生成式AI，特别是大型语言模型（LLM），正迅速成为企业解决复杂问题的关键技术。其在客服、数据分析等领域的应用前景广阔，吸引了大量企业关注。麦肯锡数据显示，今年使用生成式AI的企业比例几乎翻番，达到65%。然而，生成式AI的部署也面临诸多挑战，包括成本高、复杂性高等，导致许多企业在实施过程中进展缓慢。尽管生成式AI的经济影响潜力巨大，但企业需要找到高效构建和部署AI项目的方法，才能真正实现其价值。

🤔 **生成式AI应用迅速增长：** 今年使用生成式AI的企业比例接近翻番，达到65%，大多数企业预计生成式AI将提升其生产力，尤其在IT、网络安全、营销、客服和产品开发等领域。

📈 **生成式AI经济影响潜力巨大：** 生成式AI预计每年将带来高达4.4万亿美元的GDP增长，其对生产力的影响可比互联网、机器人自动化和蒸汽机等技术。

🚧 **生成式AI部署面临挑战：** 企业在生成式AI部署过程中遇到诸多障碍，包括成本高、复杂性高等，导致许多企业对AI项目进展不满意。尽管许多企业计划在未来一年部署生成式AI项目，但目前实际投入生产的案例仍然较少。

💡 **高效部署是关键：** 企业需要找到方法，高效地构建和部署AI项目，并确保其组件易于理解和扩展，才能充分发挥生成式AI的潜力。

💰 **成本和复杂性是主要障碍：** 生成式AI系统的实施成本和复杂性并非易事，这成为阻碍企业快速部署生成式AI的主要因素之一。

Generative AI has taken off. Since the introduction of ChatGPT in November 2022, businesses have flocked to large language models (LLMs) and generative AI models looking for solutions to their most complex and labor-intensive problems. The promise that customer service could be turned over to highly trained chat platforms that could recognize a customer’s problem and present user-friendly technical feedback, for example, or that companies could break down and analyze their troves of unstructured data, from videos to PDFs, has fueled massive enterprise interest in the technology. 

This hype is moving into production. The share of businesses that use generative AI in at least one business function nearly doubled this year to 65%, according to McKinsey. The vast majority of organizations (91%) expect generative AI applications to increase their productivity, with IT, cybersecurity, marketing, customer service, and product development among the most impacted areas, according to Deloitte. 

Yet, difficulty successfully deploying generative AI continues to hamper progress. Companies know that generative AI could transform their businesses—and that failing to adopt will leave them behind—but they are faced with hurdles during implementation. This leaves two-thirds of business leaders dissatisfied with progress on their AI deployments. And while, in Q3 2023, 79% of companies said they planned to deploy generative AI projects in the next year, only 5% reported having use cases in production in May 2024. 

“We’re just at the beginning of figuring out how to productize AI deployment and make it cost effective,” says Rowan Trollope, CEO of Redis, a maker of real-time data platforms and AI accelerators. “The cost and complexity of implementing these systems is not straightforward.”

Estimates of the eventual GDP impact of generative AI range from just under $1 trillion to a staggering $4.4 trillion annually, with projected productivity impacts comparable to those of the Internet, robotic automation, and the steam engine. Yet, while the promise of accelerated revenue growth and cost reductions remains, the path to get to these goals is complex and often costly. Companies need to find ways to efficiently build and deploy AI projects with well-understood components at scale, says Trollope.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.