NASCAR Automates 90% of Race-Day Data Tasks With AI

NASCAR and its technology partner Next League have automated roughly 90% of the manual tasks required to feed live race data into the league's official apps, the two organizations said.

The numbers behind that shift are large. Across NASCAR's three-day race weekends, staff previously performed tasks numbering in the hundreds — toggling settings, switching feeds, and checking that everything from car positions to pit-stop service data reached premium subscribers' phones. One missed setting could leave driver audio feeds mute for fans.

"There are hundreds of points of data that's coming off of each of the cars that manifest onto this live leaderboard product," says Tim Clark, executive vice president and chief brand officer at NASCAR.

What did AI actually replace?

Artificial intelligence, deployed through NASCAR's longstanding strategic partnership with the sports tech company Next League, now handles much of that operational load. AI tools, along with code written with their help, keep the apps filled with live telemetry feeds and behind-the-scenes driver audio during races.

"Over time, those things have been built in such a way where manual control of those things, depending on the state of the event at any given time, was imperative," says Next League's cofounder and CEO, David Nugent. "And now, it's just not."

Next League's AI work extends beyond race weekends. The company is expanding its NASCAR collaboration to support digital projects for the racing league's teams and for the NASCAR-owned International Motor Sports Association. AI has also accelerated engineering across NASCAR's web properties and apps.

How much did AI-targeted advertising improve?

The measurable payoff so far is in advertising. Newly personalized and contextualized ads delivered to fans have achieved a click-through rate of about 2% — 10 times the rate of traditional NASCAR house ads, according to NASCAR and Next League.

Future applications could include upgrades to NASCAR's fantasy products and beta features that highlight elements of audio broadcasts and their transcripts. One example Clark offers: automatically surfacing a recorded pit-stop conversation after a car puts on a related burst of speed.

Are humans still in the loop?

Clark is adamant that the point is not simply replacing human labor. People remain in the loop to keep race weekends running, but with less time spent pressing buttons and flipping switches, staff can focus on higher-level tasks.

"I think this year that that time has been freed up, it's allowed our team to look more strategically at what we could be doing better," Clark says.

One experiment is unlikely to be repeated: NASCAR used AI to generate a Spanish-language audio feed from the English radio broadcast for its inaugural Cup Series event in Mexico. The audio quality was fine, and some fans were probably unaware it was machine-generated, Clark says — but NASCAR would likely hire a Spanish-speaking broadcaster for future events.

What comes next?

AI has proven most useful for one specific function: generating quick working prototypes of potential products and features. That lets NASCAR test real concepts rather than make decisions from abstract ideas or static illustrations — an advantage as the league develops new ways to showcase its data trove.

"Just from a technology and a development standpoint, to deliver on all of these things was a full-time job," Clark says. "I think if what we're saying is now that's maybe a part-time job, the other part of it has to be on taking all of this information and all of this nuance around a sport like ours, and figuring out the most user-friendly way to deliver it."

For NASCAR, the automation dividend is not headcount reduction but bandwidth: engineering hours once spent keeping live feeds flowing can now go toward building the next set of fan-facing data products.