Come conciliare la minimizzazione del rischio negli investimenti in intelligenza artificiale, la relativa immaturit\u00e0 delle tecnologie nell\u2019attuale fase di sviluppo, e il paradigma dell\u2019agile. <\/p>\n\n\n\n
Dall’introduzione:<\/p>\n\n\n\n
With a claim in a potentially $13 trillion market1 at stake over the next decade, companies are working diligently to take advantage of the high returns of embedding artificial intelligence (AI) in their business processes\u2014but project cost and failure rates are on the rise. Problematically, there is no standard practice for how to implement AI in your business. That makes it very difficult for business leaders to reduce their risk of project failure. Although this is a book about potential project <\/em>failures, we\u2019d be remiss not to point out the potential damage of organizational <\/em>failures. <\/p>\n\n\n\n An organization fails without a corporate will to even consider AI projects because its leadership doesn\u2019t understand how far behind they are from industry leaders. To use a bike race as a simplified metaphor: in AI, industry leaders are the small number of riders in the breakaway, most businesses are in the peloton, and those without the will to adopt AI forgot they were in the race and are watching on TV. We don\u2019t speak to building institutionalized beliefs that enable you to avoid organizational failures in this book, but we do help AI projects address failure.<\/p>\n\n\n\n