Manufacturers are embracing AI even as they struggle to find the talent and data required.
What’s new: The market-research arm of MIT Technology Review surveyed manufacturers’ use of AI in engineering, design, procurement, and production. All respondents were at least experimenting with AI, and many expect to launch their first deployments in the next year or two. Microsoft sponsored the research.
How it works: The authors interviewed executives at 300 manufacturers in aerospace, automotive, chemicals, electronics, and heavy equipment. All were either applying or considering AI in product design or factory operations.
- The most common uses of AI in production involved designing products, creating content such as technical documentation, and building chatbots. The most common uses in earlier stages were knowledge management and quality control.
- 35 percent of respondents had deployed AI in production. Another 37 percent were experimenting with AI, while 27 percent were conducting preliminary research.
- 45 percent of respondents in electronics and 39 percent in automotive had deployed AI in production. Larger companies were more likely to have deployed AI (77 percent of companies with revenues over $10 billion compared to 4 percent of those with revenues under $500 million). Larger companies were also more likely to forecast increases in AI spending in the next two years.
- Asked to name the biggest challenges to scaling up uses of AI, respondents most often pointed to shortages of skills and talent. Asked to name challenges their company faced with respect to data, they pointed to maintaining data quality, integrating data from different parts of an organization, and governing data.
Behind the news: Manufacturers are using AI to help design products, visually inspect goods, and maintain equipment. The field has attracted major players: Last year, Microsoft and Siemens launched a pilot of Industrial Copilot, which enables users to interact in natural language with software that drives assembly lines.
Why it matters: Manufacturers want to use AI, but many face obstacles of talent and data. That spells opportunities for budding practitioners as well as for manufacturers that lack infrastructure for collecting and managing data.
We’re thinking: One key to successful implementation of AI in manufacturing is tailoring systems to the unique circumstances of each individual facility. The highly heterogeneous tasks, equipment, and surroundings in different factories mean that one model doesn’t fit all. Developers who can solve this long-tail problem stand to reap rewards.