Generative AI is a form of artificial intelligence that “self-learns” via large amounts of publicly available data. The speed at which it processes data and responds to natural language prompts to create highly relevant content is unprecedented. And its impact will undoubtedly be significant and far reaching. In fact, According to Gartner, generative AI is rapidly becoming “a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet.”
In a recent article in Industry Today, ETQ CEO Vick Vaishnavi shared his insights on the emerging roles generative AI can play to boost quality in business and manufacturing, along with its limitations and caveats.
As Vick shared, early versions of generative AI, such as Open AI’s ChatGPT or Google Bard, are already in use today. Businesses are using them to generate content, create customer communications and other tasks. In software development they’re being used to document software code, predict code sequences and automate key tasks. And, in manufacturing, they are beginning to play a role with predictive maintenance and supply chain optimization.
Yet, while generative AI will play a major role in helping companies leverage the vast amounts of data available internally and across the world, as a nascent technology, there are obstacles that must be addressed. For example, there’s currently no way to confirm the accuracy of the information it produces and the potential bias of the data it uses. There are also fears that generative AI’s ability to create simulated imagery or mass communication could enable those with sinister intent to spread false information. There is also widespread concern over how far generative AI will go. Will it replace workers, for example?
Generative AI in Quality Manufacturing
What role can generative AI play in enabling greater overall quality in manufacturing? In short, a significant one. AI is trained on massive data-sets that enable it to predict future outcomes and help manufacturers make faster, more informed decisions, such as anticipating supplier disruptions, optimizing production lines, or initiating product quality improvements. Consider the following ways that manufacturers can use generative AI to enable a new level of intelligence:
- Documentation and content creation. With its ability to generate new content, generative AI will be able to instantly create training documentation, product user guides and more, from simple or complex prompts.
- Since it not only creates text, generative AI can assist in the product design process, creating new form factors and aesthetics, based on a set of rules and context.
- Predictive maintenance. Generative AI can also help with predictive maintenance. Based on historical data, it can predict when a system or equipment is likely to fail or impact safety, offer warnings and, in some cases, take the steps needed to correct it.
- Software development. Generative AI will play a role in software coding and development, enabling critical systems to communicate, as well as to build new applications. In fact, a recent McKinsey study found that software developers can complete coding tasks up to twice as fast with generative AI.
- Bridging the digital divide. Generative AI can help bridge the gap for workers who may be experts in their respective roles, yet struggle to embrace technology. They can verbalize their request and instantly receive feedback.
Clearly, there is a lot to digest when it comes to generative AI. It’s important to remember that, as with any transformative technology, manufacturers should be open to the power of AI to increase productivity, safety and quality, as long as it works in concert with humans, and not as an opposing force.
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