What business can learn from the racetrack

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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 Virgin 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 Virgin 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 Virgin 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 Virgin Racing that companies in nearly any industry can apply:

1. Collect data to spot weaknesses, strengths and opportunities

The simulators used by the Envision Virgin Racing drivers operate based on models created from all the relevant data the team can gather. This includes information on the circuit, competitors, local weather conditions, and a range of potential scenarios. The team then gathers the data generated from each simulated race and deploys AI to collect insights and identify patterns. In doing this, it is able to better spot drivers' strengths and weaknesses and ultimately predict how the race will unfold.

Tapping into existing data sets and constantly collecting new data lays the foundation for AI to spot critical trends that lead to actionable insights. For example, retailers and consumer packaged goods businesses must remain acutely aware of ever-shifting consumer tastes. By using AI, companies can analyze massive amounts of existing internal data (like customer service call logs) and external data (like social media) to uncover insights that help not only detect consumer concerns early, but also inform next steps for fixing a problem before it results in a lost customer.

2. Make smarter, faster real-time decisions

Unlike other forms of car racing, where drivers make pitstops to refuel mid-race, Formula E drivers can't recharge or replace their electric car batteries once the green flag waves. Formula E races have no set number of laps – instead, the race runs for 45 minutes, plus one lap. The driver who crosses the finish line first, at the end of that final lap, wins. So, Formula E drivers must accurately predict how many laps the race will likely run, and make the right decisions about how to manage their battery's electric charge.

This is where Genpact helped develop an AI-powered Lap Estimate Optimizer (LEO) that analyzes volumes of data to predict how many laps each race will have. With predictive insights from LEO, the driver can make more intelligent, split-second decisions when faced with unpredictable variables, like changes in the weather and on-track crashes. By the time the driver takes to the track, the team's engineers have analyzed over 10,000 variables in the race, and are better able to recommend a strategy that will result in the best race possible.

Similar forecasting technology can be a gamechanger in industries like manufacturing, where companies need to make informed supply chain decisions in real-time.

3. Operate as a fully connected team

You can't create something like LEO without incorporating expertise and information from the entire racing ecosystem. To do this, Envision Virgin Racing collects data from not only the engineering team and the drivers, but also simulator data, track temperatures, weather patterns, and previous race information to give the driver – the team—everything they need to win. This type of data-enabled connectivity is beneficial to any company because it draws insights from internal departments, external partners, the competitive landscape, and more.

We're already seeing this kind of technology benefit insurance companies, for example. When a client submits a claim – say, from a car accident – the claims officer must answer a lot of questions: is this the driver's first accident? Is that intersection a hotspot for collisions? The list goes on. By using AI, insurers can save the claims officer time and stress by weaving together data from claims archives, customer service, accounting, legal, and historical data surrounding the intersection where the accident occurred. The final result? A quickly reached, informed, low-risk claims decision.

The key to reaping the full benefits from AI investments lies in the creation of a smart, connected AI strategy. As the successes of Envision Virgin Racing prove, an AI strategy that prioritizes data, prediction, and a connected ecosystem can accelerate the team from the back of the line to the front of the pack.

The article was authored by "Tiger" Tyagarajan, Chief Executive Officer, Genpact, and was first published by Techonomy.

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