Artificial intelligence, machine learning, and intelligent automation are commanding huge mind share among business and technology leaders today. There’s a lot of hype fueling that interest. Technology and consulting companies are pumping millions of dollars into marketing campaigns that make it seem that if you don’t have a talking robot on your road map, you will quickly become irrelevant. Business leaders fear their organizations will be left behind with no chance of recovery as their competitors become radically more efficient and effective.
On the flip side, articles abound about the potential negative impact on jobs and the risks and unintended consequences (legal, regulatory, reputational, financial) of turning more decisions and actions over to machines. Hype in tech is nothing new. What’s different this time is the degree to which reasonable and knowledgeable people believe that there is, indeed, a real urgency to get going with AI now. Dan Vesset, group vice president, analytics and information management, at market research firm IDC, warns that “if you’re not starting to invest, there’s the real possibility of being left behind forever.”
MIT research fellow and data science expert Tom Davenport and AI pundit Vikram Mahidhar explain the reason executives shouldn’t delay in their Harvard Business Review article titled “Why Companies That Wait to Adopt AI May Never Catch Up:” A number of foundational pieces must be in place to be successful with AI. These include talent, which is in short supply; having the right data infrastructure as well as sufficient quantity and quality of data to train your models; deciding how AI will be governed; and managing change in the organization, among other things.
But where do you start if you’re not a large, tech-forward enterprise? In this research report, we share the insights and real-world experiences of over a dozen leading CIOs, chief data officers, and AI experts. We explore how they identify the best opportunities for AI in their organizations; the necessary building blocks of data, technology, and talent; and the various types of risk involved and how to mitigate them.
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