AI and IoT convergence could herald a new era of technological change
In the past decade, many businesses have been built or re-imagined through IoT. More recently, we have seen stunning advances – and investments –in two other areas of technology: AI and machine learning. In the 2020s, these complementary technologies will converge to form a new wave of technological change: Artificial Intelligence of Things (AIoT).
Those who get AIoT right and effectively use its power in business and client service models stand to gain immediate and lasting competitive advantages. But to do that, organizations need to understand how AIoT came about, and what it’s already delivering for companies who have gotten it right.
With IoT, machines or “things” could communicate with other things and act on that information. By adding AI, the network can learn from past decisions, predict future activity and improve performance continuously. It is a perfect mutually beneficial relationship: AI adds value to IoT through improved decision making and IoT feeds the AI capability through connectivity, signaling and data exchange.
This is happening now. Today organizations develop real-time “thinking” networks. The AIoT systems solve a wide range of complex problems across many different industry verticals. That is why Gartner predicts that by 2022 more than 80% of enterprise IoT projects will feature an AI component.
How AIoT is being realized today
There are many current examples of AIoT changing the way businesses work, including manufacturing, sales and marketing, and automotive.
Manufacturing. Many manufacturers already use IoT to monitor their equipment. For example, an industrial manufacturer’s new AIoT deployment takes data available from IoT devices and combines it with the insight of AI to create an intelligent solution that not only offers predictive analysis on equipment, but also combines that with inventory, purchasing and servicing systems.
That means company uses AIoT to predict and take pre-emptive action on warehouse equipment, maintenance and the production floor. Rather than simply keeping tabs on their machinery, it enables them to operationalize that information, reduce unexpected downtime and on-site maintenance costs, and increase operational visibility and production.
What’s more, this application of AIoT reduces the need for engineers to come into contact with each other unnecessarily, allowing for safe social distancing – a practice which is likely to remain the norm for quite some time on the factory floor.
Sales and marketing. A major retail technology analysis company uses an AIoT solution to grow sales through customer insights. The company uses real-time proximity detection and mobile-based user behavior tracker to deliver personalized campaigns to customers in-store. Not only does this drive increased foot traffic, but it makes stores easier to navigate and offers more information to help end-customers buy more products — a feat in a challenging industry like retail.
Automotive. A global automotive company uses AIoT to predict failing or worn-out parts. The system uses existing recall, warranty, National Highway Traffic Safety Administration and social media data to provide them with visibility into which parts need replacements the most. What’s more, it enables them to provide free service checks to customers before parts fail. As a result, their customers experience fewer mechanical issues caused by failing parts, ensuring a better reputation for vehicle reliability, a crucial factor in an automaker’s brand reputation and equity with its customers.
How to plan for an AIoT future
There are two critical steps that businesses need to take to take advantage of the AIoT opportunity.
First, they must integrate their AI and IoT teams. AI developers need to work in lockstep with IoT teams to ensure that the software maximizes the potential of the hardware on which it operates. Likewise, the hardware team needs to ensure they’re building an infrastructure adequate for the existing and future needs of AI tools.
Second, businesses must move the technological focus from monitoring to predictions. Today’s IoT technology is being used mostly to track equipment performance and conditions. But to take advantage of AIoT, that focus must shift to forecasting changes in equipment behavior and predicting maintenance needs. Doing so will allow businesses to use AIoT to stay ahead of business needs, driving greater efficiency and, ultimately, business value.
AIoT puts two powerful technologies together, so one can not only seize and act on business opportunity, but anticipate – even create – business opportunities that don’t exist today.