Moving generative AI into production
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.
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.
