Podcast: AI for IT and OT: Getting Over the Hurdles and Finding the Benefits
Artificial intelligence (AI) has become the biggest buzzword that will likely define the 2020s. Even with its buzzworthy hype, AI is still very effective when used in the right way. For manufacturers, there are a variety of areas where it can be adopted to enhance and optimize operations. But the one area where it will lead is in data.
Jeff Winter, a thought leader in the fields of Industry 4.0 and digital transformation, has teamed up with Scott Achelpol, managing editor of Smart Industry, to create (R)Evolutionizing Manufacturing, a monthly series of chats about how industries of all sizes and budgets can embrace technology. This episode looks at how AI is able to transform manufacturing data into valuable, comprehensive insights.
Below is an excerpt from the podcast:
SI: Last month, we talked all about how data is everything. Isn't it especially important for companies to have data that is in order and accessible for any AI-related project or pilot to get off the ground? Are there use cases for AI that actually help companies make use of the data they've always gathered, but never have used to its full advantage? If so, give some examples, please.
JW: Sure. So first, is it super important to have companies have their data in order and accessible for AI projects, especially for pilots? Yes, you bet it is. And in this case, you can think of AI as, I love to use the Master Chef analogy, because AI can create incredible dishes, but only if you have the right ingredients. If your data is messy or incomplete or hard to access, it's kind of like trying to cook a gourmet meal with a fridge full of just random, unlabeled leftover messes. I mean, yeah, you could make food that's edible, but it most likely won't be very good. Clean, well-organized, and accessible data is the foundation for effective artificial intelligence. Also, data is crucial for AI because it's what AI uses to learn from and to make decisions, especially for machine learning and large language models. Having more data allows the model to be more accurate and effective, and more data means better learning, improved predictions, and a deeper understanding of all the little nuances. So essentially the more good data you have, the better your AI can perform. And this isn't true for other logic-based or rule-based systems where you only need a certain amount of data to make the system work.
Now on to the second part, AI use cases for taking advantage of existing data. So, one great use case is predictive maintenance. Many manufacturers have been collecting data from their machines for years. This is what historians do, and they really only look into them when they're on the hunt for something specific. They aren't just automatically taking advantage of it. Now by entering the world of artificial intelligence and by analyzing this historical data, AI can predict when machines are likely to fail and help schedule maintenance before the failure actually occurs.
Another fantastic example on a totally flip side is customer insights. So, retailers and service providers have just mountains of customer data, whether it's purchase histories or even preferences, feedback, you name it. AI can sift through all this data to uncover trends and patterns that were previously hidden. It can also help companies personalize their marketing and improve customer service and even help develop new products that better meet customers’ needs. So, you can imagine AI here is just kind of like a detective piecing together little clues to give you a complete picture of your customers’ desires and behavior.
This podcast was originally ran as Podcast: The future of AI in manufacturing and its real-world applications on Plant Services, an Endeavor Business Media partner site.