Malcolm Gladwell: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Malcolm Gladwell: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Malcolm Gladwell: we're diving back into the world of artificial intelligence, but
Malcolm Gladwell: with a focus on the powerful concept of open its possibilities, implications,
Malcolm Gladwell: and misconceptions. We'll look at openness from a variety of
Malcolm Gladwell: angles and explore how the concept is already reshaping industries,
Malcolm Gladwell: ways of doing business and our very notion of what's possible.
Malcolm Gladwell: I'm particularly excited for today's guest, Brian Ryerson. He's Senior
Malcolm Gladwell: Director of Digital Strategy at the US Tennis Association, helping
Malcolm Gladwell: to oversee one of the most iconic events in the
Malcolm Gladwell: world of sports, the US Open. Brian sat down with
Malcolm Gladwell: Pushkin's own Jacob Goldstein, host of the podcast What's Your Problem.
Malcolm Gladwell: A veteran business journalist, Jacob has reported for The Wall
Malcolm Gladwell: Street Journal, the Miami Herald, and was a longtime host
Malcolm Gladwell: of the NPR program Planet Money. IBM has been the
Malcolm Gladwell: official technology partner of the US Tennis Association for more
Malcolm Gladwell: than thirty years, and the more recent evolution into generative
Malcolm Gladwell: AI has enhanced the world class digital experiences that help
Malcolm Gladwell: more than fifteen million fans from all over the world
Malcolm Gladwell: enjoy the US Open Tennis Championships. In this episode, we
Malcolm Gladwell: will explore how generative AI is being used to generate
Malcolm Gladwell: match insights, spoken commentary for match highlights, and postmatch summaries
Malcolm Gladwell: at scale for fans to enjoy through the US Open
Malcolm Gladwell: app and website. We'll explore how these AI solutions enable
Malcolm Gladwell: the editorial team to cover more of the tournament than
Malcolm Gladwell: ever before, bringing fans even closer to the game they love,
Malcolm Gladwell: and we'll learn more about one of the engines behind
Malcolm Gladwell: this AI powered content creation, a large language model from
Malcolm Gladwell: the IBM Granite family, which is trained and maintained using
Malcolm Gladwell: the wantonex AI and data platform. Okay, let's dive in.
Jacob Goldstein: Brian, welcome to the show.
Brian Ryerson: Thanks for having me. I'm excited to be here.
Jacob Goldstein: Can you say your name and your job.
Brian Ryerson: Yeah, I'm Brian Ryerson. I'm senior director of Digital Strategy
Brian Ryerson: at the USTA.
Jacob Goldstein: Some question, what's the USTA.
Brian Ryerson: The US Tennis Association.
Jacob Goldstein: And tell me about the USTA, Like.
Brian Ryerson: What is it? Yeah? So, the USTA is the governing
Brian Ryerson: body of tennis in the US. Or mission is to
Brian Ryerson: grow the sport of tennis across the US at all levels. Really,
Brian Ryerson: I would say we're more like a health and wellness
Brian Ryerson: company where tennis is the means to health and wellness.
Brian Ryerson: And then the US Open is kind of our tenth
Brian Ryerson: pole event that happens everyear in Flushing Meadows and is
Brian Ryerson: really our chance to showcase the support of tennis at
Brian Ryerson: its highest level to fans all around the world.
Jacob Goldstein: Yeah, I mean the US Open. I assume most people
Jacob Goldstein: know this, but it's Grand Slam. It's one of the
Jacob Goldstein: what four biggest tennis tournaments in the world.
Brian Ryerson: Yes, yeah, every year, we especially the past couple of years,
Brian Ryerson: we've seen immense growth and you know, we are very
Brian Ryerson: hopeful this year and our big goals have over a
Brian Ryerson: million fans on site during the three week window this year,
Brian Ryerson: so it's an amazing event. I always say it's a
Brian Ryerson: food and wine festival where tennis is the main attraction
Brian Ryerson: and it's a really fun, unique atmosphere.
Jacob Goldstein: How did you get into the tennis business.
Brian Ryerson: It's a great question. It's not where I thought i'd
Brian Ryerson: end up for especially being there for fourteen years. So
Brian Ryerson: I was a marketing and technology major in school, and
Brian Ryerson: I also played college lacrosse and sports was always a
Brian Ryerson: big part of my life and always wanted to be
Brian Ryerson: in the sports and entertainment world. I'm here from the
Brian Ryerson: New York area. This is where I grew up. So
Brian Ryerson: I moved back home and had a few friends who
Brian Ryerson: worked there, and I started out more on the number
Brian Ryerson: side of things and really digital analytics. It was really
Brian Ryerson: the start of when Facebook and Twitter is just starting
Brian Ryerson: and digital marketing and all of that. And you know,
Brian Ryerson: I went to my first year so Open not really
Brian Ryerson: knowing what to expect, and again, I think the atmosphere
Brian Ryerson: kind of captivated me and hooked me in. And I've
Brian Ryerson: been there now fourteen years.
Jacob Goldstein: And so your title is Digital Director. What does that mean?
Jacob Goldstein: What's your job?
Brian Ryerson: Yeah, so it's an interesting one because it's tough to
Brian Ryerson: explain to folks who are not in the weeds on
Brian Ryerson: all things US open or even in the sports world.
Brian Ryerson: But really I oversee all of our consumer facing digital property.
Brian Ryerson: So that's the us open dot org, our website built
Brian Ryerson: by IBM, as well as our mobile app. I oversee
Brian Ryerson: our content strategy, our sponsorship integrations. So really anything consumer
Brian Ryerson: facing that happens on the web is under my purview.
Brian Ryerson: Even some of our new platform extensions and gaming and
Brian Ryerson: things like that. Anything that you can physically interact with
Brian Ryerson: is kind of under my purview.
Jacob Goldstein: And so you've been there now for fourteen ISSU years,
Jacob Goldstein: which in the digital world is a long time. How
Jacob Goldstein: has that sort of digital experience of sports changed over
Jacob Goldstein: that time.
Brian Ryerson: Yeah, it's obviously grown digital now, is what we say
Brian Ryerson: and what my team says. It's the number one way
Brian Ryerson: to engage with fans that can't make make it to
Brian Ryerson: the event, as well as those fans who are at
Brian Ryerson: the event, and how to enrich their stay. So it's
Brian Ryerson: really kind of you're tackling multiple fan personas. It's the
Brian Ryerson: international fan who's staying up late to watch in other countries,
Brian Ryerson: to the fan here who's maybe watching on broadcasts, and
Brian Ryerson: we go on in a company and enrich that broadcast of
Brian Ryerson: new stats and insights to the on site fan who
Brian Ryerson: bought a ticket and maybe doesn't know what match is
Brian Ryerson: happening on what court. We do have twenty plus courts
Brian Ryerson: happening at a time with all different matches, so we
Brian Ryerson: really try to help all fans navigate the US Open
Brian Ryerson: the best way possible.
Jacob Goldstein: And so, like, what are some of the sort of
Jacob Goldstein: problems you're trying to solve what are some of the
Jacob Goldstein: hard things about your job?
Brian Ryerson: Yeah, obviously technology changes at a rapid pace, right, So
Brian Ryerson: I think part of it is how do we stay
Brian Ryerson: on the forefront of that, and how do we do
Brian Ryerson: that in the best way and make the best fan
Brian Ryerson: experiences possible and the best user experience as possible. That's
Brian Ryerson: always kind of driving factor number one. Then number two,
Brian Ryerson: it's understanding and listening to our fans and what kind
Brian Ryerson: of content they want. You hear me talk a lot
Brian Ryerson: about storytelling. I feel like there's a lot of storytelling
Brian Ryerson: that happens around the years open that really really want
Brian Ryerson: to bring to fans, and that can be as simple
Brian Ryerson: as storytelling of what's happening today and what you should
Brian Ryerson: be watching too. Maybe it's your favorite players and what's
Brian Ryerson: going on behind the scenes with them, to even introducing
Brian Ryerson: I want to say, the casual fans to who they
Brian Ryerson: should be watching, why they should follow certain players, and
Brian Ryerson: more bringing that player's story to life. Yeah.
Jacob Goldstein: I mean, I feel like almost the whole point of
Jacob Goldstein: sports is to create stories for us to follow, right,
Jacob Goldstein: Like they're engineered to be stories.
Brian Ryerson: It's exactly this.
Jacob Goldstein: Thing is happening in front of you and there are
Jacob Goldstein: two antagonists and the stakes are high, and you don't
Jacob Goldstein: know how it's going to end, Like it's built to
Jacob Goldstein: be a story.
Brian Ryerson: Yeah, and that's the main challenge of the job is
Brian Ryerson: you can plan, plan, plan, but once you get on
Brian Ryerson: two players on court and you don't know what that
Brian Ryerson: outcome is going to be, it's now sitting and waiting
Brian Ryerson: and watching and you become a fan yourself. And then
Brian Ryerson: it's how do you really captivate that story and how
Brian Ryerson: do you narrate it? How do you like translate up
Brian Ryerson: to fans.
Jacob Goldstein: And it's like you kind of have to do it
Jacob Goldstein: in real time, right, Like the whole point of sports
Jacob Goldstein: is you don't know what's going to.
Brian Ryerson: Happen exactly, and that's the excitement. And it's also there's
Brian Ryerson: so many different types of fans. You know, there's the
Brian Ryerson: fans who want a lot of enriched data and their
Brian Ryerson: tennis nerds for lack of better of saying it, and
Brian Ryerson: that they really want to dive deep into the intricacies
Brian Ryerson: of the game, versus the casual fan who maybe just
Brian Ryerson: wants more of this high level storyline of what does
Brian Ryerson: this mean? Why is it important? So it's really trying
Brian Ryerson: to figure out how to deliver that at scale and
Brian Ryerson: really help fans get what they're looking for and the
Brian Ryerson: type of content they're looking for.
Jacob Goldstein: So, are there specific examples of, you know, how fan
Jacob Goldstein: feedback has led to specific features digital features you build.
Jacob Goldstein: Are there, like particularly popular features you've come up with, Like,
Jacob Goldstein: what are some specifics?
Brian Ryerson: Yeah, some low hanging fruit type things that came from
Brian Ryerson: fan feedback. Is simple things sometimes like managing time zones
Brian Ryerson: and when matches start.
Jacob Goldstein: A persistent problem where those of us can work across.
Brian Ryerson: Times exactly, and we do have like a twenty plus
Brian Ryerson: courts happening at a time, so it's a lot to follow,
Brian Ryerson: and how do you translate that to a fan whether
Brian Ryerson: it's to their native language or to their time zone
Brian Ryerson: or things like that. So that's one thing that came
Brian Ryerson: through fan feedback, and another one a three to five
Brian Ryerson: hour match, especially when you're having twenty plus of them
Brian Ryerson: happening at a time, is there's too much for one
Brian Ryerson: person to follow. So how do you start from an
Brian Ryerson: editorial perspective really helping with that storytelling and guiding a
Brian Ryerson: fan to like, all right, whether there's an upset about
Brian Ryerson: to happen, or here's your matches to watch or even
Brian Ryerson: some of the predictions we're starting to put in is
Brian Ryerson: we really want to guide the fan before a match,
Brian Ryerson: here's where you should tune in to even after a match,
Brian Ryerson: of here's what's happened, here's what's important. And we're really
Brian Ryerson: excited with some of the features we built in the
Brian Ryerson: last few years that I would say really helps us
Brian Ryerson: do that at more scale than what we were able
Brian Ryerson: to do with just writers following a match and covering
Brian Ryerson: every single match.
Jacob Goldstein: Uh huh. So I want to talk a little bit
Jacob Goldstein: about the partnership between IBM and the USTA. Tell me
Jacob Goldstein: about the work you do together.
Brian Ryerson: So IBM is our official digital and technology partner and
Brian Ryerson: innovation partner of the US Open. They predate me. It's
Brian Ryerson: a thirty year partnership and it truly as a partnership.
Brian Ryerson: So I view the IBM consulting team as an extension
Brian Ryerson: of my USTA team, So we work with them year round.
Brian Ryerson: They design, develop and deliver the digital properties. They help
Brian Ryerson: us provide the tools to create content to do things
Brian Ryerson: at scale. They help us from stats and information and
Brian Ryerson: really help us push from an innovation standpoint to make
Brian Ryerson: sure that we are staying on that cutting edge of technology.
Brian Ryerson: So I would truly say it's much more than a sponsorship,
Brian Ryerson: where it's truly a partnership to deliver that fan experience.
Jacob Goldstein: And so what are some of the specific things that
Jacob Goldstein: you have done with IBM.
Brian Ryerson: Yeah, so, I mean there's countless ones to talk through. Obviously,
Brian Ryerson: they thirty years ago, they helped us build our first
Brian Ryerson: website and it's kind of grown from there over the
Brian Ryerson: past few years. I would say I think was twenty
Brian Ryerson: eighteen as we started AI Highlights. That was really when
Brian Ryerson: we were able to have all twenty matches going at
Brian Ryerson: a single time. We were able to quickly deliver succinct
Brian Ryerson: highlights to fans to our digital platform so they could
Brian Ryerson: see highlights for every single core.
Jacob Goldstein: Is that video highlights? Is that tech summaries? What does
Jacob Goldstein: that mean?
Brian Ryerson: At the time, it was video highlights, Okay, so it
Brian Ryerson: was really taking that three to five hour match, let's say,
Brian Ryerson: and cut it down to a three minute highlight that
Brian Ryerson: could show up within moments after a match, ending to
Brian Ryerson: our website and our mobile app, so fans could see
Brian Ryerson: that all around the world and really kind of get
Brian Ryerson: that three minute overview what happened in a match?
Jacob Goldstein: And was that AI enabled. Was AI a piece of
Jacob Goldstein: how to do that?
Brian Ryerson: It was it was probably our first foray into AI.
Jacob Goldstein: Back twenty eighteen is relatively early.
Brian Ryerson: Yeah, exactly for tennis exactly. Yeah. It really I want
Brian Ryerson: to say, opened up our ability to one again storyteller,
Brian Ryerson: but attract new fans too. Is video has actually been
Brian Ryerson: our number one growth area since twenty eighteen. I think
Brian Ryerson: of that has to do with the scale of how
Brian Ryerson: we deliver that content.
Jacob Goldstein: Using AI and being able to deliver the sort of
Jacob Goldstein: video highlight reels at scale.
Brian Ryerson: Yeah, and do it quickly. Right. We've always had highlights,
Brian Ryerson: but it was a manual process where you had a
Brian Ryerson: video at or cutting through you know, a three hour match,
Brian Ryerson: selecting the right scene, stitching together. It would have to
Brian Ryerson: get voiced over, et cetera. We really have used AI
Brian Ryerson: to make it, I want to say, much more efficient
Brian Ryerson: and speed up that process and deliver it more quickly
Brian Ryerson: to our fans.
Jacob Goldstein: I mean, it would be a bummer to get scooped
Jacob Goldstein: by whatever NBC News or Yes Pen or whatever. I'm
Jacob Goldstein: sure there are all your partners and you love them
Jacob Goldstein: most likely obviously you want to have the video first, right,
Jacob Goldstein: it's your match.
Brian Ryerson: Yeah, And I think It's also important to us as
Brian Ryerson: being the USTA is ensuring that it's not just you know,
Brian Ryerson: the main marquee players, that every player and all those
Brian Ryerson: storylines and that whether it's you know, the main singles,
Brian Ryerson: draw to our mixed doubles, et cetera. They all need
Brian Ryerson: highlights and they all have their own stories to tell,
Brian Ryerson: and how do we do that at scale? It was
Brian Ryerson: something that before we had that product was not something
Brian Ryerson: you were able to do.
Jacob Goldstein: Great, So let's let's talk in some more detail about
Jacob Goldstein: what you're working on. Let's start with the app. Tell
Jacob Goldstein: me about the us Open app and the Companion website.
Brian Ryerson: Yeah. So, so I'll start with the app, and I
Brian Ryerson: feel like they serve similar needs, but they're a little
Brian Ryerson: different in their own respective manners. Is the app everybody
Brian Ryerson: has a phone in their hands at this point. The
Brian Ryerson: app is kind of their guide to when I say
Brian Ryerson: a million fans on site, we view the app as
Brian Ryerson: we want that to be their on site guide and
Brian Ryerson: Companion a million.
Jacob Goldstein: Let's just pause on a million fans on site, right,
Jacob Goldstein: because like a big professional whatever, an NFL game or
Jacob Goldstein: something that's like one hundred thousand, this is ten x that.
Brian Ryerson: Yeah, and a three week window and a very succinct, tight,
Brian Ryerson: action packed window. There's a lot of action logistics.
Jacob Goldstein: Okay, so keep going.
Brian Ryerson: So the app, you know, whether it's finding the schedules,
Brian Ryerson: the live scores, what's happening on court. That's really the
Brian Ryerson: focus point of the app app and what we're really
Brian Ryerson: focused on this year is how do we build in
Brian Ryerson: some of those match summaries into the app, into our
Brian Ryerson: Slam Tracker experience. So again, before match, that kind of
Brian Ryerson: match preview of here's maybe if you have a ticket,
Brian Ryerson: here's what to expect, here's you know are likely to win,
Brian Ryerson: who we are predicting, so you can kind of get
Brian Ryerson: some information heading in, and then after the match it's
Brian Ryerson: more of what just happened, what it means for the
Brian Ryerson: rest of the draw, who they're playing next, is this
Brian Ryerson: the first time this has happened, et cetera, and really
Brian Ryerson: enriching that experience as well. So the app is one
Brian Ryerson: your guide to what you should be watching, but also
Brian Ryerson: then giving you that insights and context to what's happening
Brian Ryerson: on that court as you're.
Jacob Goldstein: Watching, like the commentator in your pocket exactly. So you
Jacob Goldstein: used a phrase in there as if I already knew
Jacob Goldstein: it and I love the phrase, but I want you
Jacob Goldstein: to talk more about it. That phrase is slam Tracker.
Brian Ryerson: Yes, so slam Tracker is our long standing live scores.
Brian Ryerson: I want to say match Center. It is, okay, where
Brian Ryerson: every single data point, where every single match lives. And
Brian Ryerson: it really it helps showcase what's happening to match. I say,
Brian Ryerson: it's our broadcast companion. So if you're watching live, it's
Brian Ryerson: our in stadium companion. It's also the best thing to
Brian Ryerson: have if you aren't able to watch.
Jacob Goldstein: And so, like, I'm on the app and there's a
Jacob Goldstein: thing called slam Tracker, and it like taps slam Tracker.
Jacob Goldstein: What do I see on my phone when I tap
Jacob Goldstein: slam Tracker? You know, midday when the tournament's happening.
Brian Ryerson: So before match, that's where you get a lot of
Brian Ryerson: pre match content. That's where those live kind of our
Brian Ryerson: predictions are. Likelihood to win lives within that So likelihood
Brian Ryerson: to win essentially pulls in a bunch of data points.
Brian Ryerson: So pass matches, how many times these players have played
Brian Ryerson: each other against each other, Even some punditry and other
Brian Ryerson: written articles that maybe our editorial team put out and
Brian Ryerson: really kind of puts a prediction out there.
Jacob Goldstein: And so it's just a percentage chance.
Brian Ryerson: Yes exactly, but it uses millions of data points to
Brian Ryerson: come up with that. Yes, so it really helps you
Brian Ryerson: kind of understand what you're getting into for that match.
Brian Ryerson: During a live match, it is every single point, so
Brian Ryerson: point by point scoring as well as in depth analysis
Brian Ryerson: in point commentary where also this year have a live
Brian Ryerson: visualization that accompanies that that will really help bring the
Brian Ryerson: match together. And what I mean by that is it
Brian Ryerson: uses our ball tracking technology to really showcase the match
Brian Ryerson: in near real time, so within seconds delay of where
Brian Ryerson: the ball is being hit, where the players are, and
Brian Ryerson: really bring a visualization to life and layered stats and
Brian Ryerson: data on top of it.
Jacob Goldstein: Huh. If that's sort of like when I'm watching a
Jacob Goldstein: match on TV and there's like a close call as
Jacob Goldstein: the ball in or out and they do that thing
Jacob Goldstein: where they kind of show a sort of video game
Jacob Goldstein: version of where the ball landed. Does it look like that?
Brian Ryerson: It's like that before every single shot, So it's not
Brian Ryerson: just those close ones. It's our first foray to bring
Brian Ryerson: that match to life.
Jacob Goldstein: Huh. And so what do I see on that kind
Jacob Goldstein: of view that I don't see from whatever watching the video?
Brian Ryerson: Yeah? So one you'll be able just to see more
Brian Ryerson: of the ball trajectory and where the ball is being hit.
Brian Ryerson: But then you can also start layering things in stats
Brian Ryerson: and insights on top of that, So how many times
Brian Ryerson: has player a hit the ball on a certain baseline,
Brian Ryerson: how fast are they hitting it, maybe their serve percentage
Brian Ryerson: and a certain side of the court, etc. So you
Brian Ryerson: can really start layering in for the ones that really
Brian Ryerson: want to dive deep into the For.
Jacob Goldstein: The nerds, it's for the information rich exactly.
Brian Ryerson: It's the strategy of tennis. It really should be an
Brian Ryerson: interesting way to slice and dice a match.
Jacob Goldstein: Huh.
Malcolm Gladwell: It's remarkable how the USDA is leveraging AI to enhance
Malcolm Gladwell: fan engagement and deliver immersive experiences both on site and online.
Malcolm Gladwell: Brian's emphasis on storytelling really underscores the evolution of sports marketing.
Malcolm Gladwell: The slam Chakra feature particularly caught my attention. It's essentially
Malcolm Gladwell: bringing the excitement of a tennis match to life in
Malcolm Gladwell: your palm, moment by moment. As someone who appreciates the
Malcolm Gladwell: narrative intricacies of sports, I find it compelling how AI
Malcolm Gladwell: helps predict and analyze matches in real time.
Jacob Goldstein: Tell me about the AI commentary feature.
Brian Ryerson: Yeah, I know, I mentioned AI highlights back in twenty eighteen.
Brian Ryerson: It's now progress for us. And again if we go
Brian Ryerson: back to before we had a highlights, to have a
Brian Ryerson: highlight ready for the site was a video editor cutting
Brian Ryerson: the highlight and getting voiced over and then being published aside,
Brian Ryerson: and it took probably an hour plus for that highlight
Brian Ryerson: to really be created. Now with AI commentary, not only
Brian Ryerson: are we creating and cutting the highlights using our AI technology,
Brian Ryerson: but it's now using all the data points that we
Brian Ryerson: have around the match, whether it's our live scoring data,
Brian Ryerson: our ball tra directory data, etc. And it's really creating
Brian Ryerson: a script that helped storytell around that match. That's all
Brian Ryerson: using Watson X technology and then using text to speech
Brian Ryerson: we're able to actually then create the commentary on top
Brian Ryerson: of that, which all happens now within minutes. So our
Brian Ryerson: team's able to now create fully voiced highlights for every
Brian Ryerson: men's and women's singles match to our site within minutes.
Jacob Goldstein: So I know there's a new feature you're working on
Jacob Goldstein: for this year called match reports. What are match reports?
Brian Ryerson: It's our ability to succsickly tell the story of a match,
Brian Ryerson: so everything happens in five hours within that match down
Brian Ryerson: to a couple paragraphs. That really helps a user understand
Brian Ryerson: or a fan understand what just happened. Again, some key
Brian Ryerson: stats what's upcoming really help us with that storytelling. In
Brian Ryerson: the past, when we have twenty two courts happening at
Brian Ryerson: a certain time, we would have to pick and choose
Brian Ryerson: which stories we think or which matches we think are
Brian Ryerson: going to have the best stories, and that's a really
Brian Ryerson: hard thing to predict from an editorial perspective. With our
Brian Ryerson: Match Reports now we'll be able to have full coverage
Brian Ryerson: of every single match during the main draw.
Jacob Goldstein: So, of course I want to talk about jeneritive AI.
Jacob Goldstein: How could we not talk about generative Of course, what
Jacob Goldstein: are you working on with jenerative AI?
Brian Ryerson: So match Reports is the prime example of it. So
Brian Ryerson: Match Reports will be completely using Watson next genera of
Brian Ryerson: AI technology, and really, again to us, it's how can
Brian Ryerson: we do that storytelling at scale? Tennis is such a
Brian Ryerson: data rich sport. All sports have data, but tennis has
Brian Ryerson: a lot of shots and different shot types and ball
Brian Ryerson: trajectory and live scoring data and umpire chair data and
Brian Ryerson: crowd and all that factoring in jenera AI really helps
Brian Ryerson: us take some of that structured and unstructured data really
Brian Ryerson: one organize it in a way, but then help us
Brian Ryerson: quickly tell that story at scale to all of our fans.
Brian Ryerson: And I think we're really just starting to scratch at
Brian Ryerson: some of the capabilities, and we're really excited about where
Brian Ryerson: we're being, but we also see the opportunity of even
Brian Ryerson: how we can grow to new fans and new fans
Brian Ryerson: around the world using jennal of AI in the future.
Jacob Goldstein: So I'm curious, and you alluded to this a moment ago,
Jacob Goldstein: but I'd like to talk a little bit more about
Jacob Goldstein: it because it seems interesting as a technical problem. Right,
Jacob Goldstein: is the nature of turning tennis matches into stories, which
Jacob Goldstein: is fundamentally what we're talking about here in different ways
Jacob Goldstein: in different media, is about taking both structured data, right
Jacob Goldstein: like the stats, you know, points, matches, and also unstructured data,
Jacob Goldstein: right like commentary and articles and the kind of fuzzier
Jacob Goldstein: parts of storytelling. And so I'm curious how AI kind
Jacob Goldstein: of helps you manage both the structured and the unstructured data.
Brian Ryerson: Yeah, so I think the structured data is pretty self experimentatory,
Brian Ryerson: but when you get into the unstructured data and some
Brian Ryerson: of the punditry. That's where you get more of the
Brian Ryerson: opinion pieces into it, like a specific player matchup. This
Brian Ryerson: player always plays well against so and so, or as
Brian Ryerson: they play always played well at night, or they're a
Brian Ryerson: fan favorite, and the crowd, you know, adrenaline and the
Brian Ryerson: crowd being behind you can really motivate you to play
Brian Ryerson: a lot better. So it pulls in all those unstructured
Brian Ryerson: pieces and helps us really put some more rigor around
Brian Ryerson: it and help add and enrich our storytelling with it.
Jacob Goldstein: And so I'm curious when you're starting to use generative AI,
Jacob Goldstein: you know, over the past few years, like what were
Jacob Goldstein: your concerns going into that.
Brian Ryerson: I think our biggest concern is ensuring that one factually
Brian Ryerson: it is because it's only as good as the data
Brian Ryerson: you feed in. And how do you really ensure that
Brian Ryerson: your model's working right and that the output and the
Brian Ryerson: data you're feeding it matches the output and how do
Brian Ryerson: you do that at scale? So we do have a
Brian Ryerson: lot of human intervention. That's where the IBM consulting team,
Brian Ryerson: they're on site with us for those full three weeks
Brian Ryerson: really helping us review everything. And we're constantly learning, especially
Brian Ryerson: early in the tournament and I would say the other
Brian Ryerson: big concern, again it goes around to the data, is
Brian Ryerson: what data do we have available that is trustworthy? So,
Brian Ryerson: you know, we are feel very confident with the data
Brian Ryerson: that comes off of Cork, but when we get into
Brian Ryerson: that unstructured piece, what are the right data sources? How
Brian Ryerson: do we validate those data sources and how do we
Brian Ryerson: ensure that they're accurate Because if the data that has
Brian Ryerson: to go in has to be accurate for the for
Brian Ryerson: the output.
Jacob Goldstein: So how do you do that? That's the concern? How
Jacob Goldstein: how do you address it?
Brian Ryerson: Yeah, so I think there's a number of tools that
Brian Ryerson: we use all within the Watson X umbrella. We do
Brian Ryerson: a lot of training with the IBM team, so we
Brian Ryerson: have to constantly train and retrain that model. I think
Brian Ryerson: the other piece that we're doing is again as we're
Brian Ryerson: creating that content and we have the IBM consulting team
Brian Ryerson: on site helping us with that, is as we see
Brian Ryerson: things and we see outputs, it's refeeding that back into
Brian Ryerson: the model to make it better for the next time.
Brian Ryerson: So it's a constantly learning process that we're undergoing.
Jacob Goldstein: So I want to talk about scale. Yes, you have
Jacob Goldstein: like what twenty two different courts with matches going all
Jacob Goldstein: at the same time, you're trying to you know, approximately
Jacob Goldstein: instantly generate summaries of all these matches in something like
Jacob Goldstein: real time. And I'm curious in particular how the IBM
Jacob Goldstein: models you're using, the IBM Granite models are helping you scale.
Brian Ryerson: Yeah. So I think one of the big learnings we
Brian Ryerson: had with IBM granted models too is that we're able
Brian Ryerson: to run it, you know, against last year's tournaments and
Brian Ryerson: see what the expected outputs could be and really help
Brian Ryerson: train that model heading into the tournament. Because as we
Brian Ryerson: talked about in the beginning, is we can plan play
Brian Ryerson: and play. Once two players get on court, the outcome
Brian Ryerson: is unknown. So how do we really run it through
Brian Ryerson: its paces and really make sure that whatever that outcome
Brian Ryerson: could be and whatever that scenario is, whether it's a
Brian Ryerson: fifth set tie break that happens, or maybe there's a
Brian Ryerson: a fault in the match or something that we're not anticipating,
Brian Ryerson: that we have that accounted for and that the a
Brian Ryerson: won't throw off that output. So we really try to
Brian Ryerson: think through every scenario, which is sometimes difficult, right because
Brian Ryerson: again live sports is the unknown is the unknown that's
Brian Ryerson: what makes it fun. We do spend a lot of
Brian Ryerson: time thinking through potential scenarios and ensuring that we have
Brian Ryerson: the right data sets and the model to predict that.
Jacob Goldstein: Tell me about match reports and the generative AI model
Jacob Goldstein: you're using for that.
Brian Ryerson: Yeah, so match reports will be new for us this year,
Brian Ryerson: so we're in testing right now, so we're really excited
Brian Ryerson: around it. But the model that we'll be able to
Brian Ryerson: use using Watson X will use a bunch of different
Brian Ryerson: parts of the suite of tools A, meaning that again
Brian Ryerson: of taking some of that punditry and the unstructured and
Brian Ryerson: the editorial spend, take our structured data as well. And
Brian Ryerson: really what we're working on right now is figuring out
Brian Ryerson: the right prompts for the AI to really ensure that
Brian Ryerson: it tells the right structured story, meaning what just happened. Right,
Brian Ryerson: So our recap is pretty standard. Here's what the data
Brian Ryerson: is telling us, who won, who lost? How many sets?
Jacob Goldstein: Here's the story the structured data part, that's the easy part.
Brian Ryerson: Yeah, and then really where it gets exciting is then
Brian Ryerson: what does this mean? Meaning what's upcoming? So there's all
Brian Ryerson: these different scenarios when you get into you know, two
Brian Ryerson: hundred and fifty four players and a large straw. This
Brian Ryerson: allows us to distill that down and really tell kind
Brian Ryerson: of what could happen upcoming. The AI helps us do
Brian Ryerson: that at scale.
Jacob Goldstein: So I want to sort of generalize for a moment
Jacob Goldstein: to talk about kind of you know, broader challenges with
Jacob Goldstein: AI and how you've solved them. You know a lot
Jacob Goldstein: of generative AI pilots fail because the data quality isn't
Jacob Goldstein: high enough, because the risk controls aren't there, and so
Jacob Goldstein: I'm curious how you dealt with those problems and are
Jacob Goldstein: dealing with them.
Brian Ryerson: Data quality, again, we feel common with the data that
Brian Ryerson: is supplied from the US open and from the USTA, right,
Brian Ryerson: so we have again that's our structure, scoring data and
Brian Ryerson: all that. I think what we're constantly looking at is
Brian Ryerson: when we get outside of our known sources and out
Brian Ryerson: to third parties is that's where a lot of the
Brian Ryerson: testing and model work happens. So we pull in different
Brian Ryerson: data sources and really try to work through how it
Brian Ryerson: changes that output. Again, some of that comes down to
Brian Ryerson: where it's an open model and the transparency that we
Brian Ryerson: have and the learning that comes behind it. That's where
Brian Ryerson: a lot of that confidence can come from, and it
Brian Ryerson: comes from a lot of testing and feeding it more data.
Brian Ryerson: Your second question was a little bit more around the
Brian Ryerson: output I believe.
Jacob Goldstein: Right, yeah, and risks, Right, so risk I think of
Jacob Goldstein: risk more in terms of output, right, But the obvious
Jacob Goldstein: sphere is like what if it says something wrong? Yeah,
Jacob Goldstein: inflammatory or whatever like that seems scary.
Brian Ryerson: Yeah, it definitely is one of our largest concerns when
Brian Ryerson: we first took this fora I would say a lot
Brian Ryerson: of that comes through our work with IBM and IBM
Brian Ryerson: consulting team and really ensuring that again they're an extension
Brian Ryerson: and the partnership there of our team. So whenever we
Brian Ryerson: are creating let's say it's the Match Report, and we're
Brian Ryerson: going to be creating these extinct articles for every single
Brian Ryerson: men's and women's single match that happens, is all of
Brian Ryerson: those will have manual review and people looking through them
Brian Ryerson: for accuracy to ensure that the model then hallucinat or
Brian Ryerson: make up a fact or fill in the gaps from
Brian Ryerson: things like that. That's the first step. And then also
Brian Ryerson: when our editorial team goes to publish those of the website,
Brian Ryerson: they're going to be checking it as well, So there
Brian Ryerson: are manual interventions throughout that to really check that model,
Brian Ryerson: but we feel that the ability to do it at
Brian Ryerson: scale and with us more to check that is the
Brian Ryerson: efficiency problem that we've been looking to solve.
Jacob Goldstein: So the USTA and IBM have been working together on
Jacob Goldstein: digital innovation for like thirty years from you know, the
Jacob Goldstein: first website, yes for the USTA until now that's the
Jacob Goldstein: past thirty years. If you look ahead, what's the next.
Brian Ryerson: Thirty thirty years is a really long time.
Jacob Goldstein: At A three.
Brian Ryerson: Yeah, I think you know where I get excited, and
Brian Ryerson: I think I alluded to it in the beginning about
Brian Ryerson: how I feel like we're just scratching at the surface,
Brian Ryerson: especially with journat of Ai, and where I see it
Brian Ryerson: going is there's a lot of different fans out there,
Brian Ryerson: and we're also very kindness in the US open that
Brian Ryerson: we're a worldwide event and that there's a lot of
Brian Ryerson: different fans that were not necessary creating content for bespoke
Brian Ryerson: meaning in their native language or maybe it's in that
Brian Ryerson: native players language and things like that.
Brian Ryerson: Is where I get excited is we've seen immense growth
Brian Ryerson: with A Highlights and the ability to now do highlights
Brian Ryerson: at scale is the ability for us to start creating
Brian Ryerson: content in different languages, maybe covering different parts of the match,
Brian Ryerson: So maybe you do have that stats junkie really wants.
Brian Ryerson: Just it's the fastest serve and here's the deep insights
Brian Ryerson: versus the casual fan who's looking for more of the
Brian Ryerson: storytelling around how a player trains and what up to
Brian Ryerson: it was like and what it means for them afterwards
Brian Ryerson: and things like that. A lot of that takes a
Brian Ryerson: lot of time. Now we're able to solve that efficiency
Brian Ryerson: problem and do it in multiple languages, we can really
Brian Ryerson: create I want to say, personalized content to a lot
Brian Ryerson: more fans all around the world, which again helps us
Brian Ryerson: grow the sport of tennis great.
Jacob Goldstein: Uh So I want to finish with a speed round. Okay,
Jacob Goldstein: are you ready?
Brian Ryerson: I am ready?
Jacob Goldstein: Okay, first thing that comes to mind complete this sentence.
Jacob Goldstein: In five years, AI.
Brian Ryerson: Will transform many parts of the business.
Jacob Goldstein: What is the number one thing that people misunderstand about AI?
Brian Ryerson: That it's supplemental, not replacing, meaning that it helps it
Brian Ryerson: with efficiencies, but it doesn't necessarily replace the creativity.
Jacob Goldstein: Right now, what advice would you give yourself ten years
Jacob Goldstein: ago to better prepare you for today?
Brian Ryerson: I think it would have been especially now that we're
Brian Ryerson: able to take so much of that unstructured data and
Brian Ryerson: pass content that we were created to help tell stories
Brian Ryerson: was to I want to say, archive more of that
Brian Ryerson: in a way that we could be using that to
Brian Ryerson: help pull from that now. So you know, we've seen
Brian Ryerson: kind of a change in the guard from some of
Brian Ryerson: our start players to now new and up and comers,
Brian Ryerson: and it would be really fascinating to me if there
Brian Ryerson: was a way to to cross sections some of that
Brian Ryerson: and saying like what trajectories are certain up and coming
Brian Ryerson: players maybe filing from others. So it's more I wish
Brian Ryerson: we kept more of the content we created.
Jacob Goldstein: Back fave the data exactly. Well are you saving it
Jacob Goldstein: all now?
Brian Ryerson: Oh yeah, one hundred percent learned our lesson? Yes, yes.
Jacob Goldstein: So on the business side of AI, what do you
Jacob Goldstein: think is the next big thing?
Brian Ryerson: I alluded to it earlier. I think it's personalization and
Brian Ryerson: getting content that's catered to you at scale, whether you
Brian Ryerson: know that's across the sports sphere or or any type
Brian Ryerson: of written content or or new I feel like the
Brian Ryerson: ability to really get contentated to the type of fan
Brian Ryerson: you are and the insights you have is where we're
Brian Ryerson: all headed.
Jacob Goldstein: And in terms of your non work life, how do
Jacob Goldstein: you use AI? Day to day.
Brian Ryerson: It's funny. I was just having this conversation with a
Brian Ryerson: friend the other day and we were talking about sometimes
Brian Ryerson: when you're starting something new, the hardest thing to do
Brian Ryerson: is you have a blank piece of paper or a thought,
Brian Ryerson: and how do you get started. Sometimes with these generative models,
Brian Ryerson: the easiest thing and the best thing you can do
Brian Ryerson: is it helps you get started. Meaning it may not
Brian Ryerson: be one hundred percent with that first prompt, but it's
Brian Ryerson: that efficiency of whether it's an outline for a new idea,
Brian Ryerson: or it's a marketing brief you have to write, or
Brian Ryerson: sometimes even if it's an email, you have to write
Brian Ryerson: for a personal something and you're not sure how to
Brian Ryerson: word it the right way. It allows you to have
Brian Ryerson: a start and then you can edit from there. So
Brian Ryerson: again going back to my efficiency point, it helps you
Brian Ryerson: become more efficient.
Jacob Goldstein: Solve's the blank page problem.
Brian Ryerson: It does.
Jacob Goldstein: It was great to talk with you. Thank you so
Jacob Goldstein: much for your time.
Brian Ryerson: Yeah, this was fun. Thanks for having me.
Malcolm Gladwell: A huge thanks to Jacob and Brian for the deep
Malcolm Gladwell: dive into the cutting edge innovations transforming the game of tennis.
Malcolm Gladwell: Brian shed light on how the US opens partnership with
Malcolm Gladwell: IBM is harnessing data driven insights to reshape storytelling in sports,
Malcolm Gladwell: from AI generated commentary to match reports. As we look ahead,
Malcolm Gladwell: I'm excited about the possibilities for personalizing content and reaching
Malcolm Gladwell: fans in new ways. The future of AI promises more
Malcolm Gladwell: than just efficiency. It's about enhancing fan experiences worldwide. Smart
Malcolm Gladwell: Talks with IBM is produced by Matt Romano, Joey Fishground,
Malcolm Gladwell: and Jacob Goldstein. We're edited by Lydia Jean Kott. Our
Malcolm Gladwell: engineers are Sarah Bruguiere and Ben Tolliday. Theme song by Gramoscope.
Malcolm Gladwell: Special thanks to the EightBar and IBM teams, as well
Malcolm Gladwell: as the Pushkin marketing team. Smart Talks with IBM is
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Malcolm Gladwell: To find more Pushkin podcasts, listen on the iHeartRadio app,
Malcolm Gladwell: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell.
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Malcolm Gladwell: this podcast don't necessarily represent IBM's positions, strategies or opinions.