Essential Insights into Artificial Intelligence

September 22, 2020

During a recent InForum event, I had the honor of introducing the topic of artificial intelligence (AI) to a group of amazing women. Topics for InForum events vary, so I was excited to bring technology to the forefront of this session. And while I was able to offer a greater level of understanding, I also gained a few valuable insights along the way.

Artificial Intelligence and The Internet of Things

Listening to some questions from the audience, I began to see parallels between AI and IoT from an evolutionary perspective. As both of these technologies gain traction, companies are still trying to decide if the technologies are the right fit, how best to leverage them, and how it impacts the business. At SpinDance, we have had many organizations reach out to us because they know IoT needs to be part of their future, but they struggle to figure out the “where” and “why.” This issue also exists with AI. Because without a compelling value proposition, you’re likely to find yourself with many dollars spent—and little to show for it. The significant investment must be made upfront to increase the rate of success. 

AI and IoT don’t just mirror in upfront investment. Similarities also exist after implementation. Because AI and IoT are not simply “set it and forget it” solutions. Both require tweaking and improvement to achieve optimal results. For IoT, you can connect your first product. But, if you aren’t listening to user feedback to evolve the capabilities, you’ll be left behind. For AI, solutions are not perfect right out of the gate. They require reviewing the results and tweaking to increase accuracy – all to drive better decision making. This means those who expect immediate returns are often left disappointed, so they walk away without taking the necessary steps to reap the benefits. When tackling your first initiative, plan to include upfront and ongoing resource investments.

AI Does Not Equal Sci-Fi

I know many individuals believe Artificial Intelligence is only in the movies or is a robot that looks and acts like a human. Yet people don’t realize that even today the basics of AI are applied in many ways – and they’re already benefiting from it both personally and professionally. Think about the last time you bought something online. Were other items suddenly recommended for purchase? Machine Learning (a subset of AI) is at play here. The machine is learning buyer behavior to drive revenue, and hopefully, increase customer satisfaction.

In Manufacturing, AI is not leveraged solely in the automation of tasks normally done by humans. In fact, many applications improve processes while still leaving tasks to humans. For example, take the concept of predictive maintenance. AI can identify when a machine needs repair to help avoid quality issues or downtime. People are still required to fix it, but they’re made aware before it costs the company additional money from unexpected outages.

The Largest Hurdle – Good Data in the Right Place

When discussing where things go wrong and why AI can be such a challenge, you start to see an intersection between two modern-day buzzwords: Big Data and AI. Big Data is a term that’s been circulating for years. The importance and execution of Big Data have left many organizations with a lot of data and very few ideas on how to leverage it in a useful way. This stockpile of data highlights challenges when an organization tackles AI.

Implementing an AI solution does require a lot of data, but more importantly, it requires a lot of good data in the right place. Think of your organization today. Does the manufacturing data reside in the same place as the supply chain data? Likely not. Are there controls over the data inputs to ensure you’re avoiding the “garbage in, garbage out” scenario? Who decides what new information is valuable, and where that information will be collected? This is why data strategy is essential.

AI and Layers of an Onion

Eliezer Yudkowsky, an American researcher specializing in artificial intelligence said, “By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” 

Artificial intelligence has many layers—and some aren’t obvious. The danger lies in thinking it’s only about technology and outcomes. In today’s quickly advancing world, you can’t stop at those outer layers because you’ll find yourself forgetting critical factors—like security, or even ethics. Every piece of information collected must be evaluated and its use scrutinized. Because even though something feels innocent on the surface, it can have implications you never imagined when used in the wrong way. Or, even worse, when it’s exposed to an outside source.

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About the Author

Kim Burmeister, Vice President of Customer Experience and Delivery at SpinDance

Kim leads delivery teams to help companies turn their IoT vision into reality. She believes that all aspects of life and business can be improved by listening to feedback, even the stuff that is hard to hear. Kim has a bachelor’s degree in Logistics from Central Michigan University and spent much of her early career working for a Transportation Management Software company where she quickly became intrigued by software and its impact on the world. As a certified Scrum Product Owner, she dove headfirst into technology and making life easier through its application. Her passion is finding ways to drive improvements that are felt by her team members and customers.