Artificial Intelligence (AI) Appreciation Day takes place every year on the 16th July, a day to celebrate the achievements and positive impacts of the integration of AI across different industries; be this healthcare or manufacturing, or anything else you can think of.
In this spirit, IoT Insider explores how the intersection of AI and IoT is not only creating a whole host of opportunities for industries, but is becoming a necessary convergence for enterprises.
On IoT Insider, we’ve written a fair bit about what AIoT (Artificial Intelligence IoT) looks like, so if you’re looking for a more general overview of AIoT, you can find a definition of AIoT over here, and an outline of benefits and use cases, here.
Dealing with a deluge of data
A 2024 Telenor IoT report looked at the integration of AI and IoT, in particular how it is being used to reshape industries and how it will become a necessary tool for companies to remain competitive in an increasingly digitised world.
The report explained that the sheer amount of data generated by IoT devices – including ‘smart’ devices with built-in sensors, software and communication hardware from healthcare wearables to street lighting – enterprises struggle to extract meaningful insights from the data; calling for the application of AI to sort through this data and properly analyse it.
Mats Lundquist, CEO at Telenor Connexion and Head of Telenor IoT said following the publication of the report that “the convergence of AI and IoT isn’t just an evolution; it’s a revolution in how businesses operate and compete globally. This report emphasises the urgency to act now.”
Without properly processing and analysing the data, the purpose of collecting it becomes meaningless. AI technologies like machine learning algorithms prove their value in filtering, analysing, and extracting important information from this data deluge.
The synergy between AI and IoT emphasises to enterprises how they can apply these tools in a way that will support how they operate, provide deeper insights and improve their decision-making processes by putting data at the forefront. Machine learning algorithms deployed by AI means they can analyse the large amounts of data generated by IoT devices.
Real-world scenarios
As an example of what this might look like in a real-world scenario, for smart grid management, IoT devices are used to monitor energy consumption across a grid, collecting from sources such as smart meters and weather stations. AI algorithms are then deployed to analyse the data to predict, for instance, energy demand, optimise distribution and manage storage. Energy providers are then given a better idea of peak usage hours and can subsequently balance their supply and demand; they may decide to apply it to pricing models to adjust pricing depending on real-time demands.
This is just one scenario. AIoT is also facilitating predictive health monitoring, whereby the IoT device – a smart wearable – continuously collects patient data, analyses it in real time and uses it to predict potential health issues. As a result, both patients and doctors receive early warnings and can intervene in a preventative health approach. Or, retail inventory management; autonomous vehicles; traffic management; you get the picture.
In an announcement in April of this year, FLIR demonstrated the value of collecting large datasets and training AI models on them, as it introduced its camera system for traffic intelligence, monitoring vehicle speed and trajectory, lane changes, tailgating or drivers going the wrong way. Having trained AI models on its image database of millions of images collected over the last 30 years, the resulting camera system is not only well suited for understanding and identifying traffic issues, it gives FLIR the edge in using its own intelligence rather than going to another company.
Conclusion
Without a way of sorting through large amounts of collected data, arguably, enterprises aren’t getting maximum value out of it. The (unofficial) nature of IoT is to embrace smarter technologies and the positive impacts they bring; in doing so, the combination of AI and IoT can support sorting through large datasets.
The forecasted continual growth of IoT devices not only presents challenges in the way of connectivity and adding strain on existing networks, it also means more and more data will be created, and companies need to be able to harness it appropriately.
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