A large auto manufacturer asked a consulting firm to evaluate its competitive position in relation to ride-sharing startups building autonomous vehicles. Instead of viewing this as a classic strategy project, with a business case, PowerPoint decks, and five-year projections, the firm created a “game” that the automaker could “play” against its competitors. An artificial intelligence (AI) system modeled the voluminous individual choices available to customers, companies, and other entities as digital twins (a digital twin is a computerized replica of a physical asset, process, consumer, actor, or other decision-making entity). The hundreds of thousands of simulations suggested many strategic bets, option-value bets, and “no-regret strategies,” or moves that made strategic and financial sense in a multitude of situations. The selection of those strategies, in turn, made the AI system smarter through learning mechanisms called reinforcement learning, which then further empowered humans to make better decisions. As time progressed, the company was able to choose precise market approaches, pricing, advertising, and customer strategies for multiple cities and communities.
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