What is holding up AI adoption, and where is it already in use?
Even early concerns related to artificial intelligence (AI) have not appeared to slow its adoption. Some companies are already seeing benefit and experts are saying companies not adopting new technology will not be able to compete over time. However, AI adoption seems to be moving slowly despite early successful case studies.
Why AI is Moving so Slow in Manufacturing?
AI is growing, but exact numbers can be difficult to obtain, as the definition of technologies such as machine learning, AI, machine vision, and others are often blurred. For example, using a robotic arm and camera to inspect parts might be advertised as a machine learning or an AI device. While the device could work well, it might only be comparing images taken to others that were manually added it to a library. Some would argue this is not a machine learning device as it is making a preprogrammed decision, not one “learned” from the machine’s experience.
Going forward, this article will use general terms when mentioning AI technology. But when deciding on a design or product, make sure you understand the differences between terms such as supervised vs. unsupervised, and other buzzwords that might get blurry through sales and marketing efforts.
According to a Global Market Insights report publish in February this year, the market size for AI in manufacturing is estimated to have surpassed $1 billion in 2018, and is anticipated to grow at a CAGR of more than 40% from 2019 to 2025. But other resources insist that AI is moving slower. Some resources are often comparing AI case studies to the entire size of the manufacturing market, talking about individual companies investments, or specifically AI on a mass scale. From this prospective, AI growth is slower, and that is for a few reasons other than the aforementioned.
AI is still a new technology. Much of the success has been in the form of testbeds, not full-scale projects. This is because in large companies, one small adjustment could affect billions of dollars, so managers don’t want to test full-scale projects until they’'ve found the best solution. Additionally, companies of any size need to justify or guarantee a return on investment (ROI). This leads to smaller projects, a focus on low-hanging fruit, or projects that can be isolated as a testbed.
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