt, but it's that efficiency of whether it's an outline for a new idea or it's a marketing brief you have to write. Or sometimes, even if it's an email, you have to write for personal something and you're not sure how to word it the right way. It allows you to have a start and then you can edit from there.

So again, going back to my efficiency point, it helps you become more efficient. It solves the blank page problem. It does. Brian, it was great to talk with you. Thank you so much for your time. Yeah, this was fun. Thanks for having me.

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