How an e-motor racing team is plugging into AI for that extra surge

When you have to take a decision in a split second while travelling at over 150 mph, with competitors racing alongside, you need more eyes and ears on the ground.

That is a daily reality for Envision Virgin Racing, a founding team in the Formula E championship, the ‘electric international motor racing championship. Participants include Audi, BMW, Mahindra, Nissan, and Jaguar as well as Germany’s HWA Racelab, Monaco’s Venturi and Chinese Nio. Mercedes-Benz and Porsche are set to join the next racing season in November.

Professional services firm Genpact has embedded artificial intelligence (AI) into the race series to help turn data into a competitive edge for Envision Virgin Racing’s drivers. AI assesses massive amounts of data to unearth patterns which analyse systems, test numerous possibilities and recommend a course of action. Genpact’s data scientists have uncovered competitive insights from alternative data that enhance the team’s race strategy. Heat maps show rivals’ driving patterns, preferences, and missteps.

Sanjay Srivastava, Chief Digital Officer, Genpact says a Formula E race is a great metaphor for business. “It essentially condenses a year-long business cycle, and all the strategies and competitive issues that go with that, into a 45-minute, high-intensity, action-packed race,” he says.

The digital transformation work the company does with Envision Virgin Racing provides real-world examples on how to leverage AI and analytics to win on the track. “As Envision Virgin Racing needs to predict how other drivers will act to inform its race strategy, all companies need competitive intelligence,” says Srivastava. As much as a consumer goods company launching a product in a new geography must understand the competitive landscape, pharmaceutical firms need to analyse drug efficacy and safety. Eventually, a small advantage in a crowded marketplace can make a huge difference.

Motorsport, likewise, has turned to technology to gain a competitive edge. Along with the racing team, Genpact has brought together the latest in vehicle technology and AI-powered advanced analytics. These will generate new ways to approach racing strategies and drive deeper insights from data. Genpact is also helping Envision Virgin Racing clean, organise, and harness its vast volumes of data.

The company applies machine learning to release new predictive insights. “We are building tools and developing strategies that help improve the car and driver performance while managing battery power more efficiently,” says Srivastava.

Additionally, the company is determining new ways to look at data for better insights on Envision Virgin Racing’s competitors and considering new external data points in different ways.

The effort is paying off with the new insights already enhancing Envision Virgin Racing’s strategies and performance. “The team are still the only ones that have had both of their drivers win at least one race so far this season,” adds Srivastava.

The partnership leverages the tech firm’s tech expertise to drive transformative outcomes. This includes using AI for pattern discovery to organise and make sense out of myriad information sources both within and outside the car. These range from mechanical aspects and environmental concerns like temperature and weather patterns to governance factors like race regulations.

Genpact works on several streams around operations: be it enhancing race operations with accurate prediction, alternative data (analysing new insights for profiling rivals), and building peak performance (uncovering driver insights at scale). One of the key strategies is the Lap Estimate Optimiser, a tool Genpact built, that considers the many scenarios the team may face on the track, from shifting weather conditions to crashes, and estimates how many laps there will be in the 45-minute plus one lap race.

”Through this, we help Envision Virgin Racing’s drivers manage their cars’ energy more effectively and avoid the horror of running out of energy right before the finish line,” says Srivastava. Interestingly, there is a team of data scientists based in India analysing data and developing algorithms to optimise how teams’ drivers perform on the racing track.

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