This article was originally published in Training Magazine.
As the global skills gap continues to widen, we’re witnessing increased negative effects on companies’ bottom lines, including low productivity and a long time to proficiency. On-the-job training remains a costly necessity in industrial settings.
Machine learning is set to make a huge impact on blue-collar jobs, and it’s not the bogeyman some people fear. There’s a lingering sentiment that blue-collar jobs are on the front line of an automation takeover, but the fact is that it’s not easy to fully automate any occupation.
In reality, blue-collar jobs will be some of the most difficult to automate, but that doesn’t mean artificial intelligence (AI) and big data won’t have a huge role in transforming the industry. By working alongside humans as complementary assets, tools powered by AI and machine learning will seriously improve safety, productivity, and efficiency.
Industrial enterprises have seen a rise in new tech and digital tools that offer considerable improvements in the workplace, yet they still face many challenges for widespread adoption.
AI-powered digital assistants are changing training for blue-collar jobs in the aerospace, defense, automotive, and energy industries. Instead of spending many years learning by dutifully following someone’s orders, in that teacher’s idiosyncratic manner of instruction, trainees now can learn through an AI-powered digital assistant on the job.
Digital assistants now can issue customized instructions for operating heavy machinery based on prior knowledge and skill level. Using AI and machine learning, they curate instructions and display them on any wearable or mobile device for workers to complete tasks efficiently, reducing the risk for workplace accidents.
AI software like this already has received support from Lockheed Martin, BMW, and Samsung.
- Function at a higher level of competency with less pre-training
- Develop their competency on the job through a digital “master craftsman”
- Address their workforce shortages using less experienced personnel
- Reduce or enhance corporate skills training with machine learning-based instructional guidance