Revenues directly realized from AI remain strong and largely consistent. Respondents report the highest cost benefits from AI in supply chain management.
Adoption of artificial intelligence has more than doubled since 2017, a recent analysis by McKinsey finds. However, AI adoption appears to have plateaued as well — the proportion of organizations using AI has plateaued between 50% and 60%.
Looking inside AI projects, specific capabilities have been on the rise. These include technologies such as natural-language generation, robotic process automation, and computer vision, which have seen a doubling in usage — from 1.9 in 2018 to 3.8 in 2022.
Areas of the business seeing AI augmentation include marketing and sales, product or service development, and strategy and corporate finance. Respondents report the highest cost benefits from AI in supply chain management, the study’s authors, Michael Chui, Bryce Hall, and Alex Singla, all with McKinsey, report. These deployment areas have supplanted manufacturing and risk, which led as AI use cases in 2018.
Revenues directly realized from AI remain strong and largely consistent, the study shows. About a quarter of respondents report that at least five percent of their organizations’ earnings was attributable to AI in 2021, in line with findings from the previous two years. “We also found AI-related cost decreases are most often reported in supply chain management and revenue increases in product development and marketing and sales,” according to Chui, Hall, and Singla.
Approximately eight percent of companies are seeing seeing significant financial returns from AI, a percentage unchanged from the previous year’s survey. These leaders are “more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes,” the researchers observe. These advanced practices include AI development and deployment at scale, or what some call the industrialization of AI.
“Leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly,” Chui and his co-authors state. “They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms.”