Automation Alley recently brought together key representatives of Michigan manufacturers to answer two key questions proposed by Fraunhofer USA: “How is industry currently using AI?” and “How do we train the workforce using AI?” The conversation was wide-ranging and deeply valuable for all that attended the workshop. In this blog post, we dive into the key takeaways from this session of manufacturing needs in both the adoption of AI as well as AI workforce training solutions.
Automation Alley recently brought together key representatives of Michigan manufacturers to answer two key questions proposed by Fraunhofer USA: “How is industry currently using AI?” and “How do we train the workforce using AI?” The conversation was wide-ranging and deeply valuable for all that attended the workshop.
AI adoption relies heavily on data science: the storage, mining, application, manipulation and usage— which can be highly confusing and often expensive to enable. It’s often easier for manufacturers to wait until they better understand the technology. But by then, they may be too late as clients shift to manufacturers who have adopted AI.
Below we dive into the key takeaways from this session of manufacturing needs in both the adoption of AI as well as AI workforce training solutions.
Challenges and opportunities of AI usage in industry
Manufacturers expressed optimism in the application of AI tech in ways that enable them to detect anomalies that will help avoid corruption of data — either maliciously or through error — ensuring that they’re not making bad decisions with confidence. Additionally, its use in material planning, inspection, quality (vision systems), equipment maintenance and energy consumption are being favorably considered.
Manufacturers do see risk, however, in the ways that AI is applied, such that if the technology makes a mistake, manufacturers need to be able to explain why it happened and how to prevent it from happening again in the future. Another risk concern was expressed in the fear of overwriting valuable data through the AI’s application across multiple systems.
Challenges and opportunities of AI usage in workforce training
Manufacturers wanted a better way to define the skills that allow support of the AI from the plant floor: what are the baseline skills requirements? Can the technician perform diagnostics, programming, and modifications on the AI? Where can the applications training for AI be taught so that it’s not a highly paid data scientist that shows up, but an upskilled engineer or technician? These skills should be taught at local universities, community colleges or trade schools that have, for example, successfully adopted application training for robotics.
The manufacturing group further expressed a desire to have standardized training that can transcend controls engineers, mechanical engineers, and electrical engineers—training that resolves the need for the three to four skills that most often are required in maintenance of the AI.
The question of online training seemed acceptable for the more theoretical applications of AI, but whenever the plant requires in-person interaction, they believe that training should be in-person.
AI is a tool that can be used by manufacturers to close the skills gap and continuously improve operations. Conversations like these are important first steps to ensure needs are being met on the factory floor as the technology continues to evolve and its usage expands within industry.