The Marriage of AI and IoT

Stephen DeAngelis

July 11, 2019

The relentless press of technology into our lives remains unabated. Critics constantly debate whether advancements are good or bad for humanity. Futurist Jason Silva (@JasonSilva) once stated, “Technology is, of course, a double edged sword. Fire can cook our food but also burn us.” People need to remember technology’s duality trait as they create or advance new ones. As technologies become a ubiquitous part of our lives, we require some assurances they are more likely to help than hurt us. According to Bill Gates (@BillGates), philanthropic founder of Microsoft, “The advance of technology is based on making it fit in so that you don’t really even notice it, so it’s part of everyday life.” We all know, however, technologies we “don’t really notice” can bite us.

Two technologies with the capacity to both help and hurt are artificial intelligence (AI) and the Internet of Things (IoT). Both technologies are maturing rapidly and both are technologies most people don’t really notice in their daily lives. In some ways, trying to the separate the IoT from AI is a fool’s errand. The IoT, as explained by most analysts, is an ecosystem with sensors at one end and advanced analytics platforms at the other end with the two ends connected by a machine-to-machine Internet. For example, Rachel Stinson writes, “The Internet of Things — IoT, for short — is made up of devices that connect to the internet and share data with each other. IoT devices include computers, laptops, smartphones, and objects that have been equipped with chips to gather and communicate data over a network. IoT devices have become a part of the mainstream electronics culture that people have adopted into. It is estimated that there will be up to 21 billion IoT devices by 2020, impacting how we interact with basic everyday objects.”[1]

Data is the tie that binds AI and IoT

Andy Chang, Director of Product Marketing at Kuka, asserts, “Data drives decision-making — or at least it should.”[2] Where do you get the data? The simple answer is: Everywhere. The rise of the IoT (or the Industrial IoT (IIoT)) means mountains of data are going to compiled from the millions of “things” connected to the Internet. Priya Dialani (@priyadialani) writes, “Data is essential for companies to penetrate down and create noteworthy experiences. It is evaluated that 99.5% of digital data isn’t dissected. The constant growth of data from both people and the Internet of Things gadgets will just keep on developing.”[3] The IoT generates data; however, as Dialani points out, without advanced analytics that data doesn’t inform decision making. According to Dialani, “Companies can address the data difficulty just by picking up a cognitive advantage. In the coming years, Artificial Intelligence is an absolute necessity to remain aggressive.” I might have used the term “progressive” rather than “aggressive.”

The cognitive advantage to which Dialani alludes comes from better decision making. Bain analysts, Michael C. Mankins and Lori Sherer (), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[4] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” Cognitive systems, like the Enterra Enterprise Cognitive System™ (AILA®) — a system that can Sense, Think, Act, and Learn® — can dramatically improve decision making. Dialani adds, “AI can analyze enormous lumps of data in minutes and create important results. It also gives a real-time and quick response. In order to come up with a future strategy, AI empowers real-time processing and response when connected with IoT gadgets.” She goes on to list some of the benefits of marrying AI and IoT:

  • Artificial Intelligence and IoT make increasingly versatile learning and analytical frameworks providing businesses with actionable insights.
  • Marrying AI and IoT encourages synchronization, correspondence, and integration.
  • The marriage of AI and IoT illuminates organizations about proactive moves that can be taken to remain competitive.
  • AI-empowered IoT frameworks are savvy, self-learning, and can help transform companies into digital enterprises.
  • AI and IoT can help automate processes across a company and help establish a highly collaborative environment.
  • An AI/IoT-powered enterprise can improve efficiency, execution, and maintenance.

Chang adds, “From improving decision-making to increasing efficiency, pinpointing the IoT’s value proposition can clear up confusion and open doors to a future of connectivity.”

AI is not a silver bullet

“There are plenty of areas where artificial intelligence is helpful and extremely useful,” writes Steve Roemerman (@SDRoem), chairman and CEO of Lone Star Analysis. “Areas like web marketing, retail product recommendations and consumer help desks can all streamline processes in addition to other benefits when using AI.”[5] He then asks a very important question: “While these areas reap the benefits, is AI the right choice in industrial applications like the Industrial Internet of Things or is there a better option?” In his opinion, “Many AI methods are self-taught, so they avoid the need for process mapping and other tedious analytical processes, making it seem to be the right fit for IIoT. Yet, only a few methods will apply. The most useful methods are not greedy for impossible amounts of data. They focus machine learning in explainable ways. The rest will fail badly.” In other words, do your homework. AI is a blanket term covering many methodologies. Like any technology, a business case needs to be made for implementing AI solutions. Roemerman asserts the best way to apply AI solutions in a company is by using it to address discrete challenges. He explains, “A hybrid AI approach using predictive and prescriptive analytics solutions are more effective for IIoT than traditional AI. The latter has its place, but for much of IIoT, it is too slow and too expensive. Additionally, traditional AI can be too cryptic when providing a solution. When AI algorithms complete training, their original creators often cannot explain how the AI produces its answers. Since human decision makers cannot understand the process, they are often reluctant to accept the AI’s conclusions. On the other hand, the best analytics companies break big questions down into smaller, easier-to-answer questions. This creates solutions which are easy to understand. Interpreting these predictions are simple and straight forward, so humans are more likely to act and act faster.”

Roemerman’s approach makes a lot of sense. The best solutions always come from asking the best questions. When considering how AI can complement IoT processes, ask penetrating questions about specific challenges you are trying to overcome. With IoT devices generating so much data, there can be no doubt cognitive technologies have a significant role to play. Humans need to decide what those roles are.

Footnotes
[1] Rachel Stinson, “The Inevitable Future of the Internet of Things (IoT)!Supply Chain Game Changer, 23 May 2019.
[2] Andy Chang, “Your IIoT Questions, Answered,” IndustryWeek, 15 May 2019.
[3] Priya Dialani, “The Synergy of AI and IoT,” Analytics Insight, 7 April 2019.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Steve Roemerman, “Is Artificial Intelligence the Answer for IIoT?Manufacturing.net, 7 February 2019.