According to Statista, there were 30 billion IoT devices in 2020 and 75 billion in 2025. All of these devices generate huge amounts of data. However, due to stringent processes, outdated data processing tools and faulty analysis methods, around 73 per cent of the data remains unused.
To get the most out of IoT systems, companies need AI. Only then can specialists interpret this data and derive insights and forecasts. The solution that tech experts are currently seeing is Artificial Intelligence of Things (AI + IoT = AIoT ). IoT is still hype. This means that the potential of AIoT cannot yet be properly classified. In the short term, the possibilities of AIoT tend to be overestimated – but underestimated in the long term. Here you can find out what you should bet on if you are considering using AIoT.
AI + IoT: The Perfect Symbiosis
IoT solutions are multi-level sensor devices that collect large amounts of data and send them to the cloud via wireless protocols. Artificial intelligence is the ability of a machine to interpret data and make intelligent predictions. IoT devices use AI to analyze and react to the collected sensor data. AI and IoT go together perfectly. Neither AI without IoT or IoT without AI make sense: IoT data alone doesn’t say anything, and AI always needs data for food.
Gain Additional Insights
If companies equip their IoT systems with AI functions, they can gain additional insights into IoT data that would otherwise be lost. In this way, they increase the benefits of existing IoT solutions. IDC compared two groups of companies – the first used the AI + IoT combination, the second only IoT. The companies were asked about these six goals: Acceleration of internal processes, improved employee productivity, quick reaction to risks and failures, rationalization of processes, new digital services and innovations, and cost reduction.
The result is not surprising: AIoT companies appear to be more competitive than IoT companies because they are more likely to achieve each goal, with differences in the double-digit percentage range.
AIoT: Status And Risks
The AI focus of European countries on IIoT is partly because the AI area is currently overregulated. While US and Chinese companies are working on sensitive applications such as automated facial recognition, European companies face data protection issues. On the other hand, there are open legal questions about liability and intellectual property. This unsettles companies because they do not know who data belongs to and what they can do with it.
One danger of this over-regulation is that foreign companies, which have more options, develop AI solutions in all value chain stages (from marketing to production to service). At the same time, German providers can only offer AI services for IIoT to a very limited extent.
Marco Junk, Managing Director of the Bundesverband Digitale Wirtschaft, describes it as follows: “As a country of mechanical engineering, we have to realize that in the future, added value will no longer lie in the machines alone, but in the AI-based services on and with our machines.” Now I will decide “whether in future we will only be suppliers of our machines for the providers of AI services or whether we will integrate these services ourselves”.
Slowly, however, the ball is rolling: the European Commission has taken the first steps. In the White Paper on Artificial Intelligence, she presented proposals for designing a European legal framework for AI applications and political options for action to promote AI because AI is a technology that, in practice, often cannot yet do as much as one thinks. Regulations and uncertainty, at least in Europe, meaning that it is unclear where and how AI can be used. Therefore, AI is currently overrated in some places. However, especially at AIoT, it is the perfect technology to generate added value. Here are a few examples of where AIoT works and why you shouldn’t underestimate it.
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How IoT Is Changing The World
Thanks to AI-based systems for predictive maintenance, companies can obtain usable insights from machines to predict device failures. According to Deloitte, such solutions lower per cent and increase equipment availability by 10-20 per cent. Many companies have been using this approach for years, including the German compressor manufacturer BOGE. Its products are used in areas where downtime can have fatal consequences, such as in the pharmaceutical and food industries or semiconductor production.
The company used software for predictive maintenance to minimize the risk of failure. The software can provide specific information on how many hours or even minutes it will still take before a technical problem arises on the machine, which makes maintenance work easier to plan.
Remote Monitoring Of Patients By AIoT
During the COVID-19 pandemic, more and more healthcare providers are turning to technology. They rely on remote monitoring systems to treat COVID-19 patients while reducing the risk of infection for medical staff. An example of such a system is an AI-based solution from Tyto Care. It diagnoses patients using data recorded by the Tyto Care-AIt and the mobile phone. The special AI algorithms detect problems such as swollen tonsils, sore throats and lung diseases. This allows doctors to make a diagnosis without touching the patient.
At the moment, only “highly automated driving” (or “piloted driving”, level 3) is permitted in Germany. In other words, the car can almost completely take over the journey, but responsibility remains with the driver. AI is currently used in autonomous driver assistance systems (ADAS) and fulfils several tasks: reducing the fisheye effect in videos from onboard cameras to monitoring the situation on the road. Technologically, on the other hand, the automotive industry has already arrived at autonomous driving – autonomous cars are being tested worldwide.
AIoT For Improved Business Models
Rolls Royce is best known as a car and turbine manufacturer. The company used to manufacture engines and then sell servicing services for those engines. If the engine failed, it had to be serviced for Rolls Royce, which was an additional business. Today they have completely changed their business model: they sell an hourly rate for the operating time of the engines. Based on the IoT sensor data of the motors, they optimize the performance to have as little maintenance time as possible and to intervene early so that the motors remain in operation for as long as possible. For the customer, this now means that he pays for exactly what he wants to pay for, namely the operating performance of an engine.
What do we learn from it? IoT is the future of the Internet of Things – if it is implemented and used correctly. A full-stack provider such as Softeq has the right expertise to turn data-poor devices into data-rich machines and transform data into insights.
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