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There will be more than 64 billion connected devices by 2025, according to IDC. On top of that, Cisco reports five quintillion bytes of data currently get generated daily. Managing ways to gain accurate insights at speed from billions of such devices can be baffling for businesses. However, the technology landscape has gone through many changes to resolve this issue. At the forefront of driving this change is the intelligence of things (AIoT).
AIoT is a revolution that has converged the internet of things (IoT) and artificial intelligence (AI). Sharing an interface, IoT helps connect AI-enabled devices to a network to collect data while drawing insights and analytics from that data. Simply put, AIoT extends capabilities to the edge to make these otherwise data-generating devices more autonomous, more intelligent. It also empowers us to push more and more intelligence capabilities but having to churn lots of data at the source to generate higher performance and decision-making
With this surge of data generated by connected devices, Gartner has predicted that 80% of all corporation IoT tasks will incorporate AI as a significant segment by 2022. But, managing vast amounts of data generated by IoT devices and sensors is difficult with the existing, not-so-smart analytics tools. One can say AIoT won’t provide relevant insights with heaps of data still stacked in legacy systems.
Companies in various industries with developed AIoT capabilities report more robust results across critical organizational goals, including increased employee and operational productivity, easier implementation of new digital services, and decreased overall costs. The interdependence between IoT and AI manifests itself in many successful integrations in both the B2B and B2B2C space, allowing organizations to reach their customers faster with the ability to scale. According to Sas, companies using IoT data to speed up processes without AI saw a 32% increase, while those adding AI to the mix saw speeds improve by 53%. Two industries that can experience significant improvement using AIoT are pharma manufacturing and healthcare.
AIoT makes it possible to record yields by using settings at different production steps to help keep up with the incoming data, lower the reaction-time between detecting an actionable event, and adjusting the manufacturing or test process. Moreover, it can accelerate drug development processes to create a pipeline of ground-breaking treatments for acute diseases. It can also improve the overall equipment effectiveness (OEE), monitor the environment of the production facilities, and reduce measurement cycles while maintaining accuracy at optimum levels.
AIoT can help in predicting and creating early warning systems for disease control. By leveraging patient data from wearables, doctors can monitor patient vitals and configure potential illness alerts in real-time. In hospitals, AIoT helps keep track of the status of the sterilization and hygiene, occupancy optimization of the resources, and proper utilization details of equipment like nebulizers and oxygen pumps, etc. The wearable data and video data installed inside elder-aid facilities can also deliver faster and smarter analyses, which will help alert the system and save lives.
While AIoT can positively impact businesses by unraveling insights from myriads of data, one cannot remain oblivious to the potential security concerns. With the growing number of cyber risks and threats, each node in the network is at stake. And, while learning from previous data is critical for enterprises to exploit the benefits of AIoT, safeguarding that data is equally pivotal.
That’s why the role of cybersecurity in AIoT’s landscape becomes more critical. IoT devices carry both personal and professional information. Thus, the growing use of AI in such scenarios has become a necessity. AI systems can predict an attack based on historical data. AIoT will recommend the best-fit security policy and create an early warning system for the user. Together, AI and IoT can automatically detect anomalies in the data and notify the user with alerts.
To further leverage the benefits of AIoT, one cannot overlook edge computing in the landscape of AI and IoT. With numerous devices connected and vast amounts of data generated at the edge, data latency and bandwidth restrictions cause bottlenecks for IoT. Nodes are becoming energy efficient with more significant computing potential. Thus, AIoT prioritizes the offloading of some tasks to the edge to drive faster response time in critical applications. Lesser communication traffic helps maintain the network security easier. Edge AI can even help improve device security by examining incoming traffic for signs of tampering.
It’s time to become agile to revolutionize your digital transformation with AIoT. Advancements in AIoT now enhance organizations’ risk management by reducing accidents and improving safety in the work environment.
AI-enabled IoT devices are very highly responsive and closer to the user, providing real-time insights, ensuring greater security, processing, and reducing the risk of data tampering during IO operations.
With several tech capabilities coming to the forefront now, such as 5G, Digital Twin, AR/VR, it is expected to unleash a plethora of possibilities delivering ambient experiences and driving towards a path the lines between digital and physical worlds seem blurred. While enterprises are bound to get benefitted from productivity, efficiencies, etc., it will be a question of how such capabilities come together to deliver that right experience for the regular consumer and make a lasting impact.
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