Few technological advances have generated as much excitement as AI. Particularly, generative AI seems to have taken business discourse to a fever pitch. Many manufacturing leaders express optimism: Research conducted by MIT Technology Review Insights found ambitions for AI development to be stronger in manufacturing than in most other sectors.

Manufacturers rightly view AI as integral to the creation of the hyper-automated intelligent factory. They see AI’s utility in enhancing product and process innovation, reducing cycle time, wringing ever more efficiency from operations and assets, improving maintenance, and strengthening security, while reducing carbon emissions. Some manufacturers which have invested to develop AI capabilities are still striving to attain their objectives.

This study from MIT Technology Review Insights seeks to know how manufacturers are generating advantages from AI use cases—particularly in engineering and design and in factory operations. The survey included 300 manufacturers which have begun working with AI. Most of those (64%) are currently researching or experimenting with AI. Some 35% have begun to place AI use cases into production. Many executives that responded to the survey indicate they intend to spice up AI spending significantly throughout the next two years. Those that haven’t began AI in production are moving progressively. To facilitate use-case development and scaling, these manufacturers must address challenges with talents, skills, and data.

Following are the study’s key findings:

• Talent, skills, and data are the predominant constraints on AI scaling. In each engineering and design and factory operations, manufacturers cite a deficit of talent and skills as their hardest challenge in scaling AI use cases. The closer use cases get to production, the harder this deficit bites. Many respondents say inadequate data quality and governance also hamper use-case development. Insufficient access to cloud-based compute power is one other oft-cited constraint in engineering and design.
• The most important players do probably the most spending, and have the best expectations. In engineering and design, 58% of executives expect their organizations to extend AI spending by greater than 10% throughout the next two years. And 43% say the identical relating to factory operations. The most important manufacturers are much more prone to make big increases in investment than those in smaller—but still large—size categories.
• Desired AI gains are specific to manufacturing functions. Essentially the most common use cases deployed by manufacturers involve product design, conversational AI, and content creation. Knowledge management and quality control are those most often cited at pilot stage. In engineering and design, manufacturers chiefly seek AI gains in speed, efficiency, reduced failures, and security. Within the factory, desired above all is healthier innovation, together with improved safety and a reduced carbon footprint.
• Scaling can stall without the precise data foundations. Respondents are clear that AI use-case development is hampered by inadequate data quality (57%), weak data integration (54%), and weak governance (47%). Only about one in five manufacturers surveyed have production assets with data ready to be used in existing AI models. That figure dwindles as manufacturers put use cases into production. The larger the manufacturer, the greater the issue of unsuitable data is.
• Fragmentation have to be addressed for AI to scale. Most manufacturers find some modernization of information architecture, infrastructure, and processes is required to support AI, together with other technology and business priorities. A modernization strategy that improves interoperability of information systems between engineering and design and the factory, and between operational technology (OT) and data technology (IT), is a sound priority.