The bigger your company, the more important it is that every team member is on the same page. When you're as big as Genpact, with 90,000 employees and twice as many partners, then collaboration is a top priority. Sanjay Srivastava is well aware of the challenges. As Genpact's Chief Digital Officer, he is front and center at the effort to make sure the disparate teams and employees within the company are working successfully in a collaborative organizational culture, as well as offering a satisfying customer experience. For Sanjay, there are three main factors that need a strong collaboration platform within a company. It starts with the idea of the business as a connected ecosystem that drives a collective intelligence. Then there's the concept of continuous learning and innovation that requires a collaborative framework to be successful. Finally, there's the convergence of domains, the ability to pull people together from different disciplines, with different experiences, and across ...

Formula E Racing, like its Formula 1 counterpart, relies on speed and strategy to win. But how do you crunch through the reams of data that you can get from an electric race car and analyze it in a way that would help your driver and your racing team beat the competition? And that's why he has partnered with Sanjay Srivastava, Chief Digital Officer of Genpact, to leverage data analytics and artificial intelligence (AI) to build a multi-layer platform that turns a mountain of data into actionable analysis. Formula E racing produces different types of data across many fronts. There's a set of telemetry data from the cars, a stream of large data sets that cars produce while they are on the road, and data from competing drivers and their vehicles. Then there's data gleaned from weather, satellite, traffic, and road patterns. All that needs a data analytics system that can interpolate the information as it comes in from all these sources and analyze it in real-time in a way that the driver and the racing team can absorb and act upon instantaneously. But, as Sylvain points out, that's easier said than done, especially since a Formula E race happens in just one day, and every second counts. As Sylvain and Sanjay explain, it starts with knowing how to structure the incoming information so that the driver and engineers can act upon it quickly. That means setting up the correct algorithms, developing an analytical infrastructure that--with the help of AI--integrates all of the different types of data, and synchronizing it to give the driver and engineers the whole picture and predict the likeliest outcomes in any given scenario in order to make the right decisions during the race. That also means creating a user interface for the data that's both comprehensive and instantly comprehensible to the driver. The work that Sylvain and Sanjay are doing has notable implications for business that goes beyond racing. The technologies they are developing will trickle down to make electric cars and sustainable energy better. The analytics tools they are creating can potentially be utilized by other companies to make better sense of data coming from multiple sources in order to make well-informed business and digital transformation decisions and do so quickly, and to manage their resources more efficiently. This transcript has been edited for length and clarity. Michael Krigsman: Formula E Racing involves cars, speed, data, and advanced technologies such as AI and machine learning.