What is AIoT, or the Artificial Intelligence of Things? Right at the intersection of the Internet of Things (IoT) and Artificial Intelligence (AI), AIoT is a term that has cropped up only recently. It describes the convergence of IoT and AI systems with the common goal of generating useful IoT data and building on the insights from that data. With IoT and AI together on one development platform, the possibilities are endless.
The facts beyond the buzzword
The impact of combining IoT and AI is already beginning to show. A recent global study has uncovered that the majority of leaders believe that AIoT will help them become more competitive. The respondents in the study not only agree that AIoT is generating results but also say that they will not be able to compete effectively without AI. This study found that 92% of AIoT adopters say that combining IoT and AI had exceeded their expectations. The takeaway?
Companies that combine AI and IoT are more competitive than those using only IoT.
Why makes AIoT so beneficial?
- Edge-to-cloud-continuity. An AIoT approach gives you a seamless IoT process and transparency from the edge to the cloud. You start with connected IoT devices and IoT data collection. Then you proceed all the way up to advanced analytics in the cloud.
- A best-of-both-worlds approach. With AIoT, you are able to move computing closer to your data source. On the one hand, you have AI right at the IoT edge. On the other hand, you benefit from the cloud’s more extensive analytical capabilities.
- Taking ownership of your data. The AIoT approach helps you to fully control the process of making sense of your data at every stage.
As seen in the graphic below, AIoT allows you to cover your entire data journey, ensuring continuity and visibility:
The building blocks of this data journey can be described in their relation to the four main layers in IoT architectures.
- Layer 1. Devices. This is where you start with IoT data collection from edge devices, sensors, machines, and APIs.
- Layer 2. Gateway. This is where you perform initial aggregation tasks, aggregate the data, and can even do some basic anomaly detection. At the move-and-store stage, you reduce the massive data flows and move the data further up the value chain where it can be stored for long-term use.
- Layer 3. Data management. This is where you cleanse, transform, and harmonize the data.
- Layer 4. Cloud/data center. Whereas you can build some simple ML models and custom algorithms at the edge, here you can combine the data with data from other sources, do advanced analytics, AI, and deep learning.
I discuss the IoT architecture layers in more detail in the article “The IIoT Architecture: How to Tap into Its Full Potential?”:
Why is AI so essential to the success of IoT initiatives?
Classic methods can no longer apply in IoT deployments where you have massive volumes of IoT data generated at an unprecedented pace. Having AI capabilities means being able to fully utilize that data by learning from it and automating as much as possible along the way. The more sophisticated the IoT system, the stronger AI capabilities it requires. And the true value of the collected IoT data only becomes manifest when it is combined with powerful AI.
As the graphic above shows, AI is found in two locations within the IoT system: the center and the edge. AI deployments at the center, traditionally, generate predictive analytics or anomaly detection. Up until now, AI deployments have mostly served the auxiliary function of reducing the data volumes coming towards the cloud. AI close to the IoT device nodes can enhance security and help reduce latency and bandwidth. I describe this in the article “The Future is Decentralized: IoT Edge Computing is Key”. But today, you can additionally perform analytics at the edge and even have simple AI models.
Having AI capabilities means being able to fully utilize that data by learning from it and automating as much as possible along the way.
Utilizing AIoT is key to driving long-term value in companies. It helps them go beyond isolated implementations and PoCs to consistently increase the adoption of automated processes. The democratization of the insights gained with the help of AI also plays a role as these results will have to be made consumable by business analysts, decision-makers, and other non-experts.
According to the study mentioned above, companies that have developed AIoT capacities show stronger results across various critical organizational goals. Change is seen in their ability to speed up operations and introduce new digital services. You also see benefits in areas such as employee productivity and cost reduction. The study speaks of double-digit percentage differences between those combining IoT and AI and those who only use IoT.
Bringing AIoT to a platform
What happens, however, when you go one step further and take AIoT, the Artificial Intelligence of Things, to a platform? One such gesture uncovers tremendous potential as it radically simplifies the access to an AIoT-enabling infrastructure and makes it possible for companies of all sizes, regardless of their resources, to develop their own AIoT solutions.
Bringing your IoT initiative to an AIoT platform allows you to close the gap between IoT edge devices and business applications. IoT platforms do not simply serve as the middleware that connects these two domains but also add functionality to the hardware and application layer. And once you enhance IoT platforms with capabilities for data processing at the edge and advanced analytics, you make them AIoT platforms.
So what do you gain out of this?
Operationalization at scale
Bringing the combined capabilities of IoT and AI to a platform allows you to start with your IoT initiative right away, making full use of your operational infrastructure and benefiting from the platform’s built-in security. Ideally, your infrastructure consists of a public or a virtual private cloud that extends towards an on-site IoT edge device.
Quicker adoption times
Developing on a platform that combines both IoT and AI capabilities in one AIoT platform allows you to make rapid advances with your IoT initiatives. When all the building blocks are available within one product and the heavy technical challenges are removed from the outset, you can truly focus on what matters most — getting value out of your IoT data. IoT platforms with machine learning capabilities and AI algorithms already address some of the more common challenges to IoT adoption and have tackled some well-known hurdles in advance.
Adoption across multiple industries
Cross-industry adoption is another advantage of the AIoT platform model. It is often the case that you can transfer approaches and tools across use cases and adapt solutions applicable to one industry to another. This is IoT collaboration written large. Companies and even entire industries will be now encouraged to exchange ideas and approaches. Ultimately, this will lead to establishing cross-industry best practices and advancing innovation.
At Record Evolution, we are well aware of the challenges posed to complex IoT implementations. To address these challenges, we have built the Record Evolution Platform for custom IoT applications development and advanced analytics. Get in touch to talk to an expert or book a demo.
About Record Evolution
We are a data science and IoT team based in Frankfurt, Germany, that helps companies of all sizes innovate at scale. That’s why we’ve developed an easy-to-use industrial IoT platform that enables fast development cycles and allows everyone to benefit from the possibilities of IoT and AI.