Achieving Real Business Outcomes from Artificial Intelligence

Chapter 2. More Than Games and Moonshots

Although it might be easy to dismiss artificial intelligence (AI) use cases highlighted by the media as “moonshots” (e.g., curing cancer), publicity stunts (e.g., beating the best human players at Go and Jeopardy), too industry-specific (e.g., autonomous driving), or edge use cases and point solutions (e.g., spam filtering), we can apply deep learning to core strategic initiatives across many verticals. In fact, AI has already begun to demonstrate its value in large enterprises, even outside Silicon Valley and West Coast digital giants. Fortune 500 companies in industries like banking, transportation, manufacturing, retail, and telecommunications have also begun to take advantage of its power.

AI-First Strategy

In an AI-first strategy, AI operates at the core of a company, driving its product and decision-making. In several industries, new challengers are using this kind of strategy to successfully compete against incumbents. One example is in the financial industry, with companies like Citadel, Two Sigma, and Personal Capital maximizing return and reducing risk by creating the best machine intelligence. And in the automotive industry, it’s now easy to envision a day when people will decide to buy a new car based on its driving software and not on its engine, body design, or other buying criteria.

Across all industries, the use of deep learning has the potential to increase production, drive down cost, reduce waste, and improve efficiency, ...