4 Leadership Lessons from the NFL’s Chief Data Officer

Oh, and there’s more than $18 billion in annual revenue at stake if one or more of these stakeholders are unsatisfied.

So perhaps it is no surprise that an organization this big and complex has been using artificial intelligence to aid its decision-making for a long time—long before generative AI became a buzzword.

But how exactly are its leaders thinking about AI? And what can other leaders and organizations learn from the NFL’s experience?

Joel Shapiro is a clinical associate professor of managerial economics and decision sciences at Kellogg, where he leads the Analytical Consulting Lab. Over the years, his students have worked closely with the NFL on various consulting projects, which is how he came to know Paul Ballew, the NFL’s Chief Data Officer.

Shapiro and Ballew recently sat down to discuss how the NFL is using data and AI today—and what its plans are for the future. The entirety of their conversation is available to stream above (a transcript is also provided below).

Here are four takeaways from their conversation that leaders in any industry might find helpful.

1. You’ve got to balance quick wins with long-term commitment.

For organizations that are new to investing in data and AI—or are interested in taking their investments much further—the question becomes how to bring others along.

“You know you have to have it,” says Ballew. “But then the question is: What do I do? What do I invest? How much do I spend? How do I justify it? What’s the ROI?”

His advice for anyone in this situation is to strike a balance between putting “points on the board early on” and making the kind of sustained commitment that will really be transformative.

One tip? Design those early wins such that you have optionality. “The optionality allows you to support the business use cases of today, but if you get it right, it affords you the opportunity to leverage it in ways you can’t even envision.”

2. Your organization is not a monolith. Your processes should reflect this.

Before the NFL or any of its 32 clubs can use data to make decisions, the data has to a) exist and b) be shared in the correct way with the correct party, who in turn must be correctly trained to use it.

This is where Ballew spends a lot of his efforts. “Over half my organization runs the data-engineering activities, because you’ve got to get the ingredients to bake the souffle,” he says.

For instance, Ballew has a dedicated data-acquisition team, whose only focus is to acquire data and build new datasets. Without the right data, “advanced technology is useless.”

But as you think through your processes around acquiring, managing, and using data, it’s important to remember that your organization is likely not homogeneous. Different parts of the organization will need different things from your data-science team at different times. Perhaps some parts are already quite AI savvy, for instance, while others will require more support. This is the position in which Ballew finds himself. “I think oftentimes people associate any sports league as this monolithic sort of entity. And it’s not,” says Ballew. “It’s 33 entities: 32 clubs and ourselves. And our job at the end of the day is to help them.”

Whatever those relationships with other parts of your business look like today, expect them to constantly shift and flex. For instance, sometimes his data team is serving clubs, other times it is working with them as partners, and still other times it is setting rules for the clubs to follow. “There are many hats that are worn all the time.”

3. AI is a great input into human decision-making.

On its own, AI doesn’t make decisions. It’s a tool that allows the NFL (or any other organization) to make more-informed decisions. And like all tools, AI can be used cynically, or it can be used to promote the organization’s larger goals.

This means that, yes, as some critics have complained, data and AI could be used to create a dull, perfectly optimized game—the Moneyball-ization of a beloved sport. But Ballew points out that they can just as easily be used to set the stage for more-exciting games—and the NFL is in the business of entertainment, so guess which one is in its best interest?

“We continue to enhance [the game] and modify it, and it continues to result in games that are close and competitive,” says Ballew, pointing to “teams like the Texans going from worst to first” and his “hometown Lions making it to an NFC Championship game for the first time in thirty years.”

On a similar note, data- and AI-enabled injury prediction could be used cynically to, say, trade away a player who might be at high risk of injury. But Ballew says that the league is currently using these tools to improve player safety. These include projects to reduce concussions (and detect them earlier), improve nutrition, and test equipment. Ballew also pointed to recent changes to the game’s kickoff rules that were designed to improve player safety.

“You don’t take the human out of the decision-making, but you bring the science to make the decision-making more accurate, more precise, and help the human beings make better decisions,” says Ballew.

4. Marketing isn’t about your customers anymore. It’s about a single customer.

Like many other organizations, the NFL has historically taken a somewhat blunt approach to marketing. “Buy a ticket; come to a game. We have a game. Here’s our schedule,” says Ballew.

In other words, here’s what we’ve got: take it or leave it. (Though we hope you’ll take it.)

But generative AI is enabling personalization on a scale that was previously unimaginable. For the NFL, this might mean connecting with an individual customer based on them being a Bears fan who travels to other midwestern stadiums once a year to catch an away game. Or it could mean providing helpful information to an individual fantasy football player.

“Now, it’s the opportunity to talk to you about what’s really relevant to you when you want to be spoken to about it and through whatever channel: we’re channel agnostic,” says Ballew, who estimates it will take the league three-to-five years to achieve that degree of personalization.

As much as a technical shift, then, this is a mindset shift. Because it’s not about “customers” anymore. It’s about individuals. “We’re in the entertainment business. And ultimately, we want to help individuals fully take advantage of the experience how they want to take advantage of the experience, not how operationally we think they should take advantage of the experience,” he says. “You can howl at the windmill, but you should embrace it.”

Interview Transcript

Joel SHAPIRO: Alright, I am here with Paul Ballew, Chief Data and Analytics Officer of the National Football League, and Paul, I am very much looking forward to talking about how the NFL is using data and analytics and AI to change the game, and even change the business, of the NFL. And I’d love to kick off today just by having you talk a little bit about, how has the game has changed due to data and analytics? And even AI?

Paul BALLEW: Well, you see it sometimes on Sunday, easily, with coaches going forward on fourth down more. And that’s probably the most visible example that people think of where they’ll see the advertisements from AWS, and how Next Gen Stats is transforming the game in terms of how we understand it, how we see it, and how we generate excitement.

But the applications of data and analytics across the League are much broader than that. We centralized the function about three years ago, and we impacted the game across the board: officiating, analytics, and player health and safety beyond what you see on TV. All the things we’re doing to improve the game on that side, whether it’s surface or equipment, or what we’re doing in more advanced technologies, such as computer visioning. We are the first sports league to bring together all of our fan data and personalization at scale with all of our clubs, so we spend a lot of time making sure we’re engaging with our fans in the most meaningful way possible.

We optimize our viewership in terms of what we do from a scheduling standpoint. The games, you see, and what slots they end up in, are very complex things for us to tackle, so the list goes on and on. And it’s exciting for us, because we focus on both the game, the integrity of the game, the quality of the game, the safety of the game. And then the other side: we’re a commercial enterprise, so we focus on the commercial side of the business as well to maximize the engagement with fans and ultimately, the revenue for the League and for clubs.

SHAPIRO: Yeah, let’s talk a little bit about what you started with, you know, going for it on fourth down more frequently, you know. I hear people bemoan the use of analytics frequently, like in other sports, for instance. Like you heard once upon a time that in baseball nobody steals bases anymore. And it was exciting. But the data told us that that was no longer a good move. Or in basketball, too many three-pointers being shot all the time, cause that’s the, you know, the best, you know, strategy for winning the optimal number of, you know, games. What’s your take on how that has affected the game on the field? Has it been good? Has it been bad? Has it been neither?

BALLEW: I would say it’s been good in general. When you look at the ultimate goal of the game, you want it to be exciting; you want it to be competitive; you want it to be compelling. And one of the benefits we have in our game, probably more so than other games, is just that we continue to enhance it and modify it, and it continues to result in games that are close and competitive. And teams like the Texans last year, going from worst to first. My hometown Lions making it to an NFC championship game for the first time in thirty years. And analytics is there to support the art of the game as well. And what I’ve always said with organizations that are successful is, how do you meld the two together? You don’t take the human out of the decision-making, but you bring the science to make the decision-making more accurate, more precise, and help the human beings make better decisions. And yes, you get sometimes the riverboat gamblers out there that really push the boundaries of it. But for the most part, going for it on fourth down and being successful on fourth down creates more scoring opportunities, more points. And that’s the excitement in the game.

SHAPIRO: Yeah, interesting. I wonder how, you mentioned something around keeping players healthy, keeping them safe? I imagine this very much also ties into game management. But you know, one of the areas that I’m very interested in is how advanced analytics and how AI can help us predict player injury. So, I have some ongoing research that you know of that looks at the intersection of player injury and team success in the NFL, but also the ways in which data and AI can help us mitigate injury. And you know, I find some things that are totally not at all surprising, things like injuries actually matter relative to performance. But how much AI can help us is sort of super interesting, just because something is perfectly predictable. Not that injury actually is perfectly predictable, but if it were, that doesn’t mean it’s perfectly preventable, for instance. But I’d love to hear your thoughts on how AI has shaped, and has the ability to shape, keeping players healthy and on the field.

BALLEW: So a number of different ways for us. It’s when you think about player health and safety. It’s mission-critical for the league, for the reasons you’ve talked about, but also because you care about the individuals involved. And so, we have a comprehensive journey at the lead, working with the Players Association and working with the individual clubs, that goes all the way from very advanced technology. Think of the work we’ve been doing with computer visioning, which allows us to understand a number of facets that go into the probability of an injury or a severity of an injury or understanding the biomechanics of an individual in different situations into exploring and trying to understand the impact of surfaces. What we can do on equipment. So, think about guardian caps and all the work we’ve done on that. Guardian cap going into practice, especially in training camp and going into the regular season, has proven to have a material impact on reducing concussions. Then you combine all of that with what you’re doing in terms of game-time evaluations. The more elaborate science going into potentially identifying a concussion or a head injury earlier and forcing that player off the field and into a protocol environment. It’s all those things. It’s the training. It’s the nutrition. It’s all the parts that go into it. And advanced technology and data plays an incredibly important role in it, because you’re trying to understand what you just pointed out. And that is, what are the causal factors, and can I affect those causal factors against it? Can I do something on footwear to lower extremity changes? So those elements matter. I will also point out rule changes. So, think about what we’ve just done on kickoffs. A big part of kickoffs is to not take it out of the game, but to deal with the speed and space and historic kickoffs which resulted in injuries, whether they’re lower extremities or concussions. So it’s a really integrated model that uses advanced technology. We spent a lot of time enabling the data side of this, because advanced technology is useless unless you have the ingredients to bake the soufleé, namely, the data that goes along with it. And so we’re looking at it from a multifaceted perspective.

SHAPIRO: So I’m super interested by one thing that you said, which is, you know, if you can identify why somebody might get injured, then you can, you know, take precautions and keep them from getting injured, which is a very humane and certainly very reasonable use of something like injury prediction.

What happens, though, if a team chooses not to do that? Is it okay? I mean, let’s talk about, like, even the ethics of injury prediction, which I find kind of fascinating. Is it okay for a team to know that somebody’s likely to get injured, and they don’t have to necessarily keep them from getting injured. They could use that information asymmetry with other teams and just trade them away. Is that okay? Am I barking up a weird tree here? Is it something….

BALLEW: It’s probably a little bit of a weird tree. It’s interesting being on the inside with the NFL. All thirty-two clubs care deeply about this. And they care deeply about doing what’s right. And yes, the League puts forward mandates and other things. Think of guardian caps, for instance, as an example of that. But the clubs are passionate about this, because at the end of the day, injuries are not only a human issue, but they’re bad for the game. You do not want players getting injured if you can help it, because to your point, it affects the outcome of the team and overall, not to mention the human costs associated with this.

So, I don’t see…. I know, in other areas we always get into these ethical trade-offs again. I come from automotive, and the whole safety car issue of: I could make a car that’s perfectly safe, but it’ll go two miles an hour, and you’ll go nowhere. Versus a car that allows you to get from point A to point B, but you still have 30,000 fatalities a year on the road. How do you balance that? In the game of football, there’s always trade-offs and what you can do in terms of equipment or rule changes that could affect the quality of the game. But there is an intense, intense commitment to improve the safety of the game, because it’s just the right thing to do, and all thirty-two teams are very passionate about it. And when you think about it, an injury, especially an injury in a critical position, really tips the season, in many cases. You just can’t do that. And then there are other costs associated with it, so I don’t see us running into ethical dilemmas. What we have to continue to do is to try to figure out what we can actually do to affect, with a higher probability, successful outcomes. Yeah, which is the experimentation route. It’s why all the work we’re doing, for instance, on the surface right now, and collecting better data and understanding it, is all that matters. Because while concussions are high on our list and at the very top of our list, we also are spending a lot of time on lower-extremity injuries, because that’s a big deal for players and player health and safety, whether it’s knee, or Achilles, or ankles, or so on. That can be very material to an individual, and so we spend a lot of time on it.

SHAPIRO: Yeah, yeah. What’s the impact of all of that on, like, player contracts and arbitration and personnel decisions? Is that interacting with this in some way? I assume it sort of has to, but I’m not sure where exactly it would be.

BALLEW: Yeah, that’s good. The relationship with the Players Association certainly puts this very, very high. It’s way up at the top of the list as it should be, so I don’t see it impacting that as much. What happens in terms of player negotiations and contracts really reflects the game itself, the health of the game, and what’s going on with individual positions and the importance of individual positions as the game changes. That’s more of the factors that you see playing out, Joel.

SHAPIRO: Yeah, yeah, interesting. At some point in our conversations, you had differentiated between using data and using analytics and then AI. And I’d love to hear a little bit about where you sort of are defining AI within the NFL and what kinds of things you’re doing—what kinds of things you’re building towards. Maybe there’s some generative AI. But we know that AI is much more than just gen AI at this point. But I’d love to hear sort of broadly, what are you working on? And what is the relevance for the NFL?

BALLEW: It’s interesting. As you and I’ve talked about, AI is sloppy right now in terms of the language out there, and it’s fascinating, because in my field, looking at advanced technology, which we would consider to be artificial intelligence, is not something new. Again, I come from automotive. In automotive, we were using advanced AI applications across the board, whether it was autonomous vehicles or what we were doing in terms of manufacturing and sequencing and anomaly detection and pattern recognition. We were doing it with NASCAR to identify anomalies and vehicles going 200 miles an hour ten years ago. So for us at the League, we embrace very aggressively the ability to leverage technology.

And when we think about artificial intelligence, it’s broad-based. Most of our analytic solutions are ML-based. They’re not using regression techniques. They’re using machine learning techniques and reinforce learning how that affects what we do in football and what we affect in marketing, and our personalized messaging, and the like. We use it in player health and safety, as I mentioned, and a technique such as computer visioning. But then, of course, most recently, like the rest of the world, we’re interested in large language models, and what we would do in a very shorthanded way, described as gen. AI. Does that have applications for us? Yes, sometimes it’s chatbots on steroids. But it has applications for us in terms of productivity at the League, and what we’re doing, and all the things you talk about, whether that’s content creation and other opportunities, or content organization and distribution.

We generate a lot of content, and not surprisingly, how you can package that up most efficiently and target it is high on our list. So it’s broad-based, like most organizations. We’re continuing to push the envelope against all of that, to do it well and do it at scale. You gotta get the data environment right and the bulk of my organization, over half my organization, runs the data-engineering activities because you’ve got to get the ingredients to bake the soufflé.

So, we spent a lot of time on that. I know the world wants to quickly shift to talking about all the applications generally associated with gen AI, but for us, we look at the applications as being much broader than just gen AI.

SHAPIRO: And what do you do when you, you know, you’ve got the ingredients? You bake the soufflé, and you’ve got something that you can share. I’d love to hear a little bit about how the NFL works with the teams and what kind of knowledge sharing goes on. Are you supporting them? Are they your client? Are you their client? What’s the relationship there? And how do you share data results? And all the good stuff that happens there.

BALLEW: It’s a fascinating question, because it is a federated model where we’re here to support the thirty-two clubs. We’re a trade association that supports them. On the data and analytics side, it goes across the spectrum. We build capabilities that they can leverage, and we train them and give them access to it. We help them with foundational things, such as data management and data stewardship; [we] allow them to take advantage of the tools that we’ve created at the League.

In other cases, we share best practices with them, because some clubs are further along than others. Some cases we’re helping them by distributing data to them directly. So it’s pretty broad-based. And it’s interesting because I think oftentimes people associate any sports league as this monolithic sort of entity. And it’s not. It’s thirty-three entities, thirty-two clubs, and ourselves. And our job at the end of the day is to help them.

So, to some degree, we think of them as customers or clients of ours and other cases. We think of them as partners. In other cases, we think of them as individuals that have to do what we’re asking them to do as part of League initiatives. So, there are suppliers. So, there’s different hats that are worn all the time. I’m very proud to say that the relationship with the thirty-two clubs is deep and getting deeper all the time, because ultimately, for any of us to do what we’re trying to do, it comes down to three things. Can you scale it? Can you make it repeatable? And can you govern it appropriately? And governance isn’t to say doing it wrong. It’s doing it right. And the scalability and repeatability side really requires us to do it efficiently. And, therefore, having the collaboration with the clubs allows us to advance and do it efficiently, because you can just imagine, if you do it thirty-three times, the inefficiency in that model versus doing it at scale.

I know it’s taking a little time to get a line with clubs on that, but we’ve been able to get over that goal line, and it’s been really rewarding for us to invest the time with them and to do it in a collaborative way.

SHAPIRO: Yeah, interesting. One of the really cool things that I’ve had the chance to do is work with students who have done some projects with the NFL through the analytical consulting lab that I run at Kellogg. I’ve had some student projects with the NFL where we’ve talked about the fan experience, fan perceptions and behaviors, and so forth. And I would love to hear how you are using data, you alluded to this earlier on, how you’re using data analytics and maybe even AI around the fan experience. Because this has changed a lot recently. And I suspect it’s gonna change pretty fast, and I’d love to hear about that.

BALLEW: It’s our biggest initiative. We call it the one-to-one program. And when you think about it, at its core is all the old marketing idioms of right customer, right time, right message, right channel. But in the world we now live in, you have a number of things going on. First of all, individuals expect personalization. Secondly, you could do it at scale in a very targeted way with lots of capabilities to do it in context in a meaningful way. And then the third piece to all this is, you can do it with increasing degrees of technological sophistication associated with it.
So for us, we had created this program about two and a half years ago, and it involves all thirty-two clubs. It is personalization at scale. It allows the clubs to have the right communication in an orchestrated way with the League. It allows us to support our direct to consumer businesses, which are growing and important in the future. By the way, any organization that’s been disintermediated from their end customer is gonna have a challenge in the future. So, your direct to consumer side matters a lot. It helps us with our League marketing activities. All of our League marketing activities now go through a very extensive, very complex marketing-technology stack.

It allows us with what we’re doing on the fan servicing side. It also allows us to collaborate with our partners to better understand fans and justify what they’re doing and really reaching out to fans. It’s tens of millions of fans being interacted with every single day in a very precise way. We’re doing it globally as well as in the US. And it is truly a labor of love, because at the end of the day, what we’re trying to do is we’re trying to see you, know you, and engage with you in a meaningful way in context. And that takes a lot of heavy lifting, because again in a federated model, seeing and knowing requires all thirty-three entities to be collaborating together.

And then the engagement side is where the science comes in. The ability to target more precisely, to engage in a meaningful way, to collect responses to that targeted activity, or to that treatment, to build upon that in a reinforced way, and to rinse and repeat every single day.
And it’s lots of fun. My whole career has been largely based upon understanding individual behavior. And to do this at scale is something that thirty years ago, I could only dream of. Now we can do it at scale, and we can do it with virtually no latency, you know, with a high degree of precision, and the ability to adjust and continuously reinforce the learnings with regards to how people are responding to the treatment—which, from a marketing science standpoint, is what it’s all about.

SHAPIRO: Yeah, what? So when you talk about customization for the fan, what, like, give some examples of what we’re talking about, like, what are we gonna see, you know, that’s different than it has been in the past, or what have we seen already?

BALLEW: Yeah. So if you look at things historically, what we’ve done is, there’s been a lot of carpet-bombing marketing from ourselves in the clubs. You just go out and say, hey, you know, we’re doing whatever. Buy a ticket! Come to a game. We have a game. Here’s our schedule.
Now we’re having the right conversation with the individual. To talk to them about things that are relevant to them; [it] might not be ticketing. It might be something else, might be tuning, could be shop, could be they go to other stadiums, cause they, if they’re in the Midwest, for instance, they could be a Bears fan, and they go to Lambeau once a year, or they go to Minnesota once a year, or they go to Detroit once a year because they’re in the NFC north, and they’re a visiting fan. It’s the ability to see and know them and to make sure that what we’re talking to them about is what they’re going to value. Provide them the relevant information with regards to what’s going on with the team and other things that they care about. If they’re gonna play fantasy football, giving them better content, better information to do so. And that’s what this journey is all about. We’re in the entertainment business. And ultimately we want to help individuals fully take advantage of the experience. How they want to take advantage of the experience. Not how operationally we think they should take advantage of the experience.

SHAPIRO: I saw the coolest example of customized communication. I read an article the other day at the Detroit airport. It’s a message board that truly customizes what it shows to an individual based on them, opting in, based on some facial recognition and tracking. And two people standing, looking at the same board, can see totally different messages customized for individuals. It’s amazing. My son told me about it, and my first thought was, now that’s impossible. And I started to think. And then he showed me the article. That’s amazing. So like, are you pushing the message, like, I’m just imagining boards in the stadium that do the same thing for all, you know, 70,000 people. But where are you pushing communication? Are you doing it through an app? Are you trying to drive people to this one place? Are you just, wherever people want to be, that’s where you want to be? How do you think about that?

BALLEW: Yeah, wherever they wanna be. That’s ultimately where the journey goes. The big change in the science of marketing was, if you go back when I started a long time ago, we went from no understanding to coarse understanding. We created claritas segments and personas and all these other things that meant, okay, I’m gonna still saturate you, but we’re gonna do it in a way that the saturation is gonna have some customization to it.

Now it’s the opportunity to talk to you about what’s really relevant to you, when you want to be spoken to about it, and through whatever channel. We’re channel agnostic; we joke about this all the time. We don’t care. We honestly don’t care about the channel going forward. What we care about is, are we communicating to you in a way that adds value to you and will drive the outcome that we’re looking to drive? Whether that’s to open the information, whether that’s to buy something, whether that’s to go to a game. That’s what this is all about, and the next three to five years are going to take us to the next level of that degree of personalization.

And we’re seeing it in other industries. And we’re seeing it in terms of not just the digital natives, the “Netflixs” of the world, that obviously were pioneering a lot of this ten years ago. We’re now at the cusp of being able to do it across the board. And for us, that’s again the biggest investment in the biggest program that we’ve driven. It’s also essential to the future because if the future continues to be more of this direct consumer model, which, by the way, it has underpinnings there as personalization, it’s allowing you to buy and sell, buy and churn. It’s not so much selling, but buy and turn off what you want to buy and turn off, which obviously is a stress point in the business model for those that are running those streaming services. But that’s a reflection of the future. And we learned, I learned, long ago, you can howl at the windmill, but you should embrace it. You should embrace the wind power that goes along with it, and the wind power that goes along with it is, we can see you, with your permission, we can know you, and we can act and engage in a meaningful way that adds value to you. That’s the winning hand.

SHAPIRO: So, I’m curious sort of as you talk about being very fan-centric. You’re growing like crazy. They’re international aspirations, and even to call them aspirations is a little funny because you are international right now, and I know that you’re, at least I think that you’re looking to get even bigger internationally. Does all of this, I mean this has to help. Right? Customization has to be good for growth, and you know, acquisition of new customers, new fans, and so forth. Are there things that you are specifically using around data or AI that help that international growth?

BALLEW: We have the exact same approach internationally as we do in the U.S. The one-to-one program is international. It’s focused and understanding individuals outside the U.S., just like we understand them in the U.S. And at its center is, once again, to provide value and to make ourselves as relevant as possible. It’s a bigger challenge internationally, because we’re not the big dog. We’re relevant internationally, but we’re in a starting point versus, in the U.S., we are the big dog. We’re the biggest of the dogs. We’re the biggest gorilla in the rainforest by a wide margin. We don’t take that for granted, but it is a good starting point to have. Internationally, we’re not there versus the Premier League or the Bundesliga or others, in countries, much less other sports.

So the importance of being able to see and know and act upon that is even greater. And so we’re spending a lot of time and a lot of energy making sure that we can see and know fans outside the U.S.—with their permission, because every permission is even more important outside the U.S. given privacy regulations and the like, and that’s the journey we’re on. It’s exciting. It’s crazy. It’s it’s ups and downs and lefts and rights. But ultimately, for us, we believe that direct understanding of an individual and the communication channel is essential for us to grow our position outside the U.S.

SHAPIRO: How does that impact like TV contracts and broadcast rights? I mean, digital is huge. People are gonna continue and increase their consumption on digital. Does that make TV less valuable? It’s hard for me to imagine that it’s becoming less valuable, given the magnitude of people who watch their games on Sundays, and you know, so what’s the impact there?

BALLEW: Yeah, the beauty of the strategy from the League, and I would give the Commissioner and others credit for this, is to understand that it’s going to be a mixture of things. You have what we call traditional linear TV, which is free TV that’s somewhat free depending if it’s cable or not, but you have that, and that remains critically important. Then you have content being consumed behind a paywall. Whether that’s Sunday ticket or internationally, game-pass international being with our partner to zone or fill in the blank. And for us, ultimately we are making sure we have optionality in that strategy. And that’s why the understanding of the direct-to-consumer model and understanding our fans is so important because it underpins our ability to have optionality.

Now, lots of work is underway to support the traditional channels as well. We spend a lot of time on viewership optimization, because we want to make sure that our games are properly slotted to get those eyeballs to maximum effect. We want 20 million people on Sunday-night football and NBC. That’s a critically important part of our overall business model and a critically important part of building our brand. If you think about the success of the NFL, what underpinned the NFL’s success was the fact that people could watch the games. Once that exploded, that changed the game. Go back to 1960 versus 1990, and just see how much the game has changed because of the exposure. And also because it’s a great game to watch, and it’s exciting, and people enjoy it, and it’s compelling theater, and all those things going along with it. So that’s our strategy again, underpinning that overall strategy is everything that we’re doing in data, advanced analytics, and being prepared for whatever the future is. Because if anybody wants to call the ball on this and say ten years from now how accurate you’re going to be in your forecast…. I always joke, because if you go back to the early phases of ecommerce, how many people got it right in 1995? How many people that were incredibly successful, insert Steve Jobs and Bill Gates, got it right? They were all wrong, and they weren’t wrong because they’re incompetent; they were wrong because predicting where this goes is just really, really hard. In 1995, nobody thought of a smartphone. Ten years later, we had smartphones. Now, there are, whatever, 6 billion smartphones around the globe, or some crazy number along those lines.

SHAPIRO: Yeah, when you talk about, like, engaging fans and how this is gonna evolve, I mean, one of the things that it seems like it’s a strategy of the NFL is to, I mean, new-customer acquisition, new-fan acquisition. It’s not just global, but you know, you do things around family-friendly events like, you know, the draft is a big deal, for instance, and you see a lot of sort of messaging around bringing in new people. One of the challenges of bringing in new unknown folks is that you don’t have data about them, or at least not internal data. Right? You got a fan database. You could do analysis, and you can figure out ways of optimizing communication. How do you think about bringing in non-fans with a data-driven approach?
BALLEW: So, we spend a lot of time in data development, which includes individuals we don’t see and know today and making sure that we are respectful and we’re transparent as we go down that journey. But events like the draft afford us an opportunity to do that. Working with our network partners affords us the opportunity to do that. Other, what we call tent-pull, events do that as well. The League is maniacally focused on the next generation of fans, and therefore against that backdrop, my team has to be maniacally focused on making sure that we can see and know them as well. And you can if you do it respectfully, and you do it in a transparent way, and you’re thoughtful about what you’re doing.
And you have a data-acquisition orientation. And I have a data-acquisition group whose only job every single day is to acquire new data and do data sets. That’s their only job. That’s what they do every morning when they get up. That’s their focus. Because ultimately for us, we get into this journey of what you and I’ve been around for a long time. The digital journey creates an incredible opportunity for us to, for the first time, capture what’s going on. And if you do it systematically, it opens up all sorts of gates for you when I know people like to say, whatever, these flippant things that 95 percent of all the data the world’s ever created has happened in the last five years. It’s actually a ridiculous statement, because there’s no way to measure it, and there’s no way to understand it. A better way of understanding, that is, in the last five years our ability to capture data has been transformed. It doesn’t mean that in the era of Archimedes and Euclid and others. They weren’t generating data. They just had no ability other than to try to sketch it out, to write it, and lay it down in the library of Alexandria, kept it all. Now, we’re able to capture instantaneously in many cases, and for all of its complexities as well. That’s really the big change, and you just have to systematically commit to that.

SHAPIRO: Yeah, well, don’t get me started on defining what data is even. Cause I find that people get very funny about what they think data is and is not. I wanna just talk a little bit about data and AI leadership more broadly for a minute. So not just your time at the NFL, but also some of your other experiences as well. One of the things that I think a lot of places are struggling with right now is, how do they know what the right investments are in data and AI? And so you just talked about, data acquisition is huge for you because it’s gonna lay the foundation for everything that you’re gonna do moving forward. But how do you make decisions about where you invest, whether it be, you know, sort of traditional analytics, or something more cutting edge like AI? How do you do that? And also sort of the follow up to that is, do you ever get pushback on that? And what’s that dynamic?

BALLEW: Yeah, it’s been a struggle for decades of how do you strike the right balance? You know you have to have it, but then the question is, what do I do? What do I invest? How much do I spend? How do I justify it? What’s the ROI? And my discussion points for anybody doing this is to realize that you’re just going to have to strike the balance between: you’ve got to put points on the board early on, but you also have to convince the organization that there’s a lot of uncertainty associated with this. And if you get the fundamentals right, you’re going to be pleased. Your return on the investment is going to have a very healthy IRR.

You get the fundamentals right. And what I mean by that is, build the data environment correctly, be sensitive to the business, understand their current-use cases, but then also help them understand use cases they never thought about. So it’s a journey. It takes an evangelist evangelizing to make sure everybody understands, because it tends to be an amorphous concept with leaders. Okay, unless they’re a digital native when they’re born with data and analytics. But in legacy organizations, it’s an amorphous concept. I know I need to do something. I understand the importance of data and analytics. I understand the importance of advanced technology. This whole AI thing is going to impact my business. And then if you walk in and say, “and that’s a 300 million dollars price tag investment,” it becomes this shock in awe, like, well, what am I doing? Those dollars, by the way, are more automotive in nature, not NFL in nature, because in automotive you don’t get serious, and so you’re talking about hundreds of millions of dollars and potentially billions.

SHAPIRO: Ok, fair enough.

BALLEW: You have to have that mindset of saying, “look, let’s go down this journey.” And understand, in the journey, there are early wins that you can tackle, and you can see the benefits. In the same regard, build it in a way where you have optionality. And the optionality allows you to support the business use cases of today. But if you get it right, it affords you the opportunity to leverage it in ways you can’t even envision.
Now it’s getting a little easier, because the investment has been dropping so precipitously in terms of what’s required to really build the core foundational elements. You go back, 15 years ago, it was 20 times more expensive to deal with just building the data ecosystem versus today.

Now, you can do these bits and pieces of Lego blocks and do it faster and cheaper, and so on. So that helps, but it still requires a vision. You’ve got to evangelize. You have to be deeply connected with the business. You have to put points on the board early on, because if you don’t, it feels like this three-to-five year thing where you’re gonna pull back the curtain and one day go, “Tada, look at what I’ve done.” You also don’t treat it as a science project; if you’re serious about data analytics, you should be committed to it affecting all facets of your organization, instead of, I’m going to do this one-off project because I want to transform my supply chain, or I want to do whatever. Embrace it as a transformative journey, and then ultimately hold the organization accountable for hitting those deliverables. And if you do that, what’s your IRR in hundreds of percent: 400 percent, 800 percent? Who knows? It’s something along those lines when it’s done well, but you have to do it well to get to that point.
SHAPIRO: Yeah, once you’re talking about hundreds of percent, I feel like it almost doesn’t matter how many hundreds.

BALLEW: It doesn’t.

SHAPIRO: Either way, I …

BALLEW: So yeah, actually, we’re very blessed with the lead, because between the commissioner and the CFO, they got it. And when we even kick around, kind of, the break-even point. It just gets nonsensical. Because if you do it right, your break-even point is just, it’s, why have the discussion? But again, we hold ourselves accountable here, where we’re constantly reporting out on the impacted deliverables, where we’re at and timing-wise.
SHAPIRO: I have to imagine, though, that you are in a leag

ue, and all the teams where there’s some digital natives and some not. And I’m always curious, like, what do you find is, like, the hardest part about being a very data-savvy person working with non-data-savvy senior leaders and decision-makers you have to work with? I once had the good fortune of running into Paul Podesta of moneyball fame at a conference, where we were both talking, and I asked him this question. He said it’s really hard to get people to think probabilistically. If I have a model that says that something’s 80 percent likely to happen, all they hear is, “Oh, it’s gonna happen.” And then, if it doesn’t happen, they think that I was wrong or the model was wrong. And that’s just not the way things work. I’m curious what your experience has been.

BALLEW: It’s a brilliant observation of his part. Yes, that’s part of it. I would also just say the change-management side of it is hard. People don’t object to change. They object to being changed, is that old quote, and there’s a lot to be said. And when you’re in industries where people have grown up in the industry, automotive, just about everybody grew up in the industry. You were born and raised in automotive. You have gasoline in your veins, and you’re getting up in the morning, and here’s somebody coming from my side of the shop, coming in and saying, “well, maybe we should think about doing it differently. Maybe we can reconfigure the plant differently. Maybe we can change the just-in-time inventory sequencing,” and so on. And it really does require bringing them along in the journey, because that experience shouldn’t be thrown out the window. Because there’s a lot to be said for that experience.

But on the other side you’re bringing in a different perspective and really challenging the experience to say we can do it differently, or we should do this, and so on. And that to me has always been the interesting part of this, because it’s the human behavioral side of it. You’re bringing science into an area that you’re asking people to think differently, to operate differently, in some cases, challenge what they believe.
And when you ask somebody to challenge their belief system, you know, a wise man can hold two conflicting thoughts at the same time. Well, that may be true. But how many people actually possess that trait of being able to balance that, I think. Fortunately, I spent all that time in automotive where that culture was probably the hardest, because literally you grew up in the industry.

You went to GM. University, GMI Kettering up in Flint. You got your degree. You were an intern. At 22 years old, you had your first job, and over the span of 40 years you, just, you were there, and you knew the business, and you knew it backwards and forwards. And here’s some kid coming in with their PhD from MIT telling you that your approach to automotive safety has a technical flaw in it, and your crumple zone is wrong, or other things, and you just have to work through it. And I just remember my early experiences. They were quite interesting. But you eventually navigate your way through it, and you strike the right balance, and that’s really what it’s all about.

SHAPIRO: Yeah, figuring out how to balance communicating analytic results in a way that doesn’t come across as telling much more senior people what they’re doing wrong is always a challenge. I found that to be the case in lots of different contexts, you know, a lot of people who are listening to this, I think, are thinking about. They might potentially be data leaders, even if they themselves are not data experts. Right? We see people leading data teams who have deep backgrounds in data and data science and machine learning. And then some who don’t have as much training in their business leaders who are appointed to lead data teams. I wonder if you can just talk for a second about what you think are maybe the keys, like, what rises to the top when it comes to being a great leader of a great data team. What strikes you is really important?

BALLEW: I think, most importantly for us in our area, you have to have very good problem-formulation skills. There’s no doubt that the quantitative skills mattered to some degree in our profession. But I always come back to that belief system. You have to be able to possess those critical-thinking skills, and those critical-thinking skills allow you to hypothesize correctly, allow you to interpret results, allow you to engage with the business.

And I really think it’s a lost art to some degree that we have gotten to the point where our critical-thinking skills aren’t as strong as they used to be. I push my team every single day around what we call the second question, the third question, probing and trying to understand unintended consequences, and really focusing. I think right along with that good problem, formulation skills also require you to understand processes and operational design and those sorts of things going along with it, because you’re implementing things in an applied sense, in an organism that’s living.
And so understanding the processes and the connective tissue of that organization matters a lot. And so yes, you have to have the technical skills. You have to have the people skills and the communication skills. But I really think that what we’re seeing in our field is good problem-formulation skills. Understanding the importance of rolling up your sleeves and process design. And in fact, we’re increasingly hiring process scientists. Interestingly enough. In our field, those skills matter a lot in what we do. And they’re gonna matter a lot going forward because it’s about application. And then once it’s about application, it’s about accountability. And if you don’t do those things well, Joel, you’re gonna struggle.

SHAPIRO: Yeah, for sure. I have one last question for you specifically around AI. But if you want to take it with data more broadly, what excites you about what’s happening next with data and AI at the NFL. And anything that makes you nervous?

BALLEW: What excites me is very similar to where we were with the commercial application of the internet 25 years ago. And that is, we’re democratizing the ability to leverage the capabilities, and the democratization of that best embodied by a smartphone opens up doors and pathways that we could never have envisioned. I had a boss at the Federal Reserve 35 years ago who kept saying this thing called the “World Wide Web” is going to change everything. And he was very late in his career, and all his hot-shot young kids were like, “Whatever. Yeah, right. You know, the boss is just, you know, he doesn’t know what he’s….” And he was right. It just took 15 years when we made that comment to see the explosion. And what we’re now seeing with AI and advanced technology and gen in particular is we’re democratizing the capabilities. And that opens up things that will transform activities in our world to make the world a better place. You think about the applications in healthcare, which is central to the journey we’re all on. That to me excites me more than probably at any point in my career, maybe absent the late 90s when we had the other explosion in this digital revolution.

What concerns me is you’ve got to govern it appropriately. We talk a lot about hallucinations right now or other things. I think it’s much broader than that. It’s always this great balancing act between how you democratize on one end, how you govern on the other end. You don’t want to impede progress, but you realize that the data has to be properly governed; there’s privacy issues that go along with it. There’s business ramifications if you’re not properly doing it to brands and content destruction and all these things that go along with it.

So I just try to take the lessons—lessons of history, you know. Look at the ecommerce explosion: [it] had a lot of good to it, had some challenges to it. Organizations that figured out how to strike that balance are better for it, and we’re all better for it as individuals, because they just ask you every single day: Could you live without this smartphone device in terms of what it means to your life, convenience and information, and what you can do in terms of acquiring things at higher quality and lower price, and so on? How do you interact with all the loved ones in your life?

What do you do every single day now in terms of texting and group texting and so on and sharing of information and photos, and all those things that make life more rewarding. I have grandkids, and I get daily updates on my grandkids now. Not because they’re calling me, but I’m getting real-time photos and videos and so on. Same thing’s gonna happen in the next generation of where artificial intelligence is gonna have to occur.
We’re gonna have to figure out how to strike the governance balance of it. We’re gonna have to make sure that it’s being used appropriately to the best of our ability. That’s what this digital revolution’s been all about. We’re seven decades into it. It’s been a wild ride.

SHAPIRO: For sure. Paul, thanks so much for talking with me today. I really appreciate it. Really, really interesting and I love your take on this, so thanks very much.

BALLEW: Thanks, Joel. Have a nice day.

SHAPIRO: Thanks, you too.