Come conciliare la minimizzazione del rischio negli investimenti in intelligenza artificiale, la relativa immaturità delle tecnologie nell’attuale fase di sviluppo, e il paradigma dell’agile.
Dall’introduzione:
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—but 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 failures, we’d be remiss not to point out the potential damage of organizational failures.
An organization fails without a corporate will to even consider AI projects because its leadership doesn’t 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’t speak to building institutionalized beliefs that enable you to avoid organizational failures in this book, but we do help AI projects address failure.