Artificial intelligence (AI) and the Internet of Things (IoT) are hot topics. At the intersection of these two technologies is AIoT, emerging as a critical technology of interest for organizations operating within the public and private sectors. Yet, there are still several misconceptions about the best way to migrate from IoT to AIoT. Bob Banerjee, VP of Products, EPIC iO, shares key pointers to keep in mind before you start your AIoT program.
While it is not as simple as adding artificial intelligence to existing IoT devices, and it may or may not make sense to add AI for every device or application, if deployed with the right strategy from the beginning and if used correctly. For the right reasons, AIoT can deliver high-value business outcomes and the ability to scale across a variety of use cases in both the public and private sectors. These can range from monitoring critical infrastructures such as dams and water supply to ensuring the health and safety of building occupants to even helping enterprises or public agencies achieve improved outcomes in their day-to-day operations.Â
For best results, there are a few key factors that must be considered when embarking on an AIoT program:
Choose Integration and Remove Silos
IoT was designed to sense what is happening in a physical space, for example, a room’s temperature rising or dropping too quickly, a suspicious smell that may be harmful to occupants, or an unwelcome guest entering a building. The challenge today is that in many cases, these IoT sensors are siloed within facilities, monitoring temperature and air quality, for example, while security teams monitor the physical security inside and around the perimeter of the building. Therefore, no one person or group of people has visibility into all of the systems at one time, making it very easy to miss critical alerts and challenging to share data quickly.
The first step in adding AI to IoT is for enterprises and public agencies to stop deploying IoT sensors as siloed solutions. Instead, a strategy for integrating the entire fleet of IoT sensors under a single AIoT platform must be implemented.Â
The organization can benefit from simplified and streamlined management of IoT devices that eliminates duplicated efforts across facilities, IT and security teams. Further, these same organizations can experience the full power of AI in correlating all of the IoT data collected from the once disparate systems into intelligence they can use. Â
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Use AI and IoT to Create Better Outcomes
One of the most essential benefits of AIoT is driving better business outcomes. Enterprises and public agencies can spot trends and resolve problems more quickly when they use AI in combination with IoT to gather intelligence from the sizable data lakes that IoT sensors have created.Â
Yet, it is essential to remember that using AI and IoT takes you far beyond the if-this-then-that rule. Some examples include:
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- If an IoT temperature sensor placed inside a room detects that it is too hot or cold, you don’t need AI to tweak the heating and cooling to bring it back within bounds. That’s black-and-white IoT with simple rules.
- You can use IoT sensors inside turnstiles to count the number of passengers coming through a train station or the number of visitors at a sporting venue or amusement park. But that doesn’t work for ungated areas or for estimating the level of congestion on a platform, for that, AI has to work with IoT.
- Â If a car stalls in the middle of an intersection, you need AI and video cameras to take action if the vehicle has been there too long.
- Â IoT sensors might be dispersed throughout a park, monitoring temperature, humidity, air quality, and if someone opened a gate to a protected area. But you need AI to determine how many residents are using the facilities, whether it be a playground, tennis courts or trails, at any given point in time, so that city and park planning departments can more closely meet the needs of residents.
See More: AI Adoption Will Grow Ten-fold by 2030, Dominated by Use in IoT
Future-Proof Troubleshooting with an Open AIoT Platform
The vast majority of organizations take their first step into the world of AIoT to solve a problem they are experiencing today. They don’t realize that down the road, whether it is next year or five years from now, they can scale the AIoT platform they are deploying today to solve future problems, and this is where the value of having open-source AIoT comes into play.
One example of this is when a city was operating a sewage plant. During severe storms, some of the sewage pumps would stop working. The only way to find out which ones were not working was to walk a half-mile during the storm to visually inspect a single red-light bulb alerting which pump(s) had failed. The city switched to an open AIoT platform and immediately experienced ROI in the time and resources it was saving as the IoT sensors identified where the problem was occurring, and then AI sent resources out to solve it.Â
New use cases began to emerge once the city had proven that it could solve one problem with AIoT, and new use cases began to emerge. Having an open AIoT platform gave the city the flexibility to re-use their initial investment to create an IoT alert system for road flooding. The ability to scale the solution in this way would not have been possible with a proprietary system. Instead, the city would have been dealing with vendor lock-in, which would have stifled innovation and may have even prevented them from being able to expand the use cases for AIoT within their municipality.
For organizations across the public and private sectors, the journey to AIoT success begins with fully understanding the benefits derived from the combination of AI and IoT. Ultimately, success is measured by fully integrating the solutions and the ability to scale the AIoT solution to solve future business challenges.
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