The vision of AI in manufacturing is seductive: “lights out” factories that are so heavily automated that they almost run themselves, with human workers monitoring operations from an off-site control center. Indeed, a few of the most advanced robotics factories have already passed a crucial line, with robots building robots.
That’s the future that so many COOs desire, according to our global survey of more than 100 COOs at manufacturers with at least $1 billion in revenues (see sidebar, “Our methodology”). Companies are raising their bets on digital and AI technologies.
Over the past five years, one-third of respondents say that their companies spent less than 1 percent of the cost of goods sold on digital and AI. But when asked about their plans for the next five years, only 7 percent of respondents plan to keep their investment levels that low. The other 93 percent will spend more, with almost one-third intending to spend at least 5 percent.
This commitment is visible through much of the manufacturing world. Among the latest applications that factories have submitted to join the Global Lighthouse Network (GLN) of technologically advanced manufacturing sites,1 fully 90 percent of tech use cases now incorporate AI.
What’s far less clear is whether that money will be well spent. COOs’ survey responses reflect broader uncertainty about the time and expense that may be required for AI to reach the scale where it can generate real value. About two-thirds of respondents indicate that their companies’ AI implementation is still at the exploration or targeted-implementation stage (Exhibit 1). A mere 2 percent say that AI is now fully embedded across all operations.
So far, COOs’ priorities look much as one would expect. The use cases rising to the top of the list—such as predictive maintenance, schedule optimization, and process improvement—are familiar ones that manufactures have targeted for decades.
What’s striking, however, isn’t where COOs are investing the most but where they are investing the least. At the bottom of COOs’ collective priority list are several fundamental people and tech elements that are critical to get right for AI to meet its promise of generating sustained productivity improvement.
That suggests that many COOs may be overlooking serious risks to their investment plans. And, given how much more they are likely to spend, the omissions could have serious long-term consequences.
At first glance, COOs’ AI priorities look like the right ones. When asked where they expect digital and AI to deliver the greatest impact, COOs emphasize the fundamentals: expanding production capacity, boosting labor productivity, improving quality, and increasing end-to-end visibility. To minimize wasted effort on pilots, their road maps call for scaling a focused portfolio of five to 12 use cases by 2030. Target use cases, such as factory scheduling, digital performance management, and digital twins, are consistent with spending plans tilted toward shop floor automation, robotics, and the systems that control factory operations.
These choices can appear especially attractive when they continue investments that many manufacturers have made for years, as in robotics. Others are particularly important for reaching scale quickly, as with AI-informed scenario planning and data-driven decision support.
