【英文】德勤报告：技术趋势2024（62页）

What’s made generative AI so impactful is a convergence of factors. First, advanced hardware—primarily specialized AI chips used in training models—have  helped produce more advanced models such as large  language models (LLMs). These tools have gone mainstream due to a seamless user experience, enabling even  nontechnologists to engage with very advanced models. All this attention has kicked of a gold rush among  investors (fgure 3). Investors are pouring money into  startups that have generative AI technology at their core,  betting that we’re witnessing the dawn of a new paradigm for business technology, one where insights are  surfaced automatically, contracts review themselves, and  a never-ending stream of content is generated to keep  brands in front of their audiences. While there’s been plenty of talk about how AI may  threaten jobs, there’s no real indication that business  leaders are planning on using it to automate knowledge  jobs at any kind of scale. In a survey of leaders, improving content quality, driving competitive advantage,  and scaling employee expertise were the most common  reasons for deploying generative AI. Reducing headcount  was one of the lowest priorities.11 It looks more likely that  AI will liberate workers from rote, repetitive tasks and  free them to focus on more creative aspects of their jobs.  The picture that’s emerging is that AI is coming, and for  some, it’s already here. But, as the saying goes, leading  businesses know they can’t shrink their way to growth— that is, minimize risks or costs as a path to growth.12 This  means the most productive uses of generative AI won’t be  about replacing people but instead will focus on arming  employees with tools that help them advance and enhance  their productivity, knowledge, and creativity—which, in  turn, will help drive innovation in the enterprise.