When Ruth Buscombe started her career in Formula One, the sport was already generating more data than most teams knew what to do with. Today, as a former race strategist for Ferrari Sauber and current contributor to F1 TV, she's at the forefront of understanding how to turn overwhelming volumes of information into split-second decisions that can make or break a race.
In a recent episode of the Data and AI Mastery podcast, Ruth sat down with host Raoul-Gabriel Urma to discuss F1's evolution from stopwatches in muddy fields to one of the most data-intensive sports on the planet. The insights she shared offer valuable lessons for any business leader navigating today's data-rich landscape.
Modern Formula One cars generate approximately 1.1 million data points every single second from around 300 sensors. This data streams live from racetracks across the globe, sometimes from circuits 10,000 miles away from team headquarters in Europe, directly to AI systems that must generate actionable insights in less than a second.
But it's not just about collecting data. It's about knowing which data matters. Ruth explained that F1's challenge mirrors what many businesses face: moving from "not enough data" to "too much data" and then figuring out what's actually useful. The sport's solution has been driven by necessity. When the performance gap between first and last place averages just 1.3%, and pole position can be decided by 0.012 seconds (roughly 76 centimetres over a five-kilometre track), there's no room for inefficiency.
Ruth outlined how F1 teams structure their data approach around three core pillars.
First, improving their own performance by identifying KPIs that make the car marginally faster or help it use tyres more efficiently. Second, competitor analysis, which Ruth described as "copying somebody else's homework". Teams constantly analyse what rivals are doing to understand strategic sensitivities and avoid tactical traps.
The third pillar is race strategy itself, which involves predicting whether a race will be a one-stop or two-stop affair, forecasting weather patterns, and making real-time decisions about pit stops and tyre choices. These decisions happen under immense pressure, with millions watching and championships on the line.
One of the most compelling parts of the conversation addressed a question many businesses we see grapple with: if your data and models are good enough, why not let them make all the decisions?
Ruth's answer was (refreshingly) pragmatic. She described experienced human judgement as essentially "an LLM of your own experience on a pit wall". When someone with real-world experience challenges a data-driven conclusion, nine times out of ten they've spotted a second-order effect the model hasn't captured. For example, overtaking one car versus overtaking a train of cars isn't simply additive. The physics, the driver experience, and the strategic implications are fundamentally different.
However, Ruth also acknowledged the flip side. Humans have significant anchor bias towards extreme events. We remember spectacular successes and crushing failures far more vividly than average outcomes. That's why it's crucial to numerically challenge gut feelings, even your own, to ensure decisions are grounded in evidence rather than the most memorable race from five years ago.
Perhaps the most valuable insight Ruth shared was about F1's culture of embracing failure. In most industries, spending a million pounds on development that yields a 0.02% improvement (which competitors can copy within six weeks) would be considered wasteful. In Formula One, it might earn you a promotion.
Ruth referenced Mercedes team principal Toto Wolff's response after a disastrous race at Hockenheim in 2018, despite the team winning everything that year. He said it was the day competitors should fear them most, because "it's the days that we fail are the days that we get better. It's a lot harder to learn from your mistakes when you're covered in champagne."
This philosophy of "pulling the strings of failure" (borrowed from NASA) drives innovation in F1. The sport operates under a cost cap, forcing teams to do more with less whilst maintaining relentless innovation. The solution lies in creating a culture where people embrace pressure as a privilege and view failures as essential stepping stones to success.
Ruth's excitement about F1's future centres on leveraging AI to enhance the fan experience. With 75 years of broadcast video archives, natural language queries can now surface specific moments instantly. This capability helps F1 serve both die-hard fans who've watched for decades and newcomers drawn in by Netflix's Drive to Survive series.
The lesson for businesses is clear: AI isn't just about internal efficiency. It's about creating better customer experiences and telling more compelling stories with your data.
Whether you're building a scale-up or refining strategy at an established firm, F1's approach offers a masterclass in combining data rigour with human judgement, embracing calculated risks, and creating a culture where failure drives progress rather than fear.