This playbook explores areas where manufacturers need to get AI right to deliver useful results in addressing production, labor, and market pressures. Manufacturers are looking to AI as a solution to increase productivity, reduce downtime and waste, address skilled-labor shortages, and respond more quickly to market conditions. But successful AI adoption requires organizational and process changes in addition to technology, and AI might not always be a good fit for every production line task. This playbook explores five areas where manufacturers need to get AI right to deliver useful results, starting with distinguishing between relatively low-consequence AI applications and systems that could affect physical operations. Demonstrating reliable performance before receiving greater operational authority is key. Highlights include: Why industrial AI requires meticulous vetting of applications, and the use cases in which it can work. The role of data sources that may need to be connected in creating a reliable data foundation. Why build “mistrust and verify” into the deployment process and how to create a pre-action checklist.
