They have been on everyone’s lips for years, but the interaction of the two companies offers a wide range of advantages: IoT and AI. To get the most out of IoT systems and to be able to interpret data, symbiosis with AI is a must. Many companies now rely on the IoT – lower operating costs, shorter response times through automated processes and helpful insights for business development are just a few of the notable advantages of the Internet of Things.
AI, i.e. artificial intelligence, also offers a variety of business benefits: it reduces errors, automates tasks and supports relevant business decisions. Machine learning as a sub-area of AI also ensures that models – such as neural networks – are adapted to data. Based on the models, predictions and decisions can be made. For example, if sensors deliver new data, they can be integrated into the existing modules. Two questions arise here: why not combine the two? And: Isn’t the symbiosis of the two inevitable?
IoT And AI: Still A Lot Of Room For Improvement
The Statista research institute assumes that there will be 75 billion networked devices by 2025. These provide information that should, of course, also be used. This is precisely where AI comes into play, which generates predictions based on the sensor values received. However, many companies still need help to properly assess the potential of connecting IoT and AI, or AIoT for short. In addition, they often need to be more skeptical about outsourcing their data – especially regarding security and communication.
Because the increased number of networked devices, which requires the connection of IoT and AI, increases the security requirements for infrastructure and communication structure enormously. It is not surprising that companies are unsettled: Industrial infrastructures have grown historically due to constantly rising conditions and present companies with entirely new challenges, which manifest themselves, for example, in an increasing number of networked devices. With the combination of IoT and AI, many companies are venturing into relatively new territory.
Recognize And Exploit Potential
But companies can no longer deny the advantages of AIoT because this technical combination makes networked devices and objects even more helpful. Based on the insights generated by the models, those responsible can make decisions more efficiently and reliably predict future events. In this way, a continuous cycle of data collection and analysis develops.
With predictive maintenance, for example, production companies can forecast device failures and thus prevent them. The combination of the two technologies also makes sense from the safety point of view: continuous monitoring and pattern recognition help to identify failure probabilities and possible malfunctions at an early stage – potential gateways can thus be better placed and closed in good time.
The result: companies optimize their processes, avoid costly machine failures, and at the same time reduce maintenance costs and thus increase their operational efficiency. In this way, IoT and AI represent a profitable fusion: While AI increases the benefit of existing IoT solutions, AI needs IoT data to draw any conclusions. Worth knowing: When setting up an AIoT solution, there are no restrictions as far as the carrier medium is concerned – it can be obtained via cable or a private 5G solution.
Industrial Manufacturing Was Made Easy
The following use case shows how the combination of IoT and AI can improve business processes: A medium-sized automotive supplier, which among other things, produces equipment for production, was struggling with production fluctuations. The aim was, therefore, to make monitoring more active. The company was able to digitize production using IoT gateways and sensors in conjunction with an AI model based on a neural network.
It put itself in a position to be able to identify and predict failures and problems earlier. Since then, this automation of industrial production has made it possible to intervene proactively in production and to keep the production level constant over the long term. The next step is to work with the customer to develop an agile service model individually tailored to their requirements to extract additional added value from the data – the open approach thus enables it to be adapted to customers with different needs.
In this case, the complete IoT design and also the infrastructure of the neural networks are based on Cisco technology and on Cisco UCS servers, which were designed for particular high-performance calculations. This made it possible to calculate the models quickly and establish the ready-to-use solution according to the plug-and-play principle. The service is built in the cloud to make it easier and faster to scale – the on-prem infrastructure has been containerized for this purpose. This makes migration to the hyperscale very easy. The customer decides where his data is located and does not have to store critical data with third parties.
Connected Becomes Smart
With the combination of IoT and AI, those responsible can quickly make targeted and informed decisions. The devices become part of an intelligent communication system that can partially react independently based on data obtained and learned behavior. Modern technologies also increase the security of systems and data. AIoT is, therefore, a real gain for companies of all sizes. They thus optimize processes, are less prone to errors, improve their products and thus ensure their competitiveness in the long term.