Ahead of the New York City E-Prix – the only Formula E electric car races in the U.S. this season – drivers Sam Bird and Robin Frijns spent hours speeding around the bends of the Brooklyn Street Circuit track. But in fact, they raced from inside Audi's facilities—in Munich, Germany.
This is how Envision Racing team drivers prepare for all their races: through data-collecting simulators that help them scope out the track and test potential braking and acceleration opportunities. In Formula E races, managing the race car's energy is as much a challenge as weaving between competitors' cars at speeds of up to 170 mph. This kind of preparation can mean the difference between a podium-finish or a dead battery before the race even comes to an end. Indeed, Envision Racing wrapped up the 2018/2019 season with a first-place finish for Frijns during Race 2 at the New York City E-Prix, securing a 3rd-place full season ranking for the entire team.
These simulators are just one example of the sophisticated ways that Envision Racing has deployed AI and analytics to help it beat the competition. But you may be wondering: what can I, or my company, learn from a car-racing team?
It's no secret that AI is a priority for most companies. In fact, more than half of companies plan to use AI to transform processes by 2021. That said, AI isn't a magical, fix-all tool. At Genpact, we work with companies in numerous industries, and we've learned that to get the most out of AI investments you must have a deliberate strategy.
Here are three fundamental AI lessons from Envision Racing that companies in nearly any industry can apply: